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.