mcdm method

APPLICATION
OF
MCDM METHOD
FOR OPTIMIZATION OF
SPECIFICATION OF WHEEL
IN
GRINDING PROCESS

Abstract
The grinding process, which in the present scenario is practiced in a large and
diverse area of manufacturing and tool making is used to produce a high surface
finish with a close tolerance and for machining hard materials. The process is a
variation of polishing and uses abrasive materials held together by an adhesive
generally in form of GRINDING WHEEL Almost any material can be ground,
aluminium, steel, ceramics, even diamond or glass. Grinding is used to form
countless types of products such as automobile engines, sharp edges on knives,
ball bearing and drills etc

The grinding process is under continuous improvement. Research at universities
and in industry means that the science of grinding is constantly advancing
resulting in increased production, saved revenues and higher quality products
for the consumers. In grinding of hard and brittle materials such as advanced
ceramics or hard metal, process behavior and work result are closely connected
with material removal mechanisms. Material removal mechanisms are
determined by complex interactions between material properties, the mechanical
and thermal loads acting on work piece, geometry of the grits, the kinematics of
grit engagements and other specifications of the grinding. Experimental
investigations of surface grinding processes show that material removal
mechanisms are also influenced by dynamic conditions in the contact zone.
These dynamic conditions, that are not chatter vibrations, can have both a
positive and negative influence on surface quality, process forces and wear of
the grinding wheel. For a given machine tool and work piece the dynamic
contact zone conditions and specifications of wheel can be optimized by
improvement in the grinding wheel. For analyzing the dynamic contact zone
conditions based on the behavior of the grinding wheel and its specification
various methods can be used. By means of these analyses the specification of
grinding wheels can be adapted to meet the requirements of a determined
grinding process with regard to tool wear, surface roughness of the work piece
and process forces.

An Intelligent

Multi-Criteria Decision Making
In engineering design and manufacturing, conflicting disciplines and
technologies are always involved in the design process. Multi-Criteria Decision
Making (MCDM) methods can help Decision Makers to effectively deal with
such situation and make wise design decisions to produce an optimized design.
There are a variety of existing MCDM methods, thus the selection of the most
appropriate methods is critical since the use of inappropriate methods is often
the cause of misleading design decisions. However, the selection of MCDM
methods itself is a complicated MCDM problem that needs to be prudently
conducted. In this project we will aim at proposing a hybrid MCDM method to
select the most suitable MCDM method for the problem under consideration.
Relative weights are assigned to each evaluation criterion to represent the
decision maker’s preference information. The MCDM method selection
approach, its implemented and an intelligent knowledge based system will be
developed, consisting of a MCDM library storing the widely used decision
making methods and a knowledge base providing the information required for
the method selection process. The optimization of specification of wheel
problem using the MCDM method will be conducted as a proof of
implementation to demonstrate the functionality and effectiveness of the
intelligent decision support system as well as discovering an option for the
decision maker’s to select an optimized grinding wheel specification to improve
the results of grinding operation that will lead to increased production, saved
revenues and higher quality products for the consumers.

LITERATURE REVIEW
"Multi-Criteria Decision Making (MCDM) is the study of methods and
procedures by which concerns about multiple conflicting criteria can be
formally incorporated into the management planning process ", as defined by the
International Society on Multiple Criteria Decision Making
Multi-Criteria Decision Making (MCDM) is a process that allows one to
make decisions in the presence of multiple, potentially conflicting criteria.
MCDM can be divided into two categories: Multi-Attribute Decision Making
(MADM), and Multi-Objective Decision Making (MODM). MADM involves
the selection of the “best” alternative from pre-specified alternatives described
in terms of multiple attributes; MODM involves the design of alternatives
which optimize the multiple objectives of Decision Maker. Although MCDM as
a discipline only has a relatively short history of about 40 years, over 70
MCDM techniques have been developed for facilitating the decision making
process.
Among these developed MCDM methods, different methods have different
underlying assumptions, information requirements, analysis models, and
decision rules that are designed for solving a certain class of decision making
problems. This implies that it is critical to select the most appropriate method to
solve the problem under consideration since the use of unsuitable method
always leads to misleading design decisions. Consequently, bad design
decisions will result in big loss to the society, such as property damage or
personal injury. However, it can be seen that the selection of MCDM methods
itself is a complicated MCDM problem and needs to be prudently performed.
The Decision Support Systems constitute a class of computer-based
information systems which use data and MCDM models to organize
information for facilitating the decision making process. The Intelligent
Decision Support Systems are interactive computer-based systems which use
data, MCDM models, and artificial intelligence techniques for supporting
decision making in making decisions for the complex problems. The IDSS is
capable of providing decision making with effective mechanisms to better
understand the decision making problem and the implications of their decision
behaviors by allowing them to interactively exchange information with the
systems.

Project Planning

To effectively select the most appropriate MCDM method for the
optimization of specification of wheel, a systematic framework is proposed in
this study. The proposed approach consists of eight steps: define the problem,
define the evaluation criteria, initial screen, define the preferences on evaluation
criteria, define the MCDM method for selection, evaluate the MCDM methods,
choose the most suitable method, and conduct sensitivity analysis.
Step 1: Define the problem
The characteristics of the decision making problem under consideration are
addressed in the problem definition step, such as identifying the number of
alternatives, attributes, and constraints etc.. The available information about the
decision making problem is the basis on which the most appropriate MCDM
techniques will be evaluated and utilized to solve the problem.

Step 2: Define the evaluation criteria
The proper determination of the applicable evaluation criteria is important
because they have great influence on the outcome of the MCDM method
selection process. However, simply using every criterion in the selection
process is not the best approach because the more criteria used, the more
information is required, which will result in higher computational cost. In this
study, the characteristics of the MCDM methods will be identified by the
relevant evaluation criteria in the form of a questionnaire. 10 questions are
defined to capture the advantages, disadvantages, applicability, computational
complexity etc. of each MCDM method, as shown in the following. The defined
evaluation criteria will be used as the attributes of a MCDM formulation and as
the input data of decision matrix for method selection.
1) Is the method able to handle MADM, MODM, or MCDM problem?
2) Does the method evaluate the feasibility of the alternatives?
3) Is the method able to capture uncertainties existing in the problem?
4) What input data are required by the method?
5) What preference information does the method use?
6) What metric does the method use to rank the alternatives?
7) Can the method deal changing alternatives or requirements?
8) Does the method handle qualitative or quantitative data?
9) Does the method deal with discrete or continuous data?
10) Can the method handle the problem with hierarchy structure of attributes?

Step 3: Initial screen
In the initial screen step, the dominated and infeasible MCDM methods are
eliminated by dominance and conjunctive. An alternative is dominated if there
is another alternative which excels it in one or more attributes and equals it in
the remainder. The dominated MCDM methods are eliminated by the
dominance method, which does not require any assumption or any
transformation of attributes. The sieve of dominance takes the following
procedures. Compare the first two alternatives and if one is dominated by the
other, discard the dominated one; then compare the un-discarded alternative
with the third alternative and discard any dominated alternative; and then
introduce the forth alternative and repeat this process until the last alternative
has been compared.
A set of non-dominated alternatives may possess unacceptable or infeasible
attribute values. The conjunctive method is employed to remove the
unacceptable alternatives, in which the decision maker set up the cutoff values
he/she will accept for each of the attributes. Any alternative which has an
attribute value worse than the cutoff values will be eliminated.

Step 4: Define the preferences on evaluation criteria
Usually, after the initial screen step is completed, multiple MCDM methods
are expected to remain, otherwise we can directly choose the only one left to
solve the decision making problem.
With the 10 evaluation criteria defined in the step 2, the decision maker’s
preference information on the evaluation criteria is defined. This will reflect
which criterion is more important to the decision maker when he/she makes
decisions on method selection.

Step 5: Define the MCDM method for selection
In existing commonly used MCDM methods are identified and stored in the
method base as candidate methods for selection. The Simple Additive
Weighting (SAW) method is chosen to select the most suitable MCDM methods
considering its simplicity and general acceptability. Basically, the SAW method
provides a weighted summation of the attributes of each method, and the one
with the highest score is considered as the most appropriate method. Though
SAW is used in this study, it is worth to noting that other MCDM methods can
be employed to handle the same MCDM methods selection problem.

Step 6: Evaluate the MCDM methods
Mathematical formulation of Appropriateness Index (AI) is used to rank the
MCDM methods. The method with the highest AI will be recommended as the
most appropriate method to solve the problem under consideration.

Step 7: Choose the most suitable method for optimization of specification of
GrindingWheel
The MCDM method which has the highest AI will be selected as the most
appropriate method to solve the given decision making problem. If the DM is
satisfied with the final results, he/she can implement the solution and move
forward. Otherwise, he/she can go back to step 2 and modify the input data or
preference information and repeat the selection process until a satisfied outcome
is obtained. be displayed to provide guidance to DM how to get the final
solution by using the selected method. In addition, the detailed mathematical
calculation steps are also built in the MATLAB-based DSS, which highly
facilitates the decision making process. Thus, the DM can input their data
according to the instruction, and get the final results by clicking one
corresponding button.

Step 8: Conduct analysis
In this section, selection of an optimized specification of grinding wheel
problem is conducted to improve the capabilities of the grinding operation
products by proposed MCDM decision support system. It is observed that
different decision maker often have different preference information on the
evaluation criteria and different answers to the 10 questions, thus, analysis
should be performed on the MCDM method selection algorithm in order to
analyze its robustness with respect to parameter variations, such as the variation
of decision maker’s preference information and the input data.
If the decision maker is satisfied with the final results, he/she can implement
the solution and move forward. Otherwise, he/she can go back to step 2 and
modify the input data or preference information and repeat the selection process
until a satisfied outcome is obtained.
In this implementation, emphasis is put on explaining the holistic process of
the intelligent MCDM decision support system. Thus, the step by step problem
solving process is explained and discussed for this decision making problem.

CONCLUSION

In this project, a systematic MCDM selection process is developed and applied to
optimize the specification of grinding wheel. The selection of the most appropriate
MCDM methods is formulated as a complicated MCDM problem and a hybrid
framework is proposed to deal with this problem and the method evaluation criteria
for selecting the most appropriate method are defined.
Study shows that the proposed decision support system can effectively help
decision maker with selecting the most appropriate method and guide the decision
maker to get the final decision for the problem.
It is worth noting that there is no absolute “best” MCDM method since the MCDM
method selection is problem specified. The selection of the most suitable MCDM
method depends on the problem under consideration. In addition, new methods may
emerge during the process of MCDM methods selection as we get more insights on
the characteristics of the methods. For example, by combining the characteristics of
two or more decision making methods, decision maker may get one hybrid method
which is more effective for solving the given problem. This project is a future work
that needs further investigation in the method selection process.

mcdm method

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