Three times Value Analysis [601125]
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Three times Value Analysis
1 – Value Analysis in modular design product
2 – Value Analysis in the design of industrial
machining processes
3 – Value Analysis in the choice of material for
industrial parts
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1 – Value Analysis in modular design product
1.1 – The modular design of industrial machinery an d equipment
Modular design of industrial machinery and equipmen t is a technique
renowned for organizing and simplifying complex sys tems. From the simplest
components to the complex ones, the modular design of industrial machinery
and equipment proved to be efficient beyond any dou bt. Value Analysis
contributes substantially to the field of modular d esign through its creative
approach, interdisciplinary work, innovation techni ques, finding new solutions
and, not least, through its economic side.
Value Analysis is defined as a competitive, organiz ed and creative method,
aiming to meet the user's need via a specific desig n approach, from the
functional, economic and multidisciplinary points o f view. This paper presents
a concrete example of modular design, using the Val ue Analysis approach in
a concrete case related to the machinery for crushi ng materials (jaw crushers,
cylinder crushers, hybrid crushers, …).
1.2 – Introduction
Modular design is a technique renowned for organizi ng and simplifying
complex systems. From the simplest components to th e complex ones, the
modular design proved to be efficient beyond any do ubt /1/.
The most recent technological evolutions translate into highly complex
products and functions integrating the machinery, e quipment or product.
In the last ten years, numerous researches and stud ies have highlighted the
emergence of the modular design approach in definin g a complex product
and in organizing the production.
The modular design is a generic concept and it can be applied to a wide
variety of situations.
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At the same time, in all its diversity, this notion rests on the idea of simplifying
a reality through a clear breakdown of the consider ed systems.
Despite its recent revival, the notion of modular d esign is a relatively new
one.
At origin, the notion of modular design referred to a renewing method in the
design of complex products. These ideas are theoret ically founded by works
published by authors such as Simon and Alexander ar ound 1960 /2-4/.
This method consists of decomposing the end product / equipment /
machinery in several sub – systems that can be desi gned and manufactured
separately / independently.
The objectives of the paper are:
1 – the manner in which the choice of an architectu re for a product /
equipment / machinery affects the characteristics o f the machinery and the
costs, related to design and manufacturing, using t he Value Analysis
methodology,
2 – the range of machinery that can be obtained by using the modular design,
3 – defining and describing the architecture of equ ipment by presenting:
– the allocation of functions / components, the all ocation of components
according to functions is deemed modular when to a function does not
correspond to a single component,
– the characteristics of assembly interfaces. Inter faces define relational
characteristics between components and embody rules specifying the
manner in which components integrate.
Interfaces are defined, on one hand, by the degrees of interdependence they
generate between different interconnecting modules and, on the other hand,
by the level of standardization.
Modules interfaces have three features:
1 – role in the operation of the product,
2 – ease of assembly / disassembly,
3 – the level of standardization /5/.
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1.3 – Value Analysis Method
Value Analysis is characterized by:
– the functional approach which requires formulatin g the problem in terms of
purpose and not in terms of technical solutions for finding the essential and
what is relevant, thus avoiding the tendency to lim it to existing solutions and
to unwittingly prohibit numerous possibilities,
– the economic approach by systematic reference to costs, both related to
earlier products of the same group and to their fun ctions, and to what can be
expected for each function or for each new solution , multidisciplinary
approach through group work, calling for an animato r and a Decision Maker.
Group work brings together all the required compete nces and people with
different training and responsibilities, allows fin ding a consensus on the
functions, performances, principles, solutions and costs, all of these favouring
the exercise of creativity and the enrichment of th e available information.
Group work allows concomitant adjustment of the pro blems that otherwise
would only be approached sequentially and in isolat ion by various participants
to the design and manufacture of the product. The g roup should mostly be
able to have within it the skills needed to estimat e costs based on the data
available at this stage. The group can issue a prop osal, but the decision rests
with the Decision Maker. This creative approach aim s to achieve a wide
range of possibilities, allows taking into account the market developments,
the environment and the technical solutions and is particularized through:
– critical analysis of the data, of the information , of the solutions,
– iterative nature,
– specific use of certain means and methods,
– systematic, organized and participative approach, using a standard work
plan, as developed below,
– user motivation.
This section describes an outline in seven steps, a synthesis of the
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approaches that have been shown to be effective in practice. Such an outline
allows, among others, a better understanding betwee n partners, a better
tracking of the analysis taken into account.
The practical application must take into account th e plurality of the situations.
For clarity of exposition, the process is described in what follows in a
sequential series. In fact, its actual application is iterative: each step can be
resumed with new additions to the previous steps.
Value Analysis is a method that provides an operati ng technique using a
creative and organized approach.
It is managed by a group, each of them selected by their expertise in specific
subjects and coordinated by a Value Analysis expert .
The Value Analysis group activity is managed in sev en stages:
1 – information and functional analysis,
2 – creativeness,
3 – evaluation and selection of the proposals,
4 – the creative phase,
5 – development of the selected proposals,
6 – presentation of the selected proposals, set in order by priority,
7 – implementation phase.
For each of the seven phases of Value Analysis meth odological approaches
was developed simple and general rules.
Value Analysis approach consists of three successiv e tests, who transforming
all the needs expressed by users or not, in optimal solution of a technical –
economic study:
1 – the first key point of the approach, the Functi onal Analysis of Need is to
identify and formalize needs that product, service, etc., must satisfy,
2 – then, Functional Analysis of Product will trans late those needs into
technical solutions,
3 – lastly, Value Analysis is an optimization of t hese solutions to obtain
acceptable compromise between the level of satisfac tion of needs and costs.
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These three tests are conducted sequentially and it eratively.
1.4 – Selecting ideas and technical solutions.
Design and manufacture the crushing equipment
In Value Analysis ideas are very often searched usi ng the Morphological
Analysis.
The Morphological Analysis was done in two stages:
1 – modulated design / manufacture of crushing mach ines,
2 – design of cylinder variants for the cylinder cr usher.
The search for technical solution was made in line with the functions of the
machinery for crushing materials, presented below ( table 1).
Global function of the product is the following: en sures crushing, shredding of
several materials,
– Service Function (usage, main):
F 1: ensures crushing, shredding of several materia ls,
*F 2: ensures supply of material,
F 3: ensures evacuation of the materials,
*F 9: ensures the reception, transmission, distribu tion of material from other
equipment.
* These functions are outside the study.
– Constraint Function:
F 10: provides a working capacity,
F 10-1: contains components,
F 10-2: provides functional movements,
F 10-2-1: provides work energy,
F 10-2-2: converts the energy into mechanical work,
F 10-2-3: provides work energy transmission,
F 10-2-4: allows stop / start,
F10-3: ensures period of grinding,
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F 11: maintains and protects the assembly,
F 11-1: ensures easy maintenance,
F 11-1-1: ensures adjustment,
F 11-2-2: defective components can be replaced rapi dly,
F 11-2: provides support to the assembly,
F 11-3: provides fixing on the ground,
F11-3-1: easy to assemble / disassemble,
F11-3-2: easy to carry and handle,
F 12: protects the cinematic chain,
F12-1: restricts certain parameters,
F 13: shall be adapted to the constructive, energet ic and informational flows,
F13-1: manages the functioning,
F 13-1-1: provides the connection to their energy sources,
F 13-1-2: provides the connection to informatics so urces,
F 13-1-3: provides control of working parameters,
F 14: ensures operation in noxious environments,
F14-1: corrosion resistance,
F14-2: resistance to wear,
F 15: ensures user security,
F15-1: respects the rules,
F15-1-1: security,
F 15-1-2: ensures low noise,
F 15-1-3: ensures reduced vibration, noise, the hea t emissions,
F 15-1-4: ensures capturing pollutants,
F16: ensures uniformity of the movement,
– Estimation Function:
F 31: provides user interface,
F 31-1: aesthetics,
F 31-2: optimal cost,
F 31-3: ergonomics,
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F 31-4: reliability,
F 31-5: durability,
F 31-6: provides information.
Table 1 – List of functions
Symbol
and
Functions *Type of
functions Technical dimension of functions
Name Measurement
Units Value
F1 – Ensures
crushing, shredding
of several materials FS blast
degree,
force –
daN 3 – 12
1300
F2 – Protects the
assembly FC moment,
force daN*m
daN 200
100
F3 – Ensures easy
maintenance FC length mm 10 – 25
F4 – Maintains the
assembly FS weight daN 20000
F5 – Provides work
energy FS moment daN*m 100
F6 – Converts the
energy into
mechanical work FC revolution
pulsation rpm
rad/sec –
F7 – Ensures
uniformity of the
movement FC revolution
pulsation rpm
rad/sec –
F8 – Ensures a
working capacity FC productivity m3/h 6
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Table 1 (continuation) – List of functions
Symbol
and
Functions *Type of
functions Technical dimension of functions
Name Measurement
Units Value
F9 – Resistance to
wear FC eroded
material g/year
F10 – Ensures
evacuation FC flow m3/h 1 – 2
F11 – Maintenance FC – – –
F12 – Easy to
assemble /
disassemble FC – – –
F13 – Ensured the
achievement of the
semi-finished FC – – –
F4 – Protects the
cinematic chain FC moment
force daN*m
daN 200
100
F10 – Ensures supply
of material FC debit m3/h 1 – 2
F12 – Provides user
interface, (aesthetics) FE aspect,
form, number of
information 7
In this paper are studied and analysed four variant s of a crusher with a mobile
jaw and a bolted cylinder with frame:
– variant 1 (photo 1),
– variant 2 (photo 2),
– variant 3 (photo 3),
– casting (one variant).
Using a creative group proposed the following morph ology (table 2).
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Table 2 – Morphological box
Symbol and
Functions Equipment Constructive variants
F1 – Ensures
crushing,
shredding of
several materials Jaw crusher 1 – with fixed and mobile jaw
2 – with movable jaw and a cylinder
3 – with movable jaw and three
cylinders
4 – with two cylinders
5 – with four cylinders
F2 – Maintains
the assembly Frame 1 – frame variant 1
2 – frame variant 2
3 – frame variant 3
4 – frame variant 4 – cast frame
5 – welded frame
6 – combined frame
F5 – Ensures
adjustment
Adjusting
mechanisms 1 – without adjustment
2 – screw
3 – wedge assembly
4 – pneumatic / hydraulic cylinder
5 – levers
6 – spring
7 – cam
3D modeling of a crusher cylinder assembly is shown in figure 1, and in figure
2 show the exploded image of the rolls crusher.
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Figure 1 – Roller crusher assembly, 3D modeling
Figure 2 – Exploded assembly of roller crusher, 3D modeling
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Constructive variants shown in photo 1, …, 3 are at the stage of functional
models, design and manufacture respecting all the e ngineering rules.
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Photo 1 – Crusher with a movable jaw and a cylinder – variant 1
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Photo 2 – Crusher with a movable jaw and a cylinder – variant 2
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Photo 3 – Crusher with a movable jaw and a cylinder – variant 3
1.5 – The weight of functions in value and cost
The weight of functions in value is presented summa rized in table 3, and
figure 3 show the weight of functions in value.
All four variants have the same function, so the sa me weight of functions in
value.
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Figure 3 – The weight of functions in value, Y 1 (1)
Regression equation describing this distribution is Y1 = – 1.282 * X + 16.66
(1) with R – squared value on chart R 2 = 1.
The cost of functions (functional cost) for the fou r variants can be seen
summarized in tables 4, …, 8.
In figures 4, …, 7 can be seen the weight of func tions in cost for variants 1, …,
4.
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25
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27
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Figure 4 – The weight of functions in cost, variant 1, Y 2 (2)
Regression equation describing this distribution is Y2 = – 0.601 * X+ 12.24 (2)
with R – squared value on chart R 2 = 0.271.
Regression equation that best describe this distrib ution is Y 2-1 = – 0.018 * X 3
+ 0.483 * X 2 – 4.197 * X + 18.64 (2’), with R – squared value o n chart R 2 =
0.408.
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Figure 5 – The weight of functions in cost, variant 2, Y 3 (3)
Regression equation describing this distribution is Y3 = – 0.600 * X + 12.23
(3) with R – squared value on chart R 2 = 0.272.
Regression equation that best describe this distrib ution is Y 3-1 = – 0.015 * X 3
+ 0.438 * X 2 – 3.965 * X + 18.35 (3’), with R – squared value o n chart R 2 =
0.408.
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Figure 6 – The weight of functions in cost, variant 3, Y 4 (4)
Regression equation describing this distribution is Y4 = – 0.597 * X + 12.21
(4) with R – squared value on chart R 2 = 0.265.
Regression equation that best describe this distrib ution is Y 4-1 = – 0.014 * X 3
+ 0.425 * X 2 – 3.928 * X + 18.36 (4’), with R – squared value o n chart R 2 =
0.406.
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Figure 7 – The weight of functions in cost, variant 4, Y 5 (5)
Regression equation describing this distribution is Y5 = – 0.600 * X + 12.23
(5) with R – squared value on chart R 2 = 0.246.
Regression equation that best describe this distrib ution is Y 5-1 = – 0.025 * X 3
+ 0.635 * X 2 – 5.086 * X + 19.90 (5’), with R – squared value o n chart R 2 =
0.403.
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The coordinates X i and Y i are given in the tables 3, …, 8 and based on the
values for those coordinates the diagrams of figure s 5, …, 8 are plotted.
The diagram of figure 3 shows the ranking of the fu nctions by their value.
In figures 4, …, 7 shows the ranking of the functio ns by their functional cost,
for four variants of crusher.
The diagram allows significant comparisons of the f unctions total costs, and
within the total costs, of the work and material co sts:
– the very expensive functions with the highest wei ghting in the total cost of
the product,
– the secondary functions that are very expensive i n relation to the objective
functions, or even more expensive than these,
The diagram reveals a Pareto type distribution, mea ning that 20 – 30% of the
total number of functions, include 70 – 80% of the total costs of the functions.
These functions are F1, F2, F9 and F12.
In figures 5, …, 8 shows the percentage of the va lue and cost functions in the
four crusher variants.
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Figure 5 – The weight of functions in value and in cost, variant 1, Y 6 (6)
Regression equation describing this distribution is Y6 = 0.700 * X + 2.27 (6)
with R – squared value on chart R 2 = 0.368.
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Figure 6 – The weight of functions in value and in cost, variant 2, Y 7 (7)
Regression equation describing this distribution is Y7 = 0.582 * X + 3.215 (7)
with R – squared value on chart R 2 = 0.400.
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Figure 7 – The weight of functions in value and in cost, variant 3, Y 8 (8)
Regression equation describing this distribution is Y8 = 0.580 * X + 3.228 (8)
with R – squared value on chart R 2 = 0.392.
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Figure 8 – The weight of functions in value and in cost, variant 4, Y 9 (9)
Regression equation describing this distribution is Y9 = 0.582 * X + 3.212 (9)
with R – squared value on chart R 2 = 0.371.
In the case of such a distribution, the first funct ions in the order of costs,
representing 20 – 30% of the total number of functi ons (in the above example
functions F2, F7, F9 and F12, for variants 1, …, 4) are considered to be very
expensive functions.
The diagram in figure 5, …, 8 shows:
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– the equation line Y = X (12) (the first bisector) the line that averages the
weighting of functions in value and in cost, expres ses the ideal situation of the
disparity between the two weightings, the weighting of functions in value and
costs, and
– for variant 1, the regression line of equation Y 6 = 0.700 * X + 2.270 (6),
which approximates the arrangement of the points, e xpresses the real
situation of the disparity between the two weightin gs, the weighting of
functions in value and costs,
– for variant 2, the regression line of equation Y 7 = 0.582 * X + 3.215 (7),
which approximates the arrangement of the points, e xpresses the real
situation of the disparity between the two weightin gs, the weighting of
functions in value and costs,
– for variant 3, the regression line of equation Y 8 = 0.580 * X + 3.228 (8),
which approximates the arrangement of the points, e xpresses the real
situation of the disparity between the two weightin gs, the weighting of
functions in value and costs,
– for variant 4, the regression line of equation Y 9 = 0.582 * X + 3.212 (9),
which approximates the arrangement of the points, e xpresses the real
situation of the disparity between the two weightin gs, the weighting of
functions in value and in costs,
In figure 9 can be seen superimposed in the same di agram the weights of
functions in value and in cost for the four variant s.
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Figure 9 – The weight of functions in value and in cost, variants 1, 2, 3 and 4
An analysis of the diagram of figures 5, …, 9 show s that some functions are
located above the regression line, indicating high costs, not justifiable in
relation to the value.
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These aspects allow the assumption that these funct ions are deficient, hence
the solutions to be identified are to focus on thos e assemblies, parts,
materials and technological operations that contrib ute, within the general
structure of the product, to the achievement of the se functions.
In the first iteration of the Value Analysis study will provide the functional
model of this jaw crusher with a roll crusher, to s ee the deficient functions.
Deficient functions are:
– for the four variants:
– function F4 – Maintains the assembly,
– function F5 – Provides work energy,
– function F9 – Resistance to wear,
– function F12 – Provides user interface.
Comparing the four machines (jaw crusher) can obser ve the following:
– the least expensive crusher variant is number 4, with cast frame.
This optimal variant will be approached by taking t his result into account in
the following iteration.
Constructive solutions will be delimited by the val ue of the ratio:
Xi / Y i = C i (10)
where: X is the weight of functions in value (tabl e 3),
Y – the weight of functions in cost (tables 4, .. ., 8).
The table 9 shows these values.
Table 9
V 1 V 2 V 3 V 4
X 78 78 78 78
Y 3778 3910 4010 3510
Xi / Y i= C i 0.0207 0.0199 0.0195 0.0222
Ranking 2 3 4 1
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The ratio of product value and cost in two variants , being in the following
relationship:
X4 / Y 4 ˃ X 1 / Y 1 ˃ X 2 / Y 2 ˃ X 3 / Y 3 (11)
It follows that variant 4 is superior to other vari ants in terms of value, of
economic efficiency.
1.6 – Conclusions
In this iteration of the Value Analysis study:
– it was chosen the best equipment (from several va riants) from engineering
and economic point of view,
– were chosen several subassemblies of the machine,
– frame,
– a crushing system, and
– the adjustment system.
The equipment has been redesigned and optimized:
1 – from engineering viewpoint: from the initial va riants V 1, V 2, V 3 (photo 1, …,
3), consists many parts, one complicated part (many components,
mechanical machining, turning of metal parts compli cated, long and very
expensive, etc.) to the variant V 4 consists of less components (mechanical
machining, turning of metal parts simple, short and less expensive than
variants V 1, V 2, V 3 ), easy assembly / disassembly systems is very eas y,
adjustment is easy, etc. etc.
2 – from economic viewpoint:
– the cost of function F1 is 484.5 $ for variant V 1, 491.5 $ for variant V 2, 496.5
$ for variant V 3 and 471.5 $ for variant V 4,
– the cost of function F2 is 666.5 $ for variant V 1, 705.5 $ for variant V 2, 740.5
$ for variant V 3 and 615.5 $ for variant V 4,
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– the cost of function F3 is 263.5 $ for variant V 1, 270.5 $ for variant V 2, 275.5
$ for variant V 3 and 250.5 $ for variant V 4,
– the cost of function F4 is 289.5 $ for variant V 1, 302.5 $ for variant V 2, 307.5
$ for variant V 3 and 252.5 $ for variant V 4,
– the cost of function F5 is 191.5 $ for variant V 1, 204.5 $ for variant V 2, 204.5
$ for variant V 3 and 154.5 $ for variant V 4,
– the cost of function F6 is 282.5 $ for variant V 1, 295.5 $ for variant V 2, 300.5
$ for variant V 3 and 245.5 $ for variant V 4,
– the cost of function F7 is 331.5 $ for variant V 1, 331.5 $ for variant V 2, 331.5
$ for variant V 3 and 331.5 $ for variant V 4,
– the cost of function F8 is 216.5 $ for variant V 1, 221.5 $ for variant V 2, 226.5
$ for variant V 3 and 211.5 $ for variant V 4,
– the cost of function F9 is 452 $ for variant V 1, 459 $ for variant V 2, 464 $ for
variant V 3 and 439 $ for variant V 4,
– the cost of function F10 is 127.7 $ for variant V 1, 139.7 $ for variant V 2,
149.7 $ for variant V 3 and 109.7 $ for variant V 4,
– the cost of function F11 is 127.7 $ for variant V 1, 139.7 $ for variant V 2,
149.7 $ for variant V 3 and 109.7 $ for variant V 4,
– the cost of function F12 is 336.6 $ for variant V 1, 348.6 $ for variant V 2,
358.6 $ for variant V 3 and 318.6 $ for variant V 4.
– the cost of equipment decrease from 4010 $, for v ariant V 3 (figure 3) to
3500 $, for variant V 4 (table 9).
This reduction of the cost is upon the following ar ticles:
a – design,
b – type of material,
c – quantity of material from cylinder,
d – machining mode,
e – the assembly – disassembly mode,
f – adjusting mode,
g – how to repair after working.
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In the second iterations of Value Analysis study ar e analyzed for variant V4
the functions above the regression straight line, Y = X (12) (for example F2,
F7, F9 and F12) and their cost reduced, then the re gression lines are re-
plotted and the functions relocated above them are noted; these functions too
are analyzed in view of reducing their cost, follow ed by the re-plotting of the
regression lines, etc., etc., there will be analyse d the sub-assemblies and
parts participating in the implementation of these functions and there will be
proposed solutions to reduce costs (considering sec tions a, …, g) above.
In a third iterations of Value Analysis study are a nalyzed other functions
above the regression straight line, Y = X (12) and their cost reduced, then the
regression lines are re – plotted and the functions relocated above them are
noted; these functions too are analyzed in view of reducing their cost,
followed by the re – plotting of the regression lin es, in the third iteration, etc.,
etc.
At the end of the Value Analysis study the points a re aligned as perfectly as
possible along the straight line Y = a * X (12), wi th a tilt of 45 o, this is the
optimal situation, the values weighting of function s and the functions cost
weighting are equal.
1.7 – The weight of functions in value and cost
The check of this identity is performed using regre ssion analysis by
determining the linear function (the regression lin e) that represents the
average proportionality.
The regression line passes through the origin, as i t is considered that a
function with ”0” value costs ”0”. The line has the form given by equation (12).
In the case of perfect proportionality all points a re on the line (12).
In order to simplify, the calculation is tabulated.
The regression line passes through the origin, as i t is considered that a
function with ”0” value costs ”0”.
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The line has the shape:
Y = a * X ( 12 )
where: Y i – the weight of functions in cost,
X i – the weight of functions in value,
a – slope of a straight line.
The calculation of the "S" estimator is carried out using the least squares
method:
S = ∑ (Y i – a * X i ) 2 /barb2right minim ( 13 )
The calculation of the ”a” parameter is carried out using the expression:
a = ( ∑ X i * Y i ) / ∑ (X i)2 ( 14 )
The solution is considered as being acceptable for the variant in which the S'
dispersion tends to zero or, better said, the soone r S' tends to ”0” (zero), the
better is the researched solution:
S' = dS / da /barb2right0 ( 15 )
S' = ∑ ( 2 * a * (X i)2 – 2 * X i* Y i) /barb2right 0 ( 16 )
In the case of perfect proportionality (a = 1 and S ' = 0) all points are on the
line (12). The coordinates X i and Y i are given in table 17 and table 18 and
based on the data calculated in this table the diag rams from figures 18, 19
and 20 are drawn.
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1.8 – Optimizing the crusher construction.
Implementing of Value Analysis method in designing of a foundry
equipment
The construction of the frame, for variant V 4 of the crusher, is compared
below, as follows:
1 – the frame made of 4 lateral walls, of rolled bl anks welded and bolted is
analysed in iteration 1, and,
2 – the frame made of a cast blank is analysed in i teration 2.
Implementation of Value Analysis in designing of in dustrial equipment is a
demonstrated need for a long time in highly develop ed industries. Beneficial
results of applying this creative, functional and v ery practical multidisciplinary
approach can see at all the products that surround us in everyday life.
This chapter presents a complete study of Value Ana lysis applied concretely
to a selected part of equipment. The phases and ite rative operation of the
Value Analysis method are presented. Value Analysis combines both
engineering and economics without, however, placing neither engineering or
economics first. They both are similarly important, as can be concluded by
the end of this paper.
1.9 – Value Analysis applied in the design of a cru sher frame
Value Analysis is a method that provides an operati ng technique using a
creative and organized approach. It is managed by a group, each of them
selected by their expertise in specific subjects an d coordinated by a Value
Analysis expert.
The Value Analysis group activity is managed in sev en stages:
1 – information and functional analysis,
2 – creativeness,
3 – evaluation and selection of the proposals,
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4 – the creative phase,
5 – development of the selected proposals,
6 – presentation of the selected proposals, set in order by priority,
7 – implementation phase.
An example of Value Analysis is presented, applied to the redesign of a
crusher frame.
The functions of the crusher, for the two variants are presented in table 1.
1.10 – Iteration 1. Establihing the levels of impor tance of the functions
The weight of functions in value is presented summa rized in table 3, and
figure 3 show the weight of functions in value.
The product value is equal to the sum of the functi ons levels and is equal to
78.
The following percentage values of the functions va lue weighting result:
XF1 = 15.4%, X F2 = 14.1%, X F3 = 12.8%, X F4 = 11.5%, X F5 = 10.3%, X F6 =
8.97%, X F7 = 7.69%, X F8 = 6.41%, X F9 = 5.13%, X F10 = 3.85%, X F11 = 2.56%,
XF12 = 1.28%.
1.11 – Economic dimensioning of the functions
Costs were assigned to the various functions by mea ns of the functions –
costs matrix shown in table 10.
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The percentage values of the functions participatio n in the total cost are:
YF1 = 12.46%, Y F2 = 20.51%, Y F3 = 6.29%, Y F4 = 6.68%, Y F5 = 4.45%, Y F6 =
9.27%, Y F7 = 8.85%, Y F8 = 5.33%, Y F9 = 11.48%, Y F10 = 2.73%, Y F11 = 2.73%,
YF12 = 9.15%.
1.12 – Diagrams
The construction of the diagrams is presented at la st.
Based on the values for coordinates X i and Y i presented in table 3 the
diagrams of figures 10, …, 12 are plotted.
The calculus is made with the smallest squares meth od.
The parameters have the following computed values: a = 0.89, α = 41.90 o, S
= 246 and S'= 0.
Table 11 provides the necessary values for construc ting the following types of
diagrams:
– the weight of functions in value diagram (figure 10),
– the weight of functions in cost diagram (figure 1 1),
– the weight of functions in value and in cost diag ram (figure 12).
52
53
Figure 10 – The weight of functions in value, Y 11 (17)
Regression equation describing this distribution is Y11 = Y 1 = – 1.282 * X +
16.66 (17) with R – squared value on chart R 2 = 1.
54
Figure 11 – The weight of functions in cost, Y 12 (18)
Regression equation describing this distribution is Y12 = – 0.682 * X + 12.77
(18) with R – squared value on chart R 2 = 0.246.
55
Figure 12 – The weight of functions in value and in cost, Y 13 (19)
Regression equation describing this distribution is Y13 = 0.582 * X + 3.212
(19) with R – squared value on chart R 2 = 0.371.
Figure 10 shows the ranking of the functions by the ir value.
Figure 11 shows the ranking of the functions by th eir functional cost.
The diagram allows significant comparisons of the f unctions total costs, and
within the total costs, of the work and material co sts:
– the very expensive functions with the highest wei ghting in the total cost of
the product,
56
– the secondary functions that are very expensive i n relation to the service
functions, or even more expensive than these.
The diagram reveals a Pareto type distribution, mea ning that 20 – 30% of the
total number of functions, include 70 – 80% of the total costs of the functions.
These functions are F1, F2, F9 and F12.
In the case of such a distribution, the first funct ions in the order of costs,
representing 20 – 30% of the total number of functi ons (in the above example
functions F1, F2, F9 and F12) are considered to be very expensive functions.
The real situation is represented by the shape of t he straight line in figure 12,
plotted by means of the smallest squares method, an d showing
disproportions in the distribution of costs and in the contribution of the various
functions to the value of the product.
An analysis of the diagram of figure 12 shows that functions F2, F7, F9 and
F12 are located above the regression line, indicati ng high costs, not justifiable
in relation to the value.
These aspects allow the assumption that these funct ions are deficient, hence
the solutions to be identified are to focus on thos e assemblies, parts,
materials and technological operations that contrib ute, within the general
structure of the product, to the achievement of the se functions.
1.13 – Evaluation by the economic dimensions criter ion
The economic dimension or the cost of the function represents the main
criterion for the critical evaluation of functions.
These evaluating aim at identifying those functions , the too costly technical
solutions of achievement of which affect the total manufacturing cost of the
analyzed product. A correctly completed critical ev aluation will directly lead to
the identification of what can be called the defici ent functions of the analyzed
product, that is of those functions that include us eless costs.
The deficient functions from the economic viewpoint appear as:
57
– very expensive functions in relation to the other s,
– too expensive functions in relation to the existi ng technical possibilities of
achievement.
Evaluation by the criterion of economic dimension c an be achieved in several
ways, presented below:
1 – comparison of costs per function, by means of t he diagram presented in
figure 11. This diagram allows comparisons of:
– the costs of the functions and comparisons of the total cost and the cost of
each function,
– work and material costs, highlighting the very ex pensive costs, with the
highest weighting in the total cost of the product,
– functions the achievement of which requires dispr oportionate costs, of
either work or material,
2 – comparison to the functions of other products. Other products refer to:
– products of the same typo – dimensional range or family, manufactured by
that company,
– products similar to the analyzed one, manufacture d by other companies,
– products with other destinations, but having some functions similar to those
of the analyzed product.
The aim is to identify potential too expensive func tions in relation to the
existing achieved technical solutions, existing and tested in other products.
It is recommended to relate to the simplest existin g technical solutions, that
ensure the achievement of the respective functions at minimum costs.
3 – theoretical evaluation of the costs of the func tion. Such an evaluation is
possible when a function is determined by a single part or by a small number
of parts. Evaluation is carried out in relation to material consumption. Based
on the conclusions resulting from the critical eval uation of the functions, the
study of the solutions will focus on identifying re – conception solutions of the
product, in view of reducing its costs and improvin g its value. This is possible
by constructive and technological modifications of the parts, sub-assemblies
58
and assemblies, according to the identified deficie nt functions.
1.14 – Conclusions
In this chapter it is studied a component of a fram e, who contribute at the
function F2 – Protects the assembly, F3 – Ensures e asy maintenance, F4 –
Maintains the assembly, F10 – Ensures evacuation, F 11 – Maintenance, F12 –
Easy to assemble / disassemble.
Function were chosen for study and were determined:
1 – the weight of functions in value, and
2 – the weight of functions in cost.
This study is the first iteration of Value Analysis , that starting from a given
situation of a crusher frame (design, processing me thods, etc.):
1 – have highlighted the deficient functions (funct ions with high costs, not
justifiable in relation to the value).
These functions are F2, F3, F4, F10, F11 and F12.
In the following will continue with the Value Analy sis study and in the second
iteration will be redesigning and optimizing the co mponent of a crusher frame,
from engineering and from the economic viewpoint.
1.15 – Iteration 2
An analysis from the technical and economic viewpoi nt will be carried out in
order to select a technically optimum variant for t he crusher frame.
Two constructive variants of part will be studied a nd eventually the most cost
effective and the most competitive one from the tec hnical and economic
viewpoint will selected. Prior to the actual study a number of basic ideas of
creative engineering will be presented in short.
Table 12 presents the denoting by 9 assessment crit eria of the analyzed
constructive variants of crusher frame.
59
Table 12 – Synthetic table with the analyzed constr uctive variants
No. Analysis criteria Type of crusher frame
Welded and
screws
assembled cast
1 Functional characteristics 6 6
2 Semi-product 2 6
3 Mechanical machining 4 6
4 Mounting 4 5
5 Repair 3 4
6 Rigidity 4 5
7 Ergonomics 6 6
8 Aesthetics 6 6
9 Cost 2 4
TOTAL 37 48
1.16 – The analysis of the constructive variants
The analysis of the constructive variants for the c ruser frame is presented on.
An analysis from the technical and economic viewpoi nt will be carried out in
order to select a technically optimum variant of cr user frame.
Three types of variants (welded, screws assembled, and cast) will be studied
in the fallowing, in terms of engineering and the m ost cost effective and the
most competitive one from the technical and economi c viewpoint will
selected.
The type of cast cruser frame obtained from a cast semi – product ensures
the best functional characteristics, if the technic al conditions for heat
treatment are provided. It has, however, the disadv antage that it allows only
one solution for reconditioning: build – up welding and re – machining to the
initial functional dimensions.
60
Type of cast variant obtained a better score than t he welded variant of cruser
frame variant, but in many cases the cruser frame m ust be made of two parts,
to facilitate quick installation and removal of the assembly.
But in many cases the Pillow Block must be made of two parts, to facilitate
quick installation and removal of all assembly. The bottom can body with the
frame, can be incorporated into the frame.
The cost of the final variant (cast variant of fram e) presented in table 13 is
3510 $, compared to the initial situation 4030 $ in table10.
61
62
63
By introducing the new data into table 14 the diagr ams of figures 13 and 14
are plotted. These diagrams will be compared to tho se of figures 3 and 7.
64
The parameters have the following computed values: a = 0.88, α ꞊ 41.4 o, S =
209, S' = 0.
It can be noticed that S have smaller values than i n the initial variant.
Figure 15 – The weight of functions in cost, Y 14 (20)
Regression equation describing this distribution is Y14 = 0.582 * X + 3.212
(20) with R – squared value on chart R 2 = 0.371.
Table 14 provides the necessary values for the plot ting of the following types
of diagrams:
– the weight of functions in value diagram (identic al with figure 3); this
65
diagram has not changes, as the value of the system and of the functions has
remained the same,
– the weight of functions in cost diagram (figure 1 5).
1.17 – Conclusions
In two steps of Value Analysis study one component of crusher, the frame,
who contribute at the function F2 – Protects the as sembly, F3 – Ensures easy
maintenance, F4 – Maintains the assembly, F10 – Ens ures evacuation, F11 –
Maintenance, F12 – Easy to assemble / disassemble), was redesign and
optimized:
1 – from engineering viewpoint:
– from variant of welded frame and screw assembly, composed of welded
modules, one complicated part (many components, mec hanical machining,
turning of metal parts complicated, long and very e xpensive, etc.) to the cast
variant of crusher frame (two component, mechanical machining, turning of
metal parts simple, short and less expensive than t he welded crusher frame,
etc.),
2 – from the economic viewpoint:
– the cost of function F2 – Protects the assembly, decrease from 20.52%, in
the first step, variant V5 welded and screw assembl y frame, to 17.50% in the
second step, for variant V4, cast frame, of Value A nalysis study (decrease
with 15.04%),
– the cost of function F5 – Provides work energy, d ecrease from 4.47%, in the
first step, variant V5 welded and screw assembly fr ame, to 4.40% in the
second step, for variant V4, cast frame of Value An alysis study (decrease
with 1.56%),
– the cost of function F6 – Converts the energy int o mechanical work
decrease from 9.33%, in the first step, variant V5 welded and screw assembly
frame, to 6.99% in the second step, for variant V4, cast frame, of Value
66
Analysis study (decrease with 24.91%),
– following the second iteration there can be seen in figures 12 and 15 that
the value of some functions increased, of other fun ctions decreased, but the
cost of those that increased eventually decreased d ue to the decrease of the
cost of the ”product”, from 4030 $, (table 10) the first iteration, to 3510 $,
(table 13) in the second iteration,
3 – in the third step of Value Analysis study are a nalyzed other functions
above the regression straight line and their costs reduced, then the
regression line is re – plotted and the functions r elocated above it are noted;
these functions too are analyzed in view of reducin g their costs, followed by
the re – plotting of the regression line, etc., …, etc.
The constructive solution is improved from one iter ation to the other.
At the end of the Value Analysis study the points a re aligned as perfectly as
possible along the straight line Y = a * X (12), w ith a tilt of 45 o, this is the
optimal situation, the values weighting of function s and the functions cost
weighting are equal, the final interest in Value An alysis study.
67
2 – Value Analysis in the design of industrial
machining processes
2.1 – Optimization of the design – processing techn ology for the
crusher frame
The selection of a manufacturing process for an app lication in engineering or
its replacement with another, superior in terms of engineering, economics and
environmental impact is an important stage in the d esign process of a
product.
The present paper presents a modern and original me thod for optimizing the
selection of a manufacturing process for a part – f or maximizing its
performance and minimizing its cost – to attain the sustainable development
objectives.
The work strategy involves setting the functions of the product, the matrix and
the related programs to select the optimal technolo gy, applying the value
analysis approach in order to obtain an optimal des ign – machining process,
to optimize the product's value – performance / cos t ratio.
For the automation of calculations and ease of desi gn work, the authors have
developed calculus programs in Excel and Visual Bas ic that perform all
calculations and draw all the necessary diagrams. T hese programs are
general (can be run for more input data) and are ea sy to use, even for those
unfamiliar with programming methods, due to the int erface of each program.
The literature presents detailed techniques for sel ecting a design – machining
process, but up to this moment there has not been p resented the optimization
of such a process linked to the Value Analysis appr oach, which takes into
consideration the engineering problems to the same extent as the economic
ones.
68
Based on our experience in the field, on the releva nt examples of applying
the Value Analysis approach to industrial products, the authors present a
paper that is a challenge for different fields of e xpertize.
In our society everything revolves around the notio n of value. In engineering
the Value is the judgment made on the product, base d on user expectations
and motivations, expressed by a dimension that incr eases or remains at least
equal when the satisfaction of user needs increases and / or the product
related expenses decrease.
Value Analysis is an organized and creative competi veness method aimed at
meeting the user needs using a specific design, fun ctional, economic and
multidisciplinary approach.
The Value Analysis Methodology was born in 1947 at General Electric. Faced
with a shortage of strategic materials, the company management asked L.D.
Miles to identify new materials that cost less. At that point he gradually set in
practice a rigorous plan followed by a 40% reductio n of costs /6/. Value
Analysis was quickly used in industries facing econ omic and strategic
deficiencies.
The guideline of Value Analysis is the Functional A nalysis. Starting from the
idea that a product is purchased because it perform s something that matches
a buyer's need, this property was called main funct ion. For the main function
to be performed, to the product should be added a s eries of secondary
functions, which are of interest only when they con tribute to the normal
performance of the main function. It is estimated t hat, overall, only 20% of the
manufacturing costs of the products are caused by t he core functions and
80% by the secondary functions.
The object of Value Analysis is the activity, the p roduct or its components.
Only the product bears value and its subassemblies or components
contribute to the usefulness of the product /7-13/.
Based on our experience in the field, on the releva nt examples of applying
the Value Analysis approach to products /14-16/, th e author propose a new
69
method for optimizing the selection of design – man ufacturing technologies for
various parts.
The optimization is achieved by setting the functio ns of the constituent
elements, of the operations that lead to changing t he structure of the design –
machining technological processes accompanied by a reduction in costs
without altering the performances.
2.2 – Materials and semifinished products used
The materials used for making the frames of the equ ipment are summarized
in table 15.
Table 15 – Materials used in the manufacture of fra meworks /17/
Materials Symbol Comments
Gray cast
iron Fc200
Fc250
Fc300
FcX200
FcX250
FcX300 It is used for molded frameworks;
Good mechanical properties (compressive
strength);
Amortization of vibration;
Possibility to manufacture one – piece frames
with guides;
After casting and, sometimes, edging a strain-
relief treatment is required;
Aging provides dimensional stability.
Steel
castings OT35
OT45
OT52 For heavy machines, exposed to large jerks in
operation. It is more difficult to cast than cast
iron; it requires strain-relief treatments.
70
Table 15 (continuation) – Materials used in the man ufacture of frameworks
/17/
Materials Symbol Comments
Weldable
steel OL37.1 Frames made by welding for single pieces and
small series when the precision and strain
conditions allow it;
Good mechanical properties (resistance to
pulsed loads, jerks);
Guides are made of other materials and are
applied to the frame;
After welding, a strain – relief treatment is
required.
Aluminium
alloys AlCu4MgMn Small machinery; machinery and woodworking
centers. In general, the machines working at
high speed.
2.3 – Optimizing the selection of the die machining technology for a
frame
The optimum technologic process for processing the frame, made of different
blanks, is selected and the implications of selecti ng each blank are presented
in terms of mechanical processing and costs.
The functional shape of the part is represented by size, tolerance to shape
and surface position, tolerance to size, surface qu ality, hardness and
operating conditions.
The treatment within the meaning of the Functional Analysis is presented
below, only partly, in the form of stating the func tions of the machining
process and of presenting the FAST diagram ( Function Analysis System
Technique) (figure 16).
The functions are listed below:
71
The main function is:
FP: Provides frame manufacturing,
– Service Functions
FS1: Provides semi-product manufacturing,
FS2: Ensures (allows) the performance of the heat t reatment,
FS3: Provides machining,
– Constraint Functions
FC1: Provides mounting on machine tool,
FC2: Allows easy assembly, disassembly,
FC3: Allows control,
FC4: Resists environmental actions,
FC5: Provides imposed parameters (hardness, wear re sistance, shock
resistance, …),
FC6: Protects the assembly,
FC7: Allows restoration,
FC8: Provides user security,
– Estimation Functions
FE 1: Provides the user interface,
FE 11: Liked by the user,
FE 12: Cost is affordable,
FE 13: Ergonomics,
FE 14: Reliability,
FE 15: Durability,
FE 16: Carries information,
– Technical Functions
FT1: Procedures (casting, forging, cast + forged, r olled, welded, combined,
…),
FT2: Roughing machining (cutting, turning, milling, boring, …),
FT3: Finishing machining,
FT4: Thermal treatment procedures.
72
73
Figure 16 – FAST diagram of the manufacturing proce ss of the frame
Following this analysis the following functions are noted:
F1: Provides semi manufacturing production,
F2: Provides material composition,
F3: Provides structural changes,
F4: Provides machining,
F5: Allows easy assembly, disassembly,
F6: Provides imposed parameters (hardness, wear res istance, shock
resistance, …),
F7: Resist environmental actions,
F8: Allows control,
F9: Allows restoration,
74
F10: Provides the user interface.
In this chapter the author highlight and present:
1 – the particular role of applying the Value Analy sis approach to the
selection of the die machining processes,
2 – the working mode for optimizing the value / cos t ratio,
3 – a valid and useful guide for specialists to opt imize the value / cost ratio of
the machining technological processes.
2.4 – Value Analysis Method
The object of the Value Analysis is the activity, p roduct or its components.
The product is the only one bearing value and its s ubassemblies or
components contribute to its utility.
In this paper the Value Analysis product / set is c onsidered to be a machining
technological process. The components of this produ ct / set are the stages /
operations of the frame machining technological pro cess.
The actual approach of the Value Analysis is not pr esented as it is already
known.
There shall be presented the iterations and conclus ions drawn from applying
the Value Analysis approach in the assumptions desc ribed above.
In this paper the Value Analysis product / set is c onsidered to be a frame
machining technological process.
The components of this product / set are the stage s / operations of the frame
machining technological process.
Table 16 shows the classification of the functions starting from the functional
analysis of the product – respectively – the frame machining process.
75
Table 16 – Nomenclatorul func țiilor
Symbol Functions *Type of
function
F4 Provides machining FS
F2 Provides material composition FS
F1 Provides semi manufacturing production FS
F3 Provides structural changes FS
F6 Provides imposed parameters (hardness, wear
resistance, shock resistance, …) FC
F7 Resist environmental actions FC
F9 Allows restoration FC
F8 Allows control FC
F10 Provides the user interface FE
F5 Allows easy assembly, disassembly FC
*FS – Service Function; FC – Constraint Functions; FE – Estimation Function
2.5 – Iteration 1. The weight of functions in value
Throughout the two iterations of the Value Analysi s presented in this chapter
there shall be kept the 10 functions outlined in ta ble 16.
Table 17 shows the weight of functions in value.
The author have developed a program / software that calculates all the values
from the shown tables and draws all the diagrams ne cessary for presenting
the findings, in all the iterations of the Value An alysis approach.
The calculus is made using the least squares method .
76
77
The percentage values of the weight of functions in value are shown in the
last row in table 17.
The following percentage values of the functions va lue weighting result:
XF4 = 18.2%, X F2 = 16.4%, X F1 = 14.5%, X F3 = 12.7%, X F6 = 10.9%, X F7 =
9.09%, X F9 = 7.27%, X F8 = 5.45%, X F10 = 3.64%, X F5 = 1.82%.
2.6 – Economic dimensioning of the functions
The allocation of costs to functions was performed in the matrix functions –
costs from table 18.
In table 18 the cost is distributed on the function / functions it is part of.
In the first iteration the machining process is as follows:
– alloying elements at maximum values,
– obtaining semi – product (four lateral walls indi vidually cast, then assembled
with screws – figure 17),
– primary heat treatment,
– roughing (milling, drilling, milling copying),
– secondary heat treatment (improvement),
– manual semi finishing,
– manual finishing (grinding, …),
– manual check (with template, ultrasonic), …
78
/18/
/19/
79
/20/
Figure 17 – Frame with four walls individually cast and subsequently bolted
80
81
82
The percentage values of the weight of functions in cost are shown in the last
row in table 18.
The percentage values of the functions participatio n in the total cost are:
YF4 = 21.33%, Y F2 = 9.91%, Y F1 = 12.92%, Y F3 = 11.13%, Y F6 = 15.13%, Y F7 =
5.10%, Y F9 = 5.25%, Y F8 = 8.87%, Y F10 = 5.20%, Y F5 = 5.14%.
From table 17 and table 18 are extracted the requir ed values that help draw
the following types of diagrams:
– the weight of functions in value diagram (figure 18),
– the weight of functions in cost diagram (figure 1 9),
– the weight of functions in value and in cost diag ram (figure 20).
83
Figure 18 – The weight of functions in value, Y 15 (21)
Regression equation describing this distribution is Y15 = – 1.818 * X + 20 (21)
with R – squared value on chart R 2 = 1.
84
Figure 19 – The weight of functions in cost, Y 16 (22)
Regression equation describing this distribution is Y16 = – 1.627 * X + 18.95
(22) with R – squared value on chart R 2 = 0.464.
The diagram in figure 18 shows the value ranking, p rioritization and weighting
of the functions.
The assessment of the functions which is shown in f igure 19 highlights the
85
most expensive functions.
The diagrams allow comparisons between the total co sts of the functions and,
within the total costs, there are highlighted:
– the very expensive functions, with the highest we ighting in the total cost of
the product,
– the functions whose implementation requires dispr oportionate costs as
compared with other functions.
The diagram in figure19 shows a Pareto type distrib ution, i.e. 25 to 30% of
the total number of functions comprises 70 to 80% o f the total cost of
functions.
These functions are shown in the example from figur e 19, functions F1, F3,
F4 and F6.
If there is such a distribution, the first function s in the order of costs,
representing 20 – 30% from the total number of func tions, the functions are
considered expensive.
The diagram in figure 19 shows:
– the regression line drawn using the method of the least squares and
– the comparison of functions in terms of costs.
In diagram from figure 19 can be seen the regressio n line of equation Y 16 = –
1.627 * X + 18.95 (22) and the correlation coeffici ent R2 = 0,464, which
approximates the arrangement of the points, express es the real situation of
the disparity between the weighting of functions co sts.
In the diagram from figure 20 there can be seen the following lines:
– the equation line Y = X (12) (the first bisector) , the line that averages the
weighting of functions in value and cost, expresses the ideal situation of the
disparity of the two weightings, the weighting of f unctions in value and cost,
– the regression line, of equation Y 17 = 0.921 * X + 0.714 (23), which
approximates the arrangement of the points, express es the actual situation of
the disparity of the two weightings, the weighting of functions in value and in
cost.
86
– the functions:
F4: Provides machining,
F1: Provides semi manufacturing production,
F6: Provides imposed parameters (hardness, wear res istance, shock
resistance, …),
F8: Allows control,
F10: Provides the user interface, and
F5: Allows easy assembly, disassembly are situated above the regression
line.
Figure 20 – The weight of functions in value and in cost, Y 17 (23)
87
Regression equation describing this distribution is Y17 = 0.921 * X + 0.714
(23) with R – squared value on chart R 2 = 0.55.
These functions are deficient and attention should be focused on them.
The cost of these functions should be reduced.
In order to reduce the disparity between the two we ightings, the weighting of
functions in value and in cost the points should be aligned as perfectly as
possible on the equation line Y = a * X (12), the f irst bisector from figure 20.
The criterion of this reduction often leads to carr ying out the Value Analysis
studies in cascade, the optimization of the constru ctive solution being thus an
iterative process. There are analysed first of all the functions situated above
the ideal regression line (12) and these are made c heaper, the real
regression line is drawn again and afterwards is fo und that other functions are
above it; these functions are analysed looking for solutions to decrease their
cost and the regression lines are drawn again, etc. , the constructive solution
being improved from one iteration to another.
Firstly, there shall be redesigned the functions th at are placed above the
regression line (12), in order to decrease their co st.
For the points situated below the line, the matter is more complicated. By
decreasing the cost of the functions situated above the line, the latter might
have a different slope and the points initially sit uated under the line might get
above it. It is also obvious that, by decreasing th e cost of these functions, the
total cost of the product decreases leading to the automatic increase of the
weighting of the functions whose cost has not chang ed. This is another
reason due to which some points situated below the line can get above it
without modifying in any way the absolute dimension of the costs of the
functions.
Secondly, the minimization of S' must be interprete d as much possible as an
increase of the value / cost ratio.
Thirdly, in the Value Analysis might be admitted th e increase of the cost of
some functions provided their value increases faste r than costs.
88
Basically, the criterion of minimizing S' often lea ds to carrying out the Value
Analysis studies in cascade, the optimization of th e constructive solution
being thus an iterative process. There are analysed first of all the functions
situated above the regression line (12) and these a re made cheaper, the
regression line is drawn again and afterwards is fo und that other functions are
above it; these functions are analysed looking for solutions to decrease their
cost and the regression line is drawn again, etc., the constructive solution
being improved from one iteration to another.
In the second iteration of the Value Analysis appro ach there shall be
considered the functions situated above the ideal r egression line (12):
F4: Provides machining,
F1: Provides semi manufacturing production,
F6: Provides imposed parameters (hardness, wear res istance, shock
resistance, …),
F8: Allows control,
F10: Provides the user interface and
F5: Allows easy assembly, disassembly.
2.7 – Iteration 2. Cost weighting of the functions
For the second iteration there shall be presented o nly the results in tabulated
form.
The weighting of the functions in value is the same as for the first iteration as
no functions were added or removed from the system (table 17).
As the functions that cost more are highlighted in figure 20, solutions shall be
suggested for reducing the cost of these functions.
The cost of these functions can be reduced by answe ring the following
questions:
– can there be used less expensive semi – products ?
– can the thermal regimes of the heat treatments be reduced ?
89
– can there be eliminated a heat treatment operatio n ?
– can there be used another thermal, thermo – chemi cal operation ?
– can the frame be milled at a lower cost ?
These questions must be answered in such manner so that the properties,
characteristics and performance of the frame alloy are not affected, but
improved if possible !
In our study, in the second iteration, actions were taken for the following cost
elements, manufacturing process are the follows:
– alloying elements to the minimum values,
– obtaining semi manufactured (integrally cast mach ine frame – figure 21),
– primary heat treatment in steps,
– roughing (milling 3D),
– secondary heat treatment (improvement),
– manual finishing (grinding, …),
– 3D check (ultrasonic)…
90
Figure 21 – Integrally cast machine frame /21/
The allocation of costs to functions was performed in the matrix functions –
costs from table 19.
In table 19 the cost is distributed on the function / functions it is part of.
Cost weighting of the functions are the values in t he last row of table 19.
91
92
The percentage values of the weight of functions in cost are shown in the last
row in table 19.
The percentage values of the functions participatio n in the total cost are:
YF4 = 21.98%, Y F2 = 6.89%, Y F1 = 22.60%, Y F3 = 8.94%, Y F6 = 14.86%, Y F7 =
4.14%, Y F9 = 3.45%, Y F8 = 6.51%, Y F10 = 5.07%, Y F5 = 5.51%.
The coordinates X i and Y i are given in the tables 17 and 19 and based on the
values for those coordinates the diagrams of figure s 22 and 23 are plotted:
– the weight of functions in value diagram (identica l with figure 19 – iteration
1),
– the weight of functions in cost diagram (figure 2 2),
– the weight of functions in value and in cost diag ram (figure 23).
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Figure 22 – The weight of functions in cost, Y 18 (24)
Regression equation describing this distribution is Y18 = – 1.5 * X + 18.25 (24)
with R – squared value on chart R 2 = 0.522.
The critical assessment of the functions presented in figure 22 highlights the
most expensive functions.
The diagram in figure 23 represents the regression line drawn using the least
squares method and presents the comparison of funct ions in terms of value
and cost.
94
In the diagram from figure 23 there can be seen the following lines:
– the equation line Y = X (12) (the first bisector) , the line that averages the
weighting of functions in value and in cost, expres ses the ideal situation of the
disparity of the two weightings,
– the regression line, of equation Y 19 = 0.8609 * X + 1.2644 (27), which
approximates the arrangement of the points, express es the real situation of
the disparity of the two weightings,
– functions:
F4: Provides machining,
F1: Provides semi manufacturing production,
F6: Provides imposed parameters (hardness, wear res istance, shock
resistance, …),
F8: Allows control,
F10: Provides the user interface and
F5: Allows easy assembly, disassembly.
are situated above the lines aforementioned.
The weighting of the cost is larger than the weight ing of the value of these
functions.
95
Figure 23 – The weight of functions in cost, Y 19 (27)
Regression equation describing this distribution is Y19 = 0.868 * X + 1.193
(27) with R – squared value on chart R 2 = 0.614.
Following the second iteration there can be seen in figures 20 and 23 that the
value of some functions increased, of other functio ns decreased, but the cost
of those that increased eventually decreased due to the decrease of the cost
of the ”product”.
There can be seen comparatively to figure 18 (itera tion 1) in figure 23
96
(iteration 2) that the functions are grouped closer to the ideal regression lines.
Below are presented comparatively the equations of the regression lines (the
real situation) and the correlation coefficients R 2 for the two iterations:
iteration 1: Y 17 = 0.921 * X + 0.714, R2 = 0.550 (23),
iteration 2: Y 19 = 0.868 * X + 1.193, R2 = 0.641 (27).
There can be seen an increase in the value of the c orrelation coefficient R 2 in
the second iteration as compared to the first itera tion, thus resulting that the
dispersion of the points decreased in relation with the regression line.
The iterations continue until the correlation coeff icient R 2 tends to value 1 and
the regression line (the real situation) tends to Y = X (12) (the ideal situation).
In two iterations of the Value Analysis method, the frame machining
technology was redesigned and optimized from the fo llowing points of view:
1 – engineering:
– the frame machining process was modified,
– the percentages of the alloying elements were mod ified from a maximum, in
the first iteration to a minimum, in the second ite ration,
– the primary continuous heat treatment was replace d with a heat treatment
in stages,
– the classic milling process (less expensive) of t he frame was replaced with
high velocity milling, using 3D processing machines (workmanship, more
expensive equipment but higher productivity and pre cision),
– the manual control process, using templates (less expensive) was replaced
with a 3D machines control process (workmanship, mo re expensive
equipment but higher productivity and precision),
– as the machining processes are more precise in th e 2-nd iteration, the final
remedies are fewer than in the first iteration,
2 – economics:
– the cost of the product decreased from 1995 $, in the first iteration to 1580
$ in the second iteration, a 20.80% decrease,
97
– the cost of functions F5 – Allows easy assembly, disassembly, F1 – Provides
semi manufacturing production and F4 – Provides mac hining decreases in the
second iteration compared to the first iteration (t able 20),
– the cost of function F5 – Allows easy assembly, d isassembly, decreased
from 21.33% in the first iteration, to 17.54% in th e second iteration, a
decrease of 17.76%,
– the cost of function F1 – Provides semi manufactu ring production,
decreased from 15.13% in the first iteration, to 12 .69% in the second
iteration, a decrease of 16.12%,
– the cost of function F4 – Provides machining, dec reased from 5.25% in the
first iteration, to 3.91% in the second iteration, a decrease of 25.52%,
– the cost of function F2 – Provides material compo sition, F3 – Provides
structural changes, F6 – Provides imposed parameter s (hardness, wear
resistance, shock resistance, …), F7 – Resist env ironmental actions, F9 –
Allows restoration, F8 – Allows control and F10 – P rovides the user interface,
increased in the second iteration, compared to the first iteration, but the cost
of the product “the processing of the frame” decrea sed in the second
iteration.
Table 20 – Comparing costs in the two iterations of Value Analysis method
F4 F1 F5
First iteration 21.90 22.61 5.51
Second Iteration 21.50 18.35 5.11
The reduction % 2.22 23.2 7.89
In the third iteration of the Value Analysis method there shall be analyzed the
functions situated above the regression line Y = X (12), (F2 – Provides
material composition, F3 – Provides structural chan ges, F6 – Provides
imposed parameters (hardness, wear resistance, shoc k resistance, …), F7 –
Resist environmental actions, F9 – Allows restorati on, F8 – Allows control and
98
F10 – Provides the user interface), there shall be analyzed the components
participating to achieving these functions and solu tions shall be proposed for
reducing the costs.
For the functions F4 – Provides machining and F3 – Provides structural
changes there shall be searched alloying and therma l treatments elements
that decrease their cost, but maintain the qualitie s and the properties of the
die material.
2.8 – Aplications
This guide can be used for optimizing the value / c ost ratio for different types
of machining technological processes for various pa rts:
Important is to achieve:
– functional modelling (with the help of functions) of the technological
processes used to machine parts (considered as a se t) within the Value
Analysis approach,
– the valorisation of the functions,
– the allocation of the cost of technological stage s / operations on the function
/ functions they are part of,
– the manner of interpreting the results from the d iagrams that represent the
weighting of the functions in value and in cost,
– the proposal of variants with a lower cost for pr ocessing operations, heat
treatment operations, control operations,
– the working manner using the programs made availa ble by the author.
Designing a mathematical – economic model for makin g decisions regarding
the optimization of the technological processes for machining parts in terms
of the Value Analysis approach is an absolute novel ty in the field.
This modelling has an important role because it ope ns a wide range of
engineering applications.
The modelling of the Value Analysis products, with various applications that
99
range from engineering, medicine, …, to services, h as lead in the last 65
years of applications to undeniable progress. Along with this study the Value
Analysis method takes an important step, opens new horizons for
applications and keeps up with the directions of th e science to penetrate in
different fields of activity, highlights the weakne sses (increased costs) of the
technological processes of machining different part s / elements and guides,
knowingly, the engineer towards the deficient point s and helps to remedy
them.
100
3 – Value Analysis in the choice of material for
crusher frame
The object of the Value Analysis is the activity, p roduct or its components.
The product is the only one bearing value and its s ubassemblies and
components contribute to its utility /22-28/.
3.1 – Selection of materials
The selection of a material for its application in engineering or replacing it with
another material superior in terms of engineering, economics and
environmental impact is an important stage in the d esign process of a
product.
In high – tech enterprises these choices can contro l the costs and the
performance of all components and of the assembly.
The design of a product includes the following step s:
– general preparation (familiarization with the glo bal status in the field),
– theoretical preparation (preparation of drawings, constructive diagrams and
calculus),
– drafting the project (analysis of existing soluti ons, preparing new drawings
and constructive diagrams and calculus),
– technical design (completion of the product's con structive diagram and of
the details),
– preparing the laboratory prototype (the model),
– experimentation and testing,
– design completion and error correction,
– manufacturing the zero series,
– factory trials,
– conclusions and recommendations.
101
The material selection is performed during the tech nical design stage and it
may be replaced during the experimenting and testin g period of the laboratory
prototype, depending on its behaviour to strains.
The criteria for material selection are multiple: p urpose, operational strains,
technological possibilities, working environment, p roduction type, reliability,
aesthetics, supply opportunities, cost, etc.
One aspect of optimizing the design process for a p roduct is the selection of
the materials and the processes that best meet the functional and
engineering requirements and which maximize perform ance and minimize
cost.
The working strategy in this regard includes the fo llowing stages:
1 – an accurate assessment of the actual working co nditions (functional role,
type, character and value of the mechanical strains , temperature and
environmental conditions),
2 – formulating a performance matrix to measure the correlation between
material and requirement(s),
3 – establishing the search procedures for identify ing solutions,
4 – identifying materials that meet the constraints and ranking them according
to their ability to satisfy requirements.
The characteristics imposed to iron steel used to m anufacture a frame are
briefly described below:
1 – mechanical characteristics,
2 – technological characteristics,
3 – operating characteristics generated by the chem ical composition and the
structure obtained by hardening and low temperature recovery:
– high resistance to wear,
– resistance to bending, tenacity, resistance to sm all deformations generated
by very fine granulation,
– resistance to shocks (tenacity), provided by the medium content of carbon
(0.5 – 0.62%C) and the suitable content of alloying elements, especially silica,
102
nickel, manganese, chromium and molybdenum,
– high hot-hardness, provided by large quantities of carbon (0.7 – 1.2%C),
wolfram, vanadium, chromium, manganese and cobalt,
– resistance to crushing provided by the elements d issolved in austenite and
martensite, especially by cobalt,
– dimensional stability, given by manganese, vanadi um, chromium and
wolfram.
4 – economic characteristics:
– production costs,
– supply difficulties.
For selecting a material used to manufacture a fram e, the author have
created two programs (in Excel and Visual basic) to automate calculations
/29/.
Figure 21 a shows the program in Excel and figure 2 1 b shows the program in
Visual Basic for the main characteristics of the ir on steel.
103
104
Each feature is granted weight according to the req uirements of the part
Vk – standard features;
tk – ranking:
1 – FS = very weak; 2-3 – S = weak; 4-5 – M = mediu m; 6-7 – B = good;
8-9 – FB = very good;
Hardenability: R – reduced; M – medium; Ad – deep.
105
106
The habit of draftsmen to select materials with fea tures that are superior to
the minimum features of the parts proves to be unec onomical. This fact does
not increase the technical performances of the prod uct the part is
incorporated in, it only makes such product more ex pensive.
The optimization of the choice of a material / allo y for a certain part proves to
be difficult and complex and it must be dealt with by the draftsmen.
Such choice / optimization means selecting a materi al / alloy that meets
minimum resistance, reliability, maintenance and du rability requirements of
the part at minimum costs.
The methodology of optimizing the choice of a mater ial / alloy for
manufacturing a part is presented below /29/.
In the literature there is no generally acknowledge d method for optimizing the
selection of a material / alloy for a concrete appl ication.
This is where the experience of drafting engineers is required and in the
selection of the material / alloy for a piece the f ollowing aspects are
considered:
– strains arisen during operation,
– operating conditions (temperature, pressure, spee d, environment),
– the precision class of the part (the surface qual ity and tolerances to shape
and position of surfaces and dimensional tolerances ) and
– manufacturing conditions.
Thus, the issue is dealt with in a very uneconomica l manner as it does not
fully exploit the features of the material / alloy.
Presented below, the manner of optimizing the choic e of a material / alloy
was named by the author of the method (Pisarciuc Mi hai) /29/, the method of
analysis of optimal values, method based on full ex ploitation of all functional,
technological and economic properties of a material / alloy in the actual
manufacturing conditions.
This methodology fits perfectly in the larger appro ach of the Value Analysis
method.
107
The method is very easy to apply in today's conditi ons of development of
calculation methods and of the existence of special ized software.
In this respect, I made my own software to automate calculations, as
presented in figure 21 a and b.
In short, the steps for solving this optimization a re presented below:
1– the functional role of the part is set,
2 – the factors influencing the choice of material s / alloys is set taking into
account:
– functional properties (physical, chemical, mechan ical, electric, magnetic,
optical, aesthetic),
– technological properties (castability, deformabil ity, turnability, weldability,
hardenability)
– economic properties (cost, energy consumption, fu els, raw material,
pollution, etc.),
3 – the following properties should be considered: thermal conductivity,
melting point, viscosity, density, corrosion resist ance, refractoriness,
elasticity, rigidity, plasticity, brittleness, cree p, toughness, tensile strength,
flow resistance, fatigue resistance, wear resistanc e, etc.
The factors related to the study of the material / alloy are entered in a table
from figures 21 a and b, on the rows, and the mater ials are entered in the
column.
4 – properties are assessed using a scoring system based on the value of
each property, and a score t k is awarded (scores such as 1, …, 3 or 1, …, 10 ,
1, …, 100, etc., according to the desired delimit ing accuracy) in the table from
figures 21 a and b),
5 – the weight of the properties is performed by gr anting to each k property a
dk weight.
The software can be used as follows:
– the weight of the importance of each primary fact or is determined by giving
each property a d k weight, the last row from the tables in figure 21.
108
The following requirement has to be met by the weig hts:
∑
==m
kkd
11
(28)
where m is the number of factors.
The optimum solution is the one where the maximum i s obtained, the
information in the table from figure 1 can be found in the last column:
t dk k
km
* max =
=∑
1
(29)
The object of the Value Analysis is the activity, p roduct or its components.
The product is the only one bearing value and its s ubassemblies or
components contribute to its utility.
Therefore, apparently a part, a component of an all oy could not be
approached using the Value Analysis approach.
However, if we penetrate within its structure /30/, at micro level we notice that
our part is made of several constituents, of severa l components (figure 24).
We call these constituents subassemblies and compon ents of our part, which
is an assembly at micro level.
109
Figure 24 – The ensemble the molded piece, the asse mbly of the part alloy
/30/
In the case of an assembly with several constituent s / alloying elements we
can apply the Value Analysis approach to this assem bly, which is the alloy of
the part.
Constituents / alloying elements and heat treatment operations, to a certain
extent, contribute each to the increase or decrease of some properties of the
part's alloy.
At this point, three important specifications must be made:
1 – it is taken into consideration an assembly, to which the Value Analysis
approach can be applied, the part's alloy and not t he actual part,
2 – each alloying element and each heat treatment o peration costs,
3 – this approach, to the knowledge of the authors, is unique and the first of
this kind in the specialty literature.
The actual approach of the Value Analysis is not pr esented as it is already
known.
There shall be presented the iterations and conclus ions drawn from applying
110
the Value Analysis approach in the assumptions desc ribed above.
3.2 – Value Analysis method
The first phase of Function Analysis includes the a ssertion of functions
followed by a validation of functions. The enunciat ion of functions are
presented below:
1 – Supports the assembly,
2 – Provides mechanical strength,
3 – Provides resistance to corrosion,
4 – Provides cutting machinability,
5 – Provides plasticity,
6 – Provides weldability,
7 – Provides casting,
8 – Provides dimensional stability,
9 – Ensures vibration resistance,
10 – Ensures maintenance (machined, weldability, .. .,),
11 – Provides thermal conductivity,
12 – Provides hardness
13 – Provides resistance to chafing and abrasive we ar,
14 – Provides elasticity limit.
Table 22 shows the functions retained after their v alidation.
Table 22 – Classification of the functions
Symbol Functions Type of
functions
F1 Supports the assembly FS
F2 Provides cutting machinability FC
F3 Provides mechanical strength FC
111
Table 22 (continuation) – Classification of the fun ctions
Symbol Functions *Type of
functions
F4 Provides dimensional stability FC
F5 Ensures maintenance (machined, weldability, …,) FC
F6 Provides resistance to chafing and abrasive wear FC
F7 Provides hardness FC
F8 Provides resistance to corrosion FC
F9 Ensures vibration resistance FC
F10 Provides mechanical strength FC
*FS – Service Functions; *FC – Constraint Function.
3.3 – Iteration 1. The weight of functions in value
Throughout the two iterations of the Value Analysis there shall be kept the 10
functions outlined in table 22.
The author have developed a program / software that calculates all the values
from the shown tables and draws all the diagrams ne cessary for presenting
the findings, in all the iterations of the method.
The calculus is made using the least squares method . In order to simplify, the
calculation is tabulated.
The program includes the following subroutines:
– value weighting of the functions (first table),
– cost weighting of the functions (second table), a nd
– computational elements for plotting the diagrams (the third table).
In table 23 shows the weight of functions in value.
112
113
The percentage values of the weight of functions in value are shown in the
last row in table 23.
The following percentage values of the functions va lue weighting result:
XF1 = 18.2%, X F7 = 16.4 X F6 = 14.5%, X F3 = 12.7%, X F5 = 10.9%, X F2 = 9.09%,
XF9 = 7.27%, X F10 = 5.45%, X F8 = 3.63%, X F4 = 1.82%.
3.4 – Economic dimensioning of the functions
To exemplify the Value Analysis method two cast iro n alloys for
manufacturing frames is chosen. The chemical compos ition of the cast iron
alloys for manufacturing frames and the costs of al loying elements are shown
in table 24 (Fr Cr 2.5 and Fc 250).
In the first iteration the cast iron alloys has:
– refractory cast iron Fr Cr 2.5, with the chemical composition shown in table
24 (structure G l + pearlite + (FeCr ) 3C /31/ – pp 102),
– alloying elements in the maximum percentage,
– apply the following heat treatments:
– relaxation annealing, consisting of heating at 65 0° C aimed at eliminating
the internal stress,
– recovery.
114
115
The conditions for forming primary and secondary st ructures, the structural
characteristics and the physico-mechanical, technol ogical and exploitation
properties of cast iron are determined by a large n umber of factors: structure
of the liquid cast iron, chemical composition of th e cast iron, rate of cooling,
heat treatments applied to parts after solidificati on
The influence of alloying elements on the cast iron is briefly presented
hereunder /31/.
An increase in the carbon content causes reduction in the strength and
hardness of the cast iron.
An increase in the silicon content causes an increa se in the ratio of ferrite and
a decrease in the pearlite ratio and therefore a de crease in the strength and
hardness and an increase in elongation.
Given the similarities and differences between the actions of carbon and
silicon on the structure and characteristics of the cast irons, in order to obtain
lamellar graphite grey cast iron with the best poss ible characteristics, the Si/C
ratio must be increased.
An increase in the manganese content causes an incr ease in the strength
and hardness and a decrease in the plasticity and t oughness. Manganese is
the main element for grey cast irons which regulate s the amount of pearlite in
the structure, thus providing superior strength cha racteristics. Exceeding
some critical amounts causes the occurrence of free cementite and thus an
increase in the hardness but a decrease in the stre ss and machinability.
In a ferritic structure, a content of up to 0.6% Mn does not practically affect
the characteristics of the cast irons while higher values cause an
improvement in the strength properties but a decrea se in the elongation due
to the embrittlement of ferrite.
Contents of less than 0.6% Mn are recommended for o btaining ferritic cast
irons while for higher ratios of pearlite this cont ent can be up to 1%.
The correlation between the composition of the base metal mass and the
content of manganese allows obtaining the desired s tructure directly by
116
moulding or reducing the duration of the heat treat ment, especially in the
ferritisation treatment.
The influence of the alloying elements on the chara cteristics of the frame cast
iron alloys is presented in table 25.
Table 25 – The influence of alloying elements on th e a cast iron alloys
properties
Properties
Alloying elements
Plasticity
Mechanical strength
Hardness
Corrosion resistance
Mechanical machining
Refractoriness
Abrasion resistance
Elongation
Tenacity
Thermal resistance
Silicon – ↓ ↓ – – – – ↑ – –
Manganese ↑ ↑↓ ↑ ↑↓ ↓ ↑ –
Sulfur ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ –
Phosphorus – ↑↓ – – – – ↑ – – –
Nickel – ↑ – – – – – – – –
Cooper – ↑↓ ↑ – ↓ – – – – –
Chrominm – – – – ↑ – ↑↑ – – ↑
Molibdenum – ↑ – – – – – – – ↑
Vanadium – ↑ ↑ – ↓ – – – – –
The sulphur adversely affects the properties of the cast iron due to the effect
of embrittlement manifested by the eutectics rich i n sulphur.
In the case of grey cast irons, an increase in the phosphorus content initially
determines an increase in the mechanical strength ( due to the ratio of pearlite
in the structure), then there is a decrease there o f.
In cast irons with high toughness and plasticity, t he phosphorous has a more
117
pronounced embrittling action. Alloying cast irons with phosphorous (0.25 –
1.5%) aims at increasing the fluidity or the wear r esistance.
The accompanying elements (Pb, Al, Zn , As, Sb, Ti, Bi) mostly increase the
hardness and strength but decrease the machinabilit y of the grey cast irons.
Nickel does not have adverse effects on iron as it increases the mechanical
strength.
Copper increases the mechanical strength and hardne ss of cast irons as it
decreases the tendency to form ferrite and finishes the pearlite.
Chromium increases the whitening tendency of the st ructure during
solidification. Chromium also increases the resista nce to wear, to heat and
oxidation of the cast iron.
Molybdenum is a very good alloying element because of its effect of
increasing the resistance of cast irons, especially to heat, thus increasing
their resistance to creep.
Vanadium is very active in what concerns the format ion of carbides and it is
used mainly for its effect of finishing the primary structure /31/.
The allocation of costs to functions was performed in the matrix functions –
costs from table 26.
In table 26 the cost is distributed on the function / functions it is part of.
118
119
The percentage values of the weight of functions in cost are shown in the last
row in table 26.
The following percentage values of the functions va lue weighting result:
YF1 = 21.9%, Y F7 = 11.9%, Y F6 = 11.1%, Y F3 = 12.7%, Y F5 =11.6%, Y F2 =
16.6%, Y F9 = 4.63%, Y F10 = 2.56%, Y F8 = 2.06%, Y F4 = 4.99%.
3.5 – The weight of functions in value and in cost
The check of this identity is performed using regre ssion analysis by
determining the linear function (the regression lin e) that represents the
average proportionality.
The regression line passes through the origin, as i t is considered that a
function with ”0” value costs ”0”.
The line has the expression of the relationship (12 ).
In the case of perfect proportionality all points a re on the line (12). In order to
simplify, the calculation is tabulated (table 27).
The coordinates X i and Y i are given in table 27 and based on the data
calculated in this table the diagrams from figures 25, 26 and 27 are drawn.
120
3.6 – Diagrams
From table 27 are extracted the required values tha t help draw the following
types of diagrams:
121
– the value weighting of the functions diagram (fig ure 25),
– the cost weighting of the functions diagram (figu re 26),
– the cost and value weighting of the functions dia gram (figure 27).
The diagram in figure 25 shows the value ranking, p rioritization and weighting
of the functions.
The assessment of the functions which is shown in f igure 26 highlights the
most expensive functions.
Figure 25 – The weight of functions in value, Y 20 (30)
122
Regression equation describing this distribution is Y20 = – 1.818 * X + 20 (30)
with R – squared value on chart R 2 = 1.
Figure 26 – The weight of functions in cost, Y 21 (31)
Regression equation describing this distribution is Y21 = – 1.711 * X + 19.41
(31) with R – squared value on chart R 2 = 0.652.
123
The diagrams allow comparisons between the total co sts of the functions and,
within the total costs, there are highlighted:
– the very expensive functions, with the highest we ighting in the total cost of
the product,
– the functions whose implementation requires dispr oportionate costs as
compared with other functions.
The diagram shows a Pareto type distribution, i.e. 20 to 30% of the total
number of functions comprises 70 to 80% of the tota l cost of functions.
These functions are shown in the example from figur e 26, functions F1, F2
and F3.
If there is such a distribution, the first function s in the order of costs,
representing 20 – 30 % from the total number of fun ctions, the functions are
considered expensive.
124
Figure 27 – The weight of functions in value and in cost, Y 22 (32)
Regression equation describing this distribution is Y22 = 0.955 * X + 0.400
(32) with R – squared value on chart R 2 = 0.719.
The diagram in figure 27 shows:
– the regression line drawn using the method of the least squares and
– the comparison of functions in terms of value and costs.
In diagram from figure 27 the following lines can b e seen:
– the equation line Y = X (12) (the first bisector) the line that averages the
125
weighting of functions in value and in cost, expres ses the ideal situation of the
disparity between the two weightings, the weighting of functions in value and
in cost,
– the regression line of equation Y 22 = 0.955 * X + 0.400 (32), which
approximates the arrangement of the points, express es the real situation of
the disparity between the two weightings, the weigh ting of functions in value
and in costs,
– functions F1, F2, F4 and F5 are situated above th e lines aforementioned.
The weighting of the cost is larger than the weight ing of the value of these
functions. These functions are deficient and attent ion should be focused on
them. The cost of these functions should be reduced .
For the second iteration of Value Analysis method s hall be considered the
functions:
F1 – Supports the assembly,
F5 – Ensures maintenance (machined, weldability, .. .,),
F2 – Provides cutting machinability,
F4 – Provides dimensional stability, situated abo ve the regression lines (12).
3.7 – Iteration 2. Economic dimensioning of functi ons
For the second iteration there shall be presented o nly the results.
The weighting of the functions in value is the same as for the first iteration as
no functions were added or removed from the system (table 23).
Being highlighted in figure 27, engineering solutio ns shall be suggested for
reducing the cost of these functions which are more expensive.
The cost of these functions can be reduced by answe ring the following
questions:
– can there be used less expensive alloying element s?
– can certain too expensive alloying elements be el iminated?
– can the quantity of alloying materials be decreas ed?
126
– can the thermal regimes of the heat treatments be reduced?
– can a heat treatment operation be eliminated?
– can another heat or thermo – chemical treatment o peration be used?
These questions must be answered in such manner so that the properties,
characteristics and performance of the part's alloy are not affected, but
improved if possible !
In the second iterations actions were taken for the following cost elements:
– cast iron Fc 250, with the chemical composition s hown in table 24,
(structure G l + Ferrite),
– alloying elements in the minimum percentage,
– there is applied a quenching heat treatment + rec overy.
The allocation of costs to functions was performed in the functions – costs
matrix from table 28.
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128
The percentage values of the weight of functions in cost are shown in the last
row in table 28.
The following percentage values of the functions co st weighting result:
YF1 = 16.1%, Y F7 = 12.8%, Y F6 =12.6%, Y F3 = 9.91%, Y F5 = 12.3%, Y F2 =
12.3%, Y F9 = 5.94%, Y F10 = 6.31%, Y F8 = 5.51%, Y F4 = 6.31%.
3.8 – The weight of functions in value and in cost
Coordinates X i and Y i are given in table 29 and, based on the data
calculated and presented in this table, the diagram s from figures 6 and 7 are
drawn.
129
130
From table 29 are extracted the required values tha t help draw the following
types of diagrams:
– the weight of functions in value diagram (identic al with figure 25 – iteration
1),
– the weight of functions in cost diagram (figure 2 8),
– the weight of functions in value and in cost (fig ure 29).
Figure 28 – The weight of functions in cost, Y 23 (33)
131
Regression equation describing this distribution is Y23 = – 1.374 * X + 17.56
(33) with R – squared value on chart R 2 = 0.242.
The diagram in figure 29 represents the regression line drawn using the least
squares method and presents the comparison of funct ions in terms of value
and cost.
In the diagram from figure 29 there can be seen the following lines:
– the equation line Y = X (12) (the first bisector) , the line that averages the
weighting of functions in value and in cost, expres ses the ideal situation of the
disparity of the two weightings, the weighting of f unctions in value and in cost,
– the regression line, of equation Y 24 = 0.706 * X + 2.667 (34), which
approximates the arrangement of the points, express es the real situation of
the disparity of the two weightings, the weighting of functions in value and in
cost.
132
Figure 29 – The weight of functions in value and in cost, Y 24 (34)
Regression equation describing this distribution is Y24 = 0.706 * X + 2.667
(34) with R – squared value on chart R 2 = 0.836.
Following the second iteration there can be seen in figure 29 that the deficient
functions which are expensive situated above the id eal regression line are the
functions:
F5: Ensures maintenance (machined, weldability, … ,),
133
F2: Provides cutting machinability,
F4: Provides dimensional stability, situated above the regression lines (12).
There can be seen comparatively to figure 27 (itera tion 1) in figure 29
(iteration 2) that the functions are grouped closer to the ideal regression lines.
There can be seen comparatively to figure 27 (itera tion 1) in figure 29
(iteration 2) that the functions are grouped closer to the ideal regression lines,
the equations of the regression lines (the real sit uation) and the correlation
coefficients R 2 for the two iterations are:
iteration 1: Y 22 = 0.955 * X + 0.400, R2 = 0.719 (32),
iteration 2: Y 24 = 0.706 * X + 2.667, R2 = 0.836 (34).
There can be seen an increase in the value of the c orrelation coefficient R 2 in
the second iteration as compared to the first itera tion, thus resulting that the
dispersion of the points decreased in relation with the regression line.
The iterations continue until the correlation coeff icient R 2 tends to value 1 and
the regression line (the real situation) tends to Y = X (12) (the ideal situation).
3.9 – Results
In two iterations of the Value Analysis study our p roduct was redesigned and
optimized in terms of:
1 – engineering:
– the percentages of the alloying elements were mod ified from a cast iron
alloys for manufacturing the crusher frame Fr Cr 2. 5 at Fc 250,
2 – economics:
– the cost of function F7 – Provides hardness, decr eased from 11.92% in the
first iteration to 4.49% in the second iteration, a decrease of 165%,
– the cost of function F6 – Provides resistance to chafing and abrasive wear,
decreased from 11.02% in the first iteration to 6.1 3% in the second iteration,
a decrease of 80.3%,
– the cost of function F3 – Provides mechanical str ength, decreased from
134
12.66% in the first iteration to 12.35% in the seco nd iteration, a decrease of
2.56%,
– the cost of function F10 – Provides mechanical st rength, decreased from
2.56% in the first iteration to 4.03% in the second iteration, a decrease of
81.5%,
– following the second iteration there can be seen in figures 26 and 28 that
the value of some functions increased, of other fun ctions decreased, but the
cost of those that increased eventually decreased d ue to the decrease of the
cost of the ”product”, from 339 $, (table 26) the first iteration, to 309.8 $,
(table 28) in the second iteration.
In the third iteration of the Value Analysis study there shall be analysed the
functions situated above the regression line Y = X (12), F2, F4, F5, there shall
be analysed the ”components” participating to achie ving these functions and
engineering solutions shall be proposed for reducin g the costs and may
continued with the fourth iteration, …, where app ropriate.
3.10 – Aplications / Conclusions
This guide can be used for optimizing the value / c ost ratio for:
– different types of alloys for the parts (cast iro n, steel, aluminium alloys,
copper alloys, etc.),
– composite materials,
– the modelling using the Value Analysis method pre sents the allocation on
the costs of the function and not on the components of the product; this point
of view opens unforeseen possibilities for interpre ting the results and pairing
the components of the product.
It is important to achieve:
– the functional modelling (using functions) of the alloy (viewed as a whole)
within the Value Analysis method,
– the valorisation of the alloy's functions,
135
– the allocation of the cost of a alloy / assembly constituent to the functions it
is part of,
– the manner of interpreting the results from the d iagrams,
– the proposal of variants with a lower cost for: a lloying elements and applied
heat treatment operations,
– the working manner using the programs.
The design of a mathematical – economic model for t aking some decisions on
the optimization of the alloying elements percentag e, on the manner of
applying the heat treatment in the case of some all oys in terms of Value
Analysis method is an absolute novelty in the field .
This modelling has an important role as it opens a wide range of engineering
applications. The modelling of the Value Analysis p roducts, having various
applications from engineering, medicine to services has brought in the last 65
years of applications undeniable progress.
With this study, the Value Analysis makes an import ant step in the world of
”micro assemblies”, it opens new horizons of applic ations and keeps up with
the directions of science to penetrate the micro, n ano, …, limits.
136
References
/1/ – Suzanne Niles, Standardisation et modularité de l'infrastructure
physique de réseaux critiques, http://www.apcmedia. com/salestools/SNIS-
66ZTJB_R0_FR.pdf-12.12.11
Standardisation et modularité de l'infrastructure p hysique de …
www.apcmedia.com/…/VAVR-626VPD_R0…
/2/ – MODULARITE: L’ EMERGENCE DE NOUVELLES COMPETE NCES
ORGANISATIONNELLES DANS LES INDUSTRIES DE BIENS
COMPLEXES ? Florent Catel, Doctorant LEPII, Jean-Ch arles Monatéri,
Chercheur CNRS LEPII Systèmes modulaires : évolutio n de la fiabilité,
www.apcmedia.com/…/SNIS-66ZTJB_R0_F…
/3/ – Simon H., (1962), The architecture of complex ity, Proceedings of the
American Philosophical Society, in Garud, R., Kumar aswamy, A. et Langlois,
R.N., Managing in the modular age (2003), Blackwell Publishing, Oxford,
pp15-38)
/4/ – Alexander C., (1964), Notes on the synthesis of form, Harvard University
Press, Cambridge
/5/ – Fixson S., (2003), Product architecture: a st rategic decision variable
linking product, process and supply chain, working paper
/6/ – http://www.value-eng.org/valueworld/older_iss ues/1968_April.pdf
/7/ – www.afav.asso.fr/html/av.html
/8/ – www.scav-csva.org/v1/html/fr/Methode.html
/9/ – michel.jean.free.fr/cours.AV.html
/10/ – michel.jean.free.fr/AV/introduction.html
/11/ – michel.jean.free.fr/AV/glossaire.html
/12/ – www.valorex, constantineau.francine
/13/ – NF EN 12973, NF X-50.150, NF X-50.151, NF X- 50.152, NF X-50.153
/14/ – Chichernea Fl., Chichernea Al., Managementul Valorii, ISBN 978-973-
598-852-4, Editura Universit ății Transilvania Bra șov, 2011
137
/15/ – Chichernea Fl., Chichernea Al., Itera țiile Analizei Valorii, ISBN 978-
606-19-0040-4, Editura Universit ății Transilvania Bra șov, 2012
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ISBN 978-606-19-0039-8, Editura Universit ății Transilvania Bra șov, 2012
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mecanica/PROIECTAREA-ORGANOLOGICA-A-MAS55893.php
/18/ – www.prengineering.com/default.aspx?newsItemI D=225
/19/ – http://whocrushingscreening.co.za/repairs/
/20/ – www.me2000.ca/mill.htm
/21/ – AUBEMA MASCHINENFABRIK GMBH · KÖLNER STRASSE 94 · D-
51702 BERGNEUSTADT · TEL.: +49(0)2261/4094-0 · FAX: -325, E-MAIL:
INFO@AUBEMA.DE · INTERNET: WWW.AUBEMA.COM
/22/ – Jean Michel, JM 338. (2001) The value n° 90, pp 2-7
/23/ – Brun G., Constantineau F. (2001) The value m anagement: a new
management style, AFNOR 2001
/24/ – Miles, L., D. Techniques of Value Analysis a nd Engineering, Available
from: http://wendt.library.wisc.edu/miles/milesbook .html, Accessed:
01/02/2011
/25/ – Kenneth C., Value Analysis and function anal ysis system technique,
Available from: http://www.npd-solutions.com/va.htm l, Accessed: 10/02/2011
/26/ – Chichernea Fl., Chichernea Al., (2010) Value Analysis, Part IV, Rev.
Metalurgia International nr.3, 2010, pg. 20, ISSN – 1582-2214
http://www.metalurgia.ro/Metalurgia_International_3 _2010.pdf
/27/ – Chichernea Fl., Chichernea, Al., Managementu l Valorii, ISBN 978-973-
598-852-4, Editura Universit ății Transilvania Bra șov, 2011
/28/ – Chichernea Fl., Chichernea Al., Itera țiile Analizei Valorii, ISBN 978-
606-19-0040-4, Editura Universit ății Transilvania Bra șov, 2012
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http://facultate.regielive.ro/laboratoare/metalurgi e_si_siderurgie/alegerea_
materialul optim pentru confectionarea_unei_piese-1 34112.html kw:alege
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materialul unei piese
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http://www.keytometals.com/page.aspx?ID=CheckArticl e&site=kts&LN=TR&
NM=194-last accessed October, 2013
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Additional References
/32/ – Chichernea Fl., Value analysis, part I. Rev. Metalurgia International,
nr.9, 2008, pg. 62, ISSN 1582-2214
/33/ – Chichernea Fl., Value Analysis, part II, Rev . Metalurgia International,
nr.3, 2009, pg. 5, ISSN 1582-2214
/34/ – Chichernea Fl., Chichernea Al., Value Analys is, part III, Rev.
Metalurgia International, nr.2, 2010, pg. 22, ISSN 1582-2214
/35/ – Ștef ănescu Cl., Tehnologii de executare a pieselor prin turnare, pg.101,
EdituraTehnic ă Bucure ști, 1981
/36/ – http://marcel.suciu.eu/Cartea_Std_Mater.pdf
/37/ – http://www.castingquality.com/wp-content/upl oads/2010/07/grey-cast-
ironcomposition.pdf
/38/ – Bejan V., Tehnologia fabric ării și a repar ării utilajelor tehnologice, vol.I,
II, ISBN 973-95458-7-4, Bucure ști, 1991
139
Contents
Three times Value Analysis …………………………… 1
1 – Value Analysis in modular design product …… ………………………………….. 2
1.1 – The modular design of industrial machinery an d equipment …………….. 2
1.2 – Introduction ………………………….. …………………………………………… …….. 2
1.3 – Value Analysis Method ………………….. ………………………………………….. 4
1.4 – Selecting ideas and technical solutions. …. …………………………………….. 6
Design and manufacture the crushing equipment ….. ………………………………. 6
1.5 – The weight of functions in value and in cost …………………………………. 19
1.6 – Conclusions …………………………… …………………………………………… …. 43
1.7 – The weight of functions in value and incost . …………………………………. 45
1.8 – Optimizing the crusher construction. …….. ……………………………………. 47
Implementing of Value Analysis method in designing of a foundry equipment
………………………………………….. …………………………………………… …………… 47
1.9 – Value Analysis applied in the design of a cru sher frame ………………… 47
1.10 – Iteration 1. Establihing the levels of impor tance of the functions ……. 48
1.11 – Economic dimensioning of the functions ….. ……………………………….. 48
1.12 – Diagrams …………………………….. …………………………………………… …. 51
1.13 – Evaluation by the economic dimensions criter ion ………………………… 56
1.14 – Conclusions ………………………….. …………………………………………… … 58
1.15 – Iteration 2 ………………………….. …………………………………………… ……. 58
1.16 – The analysis of the constructive variants .. …………………………………. 59
1.17 – Conclusions ………………………….. …………………………………………… … 65
2 – Value Analysis in the design of industrial mach ining processes …………. 67
2.1 – Optimization of the design – processing techn ology for the crusher
frame ……………………………………… …………………………………………… ……….. 67
2.2 – Materials and semifinished products used …. ……………………………….. 69
2.3 – Optimizing the selection of the die machining technology for a frame . 70
2.4 – Value Analysis Method ………………….. ………………………………………… 74
140
2.5 – Iteration 1. The weight of functions in value …………………………………. 75
2.6 – Economic dimensioning of the functions …… ………………………………… 77
2.7 – Iteration 2. Cost weighting of the functions …………………………………… 88
2.8 – Aplications …………………………… …………………………………………… …… 98
3 – Value Analysis in the choice of material for cr usher frame ………………. 100
3.1 – Selection of materials …………………. ………………………………………….. 100
3.2 – Value Analysis method ………………….. ………………………………………. 110
3.3 – Iteration 1. The weight of functions in value ……………………………….. 111
3.4 – Economic dimensioning of the functions …… ………………………………. 113
3.5 – The weight of functions in value and in cost ……………………………….. 119
3.6 – Diagrams ……………………………… …………………………………………… … 120
3.7 – Iteration 2. Economic dimensioning of functi ons ………………………… 125
3.8 – The weight of functions in value and in cost ……………………………….. 128
3.9 – Results ………………………………. …………………………………………… …… 133
3.10 – Aplications / Conclusions ……………… ………………………………………. 134
References …………………………………. …………………………………………… ….. 136
Additional References ……………………….. …………………………………………… 138
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