APPLICATION OF MCDM METHOD FOR OPTIMIZATION OFSPECIFICATION OF WHEEL IN GRINDING PROCESS
AbstractThe grinding process, which in the present scenario is practiced in a large anddiverse area of manufacturing and tool making is used to produce a high surfacefinish with a close tolerance and for machining hard materials. The process is avariation of polishing and uses abrasive materials held together by an adhesivegenerally in form of GRINDING WHEEL Almost any material can be ground,aluminium, steel, ceramics, even diamond or glass. Grinding is used to formcountless types of products such as automobile engines, sharp edges on knives,ball bearing and drills etcThe grinding process is under continuous improvement. Research at universitiesand in industry means that the science of grinding is constantly advancingresulting in increased production, saved revenues and higher quality productsfor the consumers. In grinding of hard and brittle materials such as advancedceramics or hard metal, process behavior and work result are closely connectedwith material removal mechanisms. Material removal mechanisms aredetermined by complex interactions between material properties, the mechanicaland thermal loads acting on work piece, geometry of the grits, the kinematics ofgrit engagements and other specifications of the grinding. Experimentalinvestigations of surface grinding processes show that material removalmechanisms are also influenced by dynamic conditions in the contact zone.These dynamic conditions, that are not chatter vibrations, can have both apositive and negative influence on surface quality, process forces and wear ofthe grinding wheel. For a given machine tool and work piece the dynamiccontact zone conditions and specifications of wheel can be optimized byimprovement in the grinding wheel. For analyzing the dynamic contact zoneconditions based on the behavior of the grinding wheel and its specificationvarious methods can be used. By means of these analyses the specification ofgrinding wheels can be adapted to meet the requirements of a determinedgrinding process with regard to tool wear, surface roughness of the work pieceand process forces.
An Intelligent Multi-Criteria Decision MakingIn engineering design and manufacturing, conflicting disciplines andtechnologies are always involved in the design process. Multi-Criteria DecisionMaking (MCDM) methods can help Decision Makers to effectively deal withsuch situation and make wise design decisions to produce an optimized design.There are a variety of existing MCDM methods, thus the selection of the mostappropriate methods is critical since the use of inappropriate methods is oftenthe cause of misleading design decisions. However, the selection of MCDMmethods itself is a complicated MCDM problem that needs to be prudentlyconducted. In this project we will aim at proposing a hybrid MCDM method toselect the most suitable MCDM method for the problem under consideration.Relative weights are assigned to each evaluation criterion to represent thedecision maker’s preference information. The MCDM method selectionapproach, its implemented and an intelligent knowledge based system will bedeveloped, consisting of a MCDM library storing the widely used decisionmaking methods and a knowledge base providing the information required forthe method selection process. The optimization of specification of wheelproblem using the MCDM method will be conducted as a proof ofimplementation to demonstrate the functionality and effectiveness of theintelligent decision support system as well as discovering an option for thedecision maker’s to select an optimized grinding wheel specification to improvethe results of grinding operation that will lead to increased production, savedrevenues and higher quality products for the consumers.
LITERATURE REVIEW "Multi-Criteria Decision Making (MCDM) is the study of methods andprocedures by which concerns about multiple conflicting criteria can beformally incorporated into the management planning process ", as defined by theInternational Society on Multiple Criteria Decision Making Multi-Criteria Decision Making (MCDM) is a process that allows one tomake 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 involvesthe selection of the “best” alternative from pre-specified alternatives describedin terms of multiple attributes; MODM involves the design of alternativeswhich optimize the multiple objectives of Decision Maker. Although MCDM asa discipline only has a relatively short history of about 40 years, over 70MCDM techniques have been developed for facilitating the decision makingprocess. Among these developed MCDM methods, different methods have differentunderlying assumptions, information requirements, analysis models, anddecision rules that are designed for solving a certain class of decision makingproblems. This implies that it is critical to select the most appropriate method tosolve the problem under consideration since the use of unsuitable methodalways leads to misleading design decisions. Consequently, bad designdecisions will result in big loss to the society, such as property damage orpersonal injury. However, it can be seen that the selection of MCDM methodsitself is a complicated MCDM problem and needs to be prudently performed. The Decision Support Systems constitute a class of computer-basedinformation systems which use data and MCDM models to organizeinformation for facilitating the decision making process. The IntelligentDecision Support Systems are interactive computer-based systems which usedata, MCDM models, and artificial intelligence techniques for supportingdecision making in making decisions for the complex problems. The IDSS iscapable of providing decision making with effective mechanisms to betterunderstand the decision making problem and the implications of their decisionbehaviors by allowing them to interactively exchange information with thesystems.
Project Planning To effectively select the most appropriate MCDM method for theoptimization of specification of wheel, a systematic framework is proposed inthis study. The proposed approach consists of eight steps: define the problem,define the evaluation criteria, initial screen, define the preferences on evaluationcriteria, 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 areaddressed in the problem definition step, such as identifying the number ofalternatives, attributes, and constraints etc.. The available information about thedecision making problem is the basis on which the most appropriate MCDMtechniques will be evaluated and utilized to solve the problem.Step 2: Define the evaluation criteria The proper determination of the applicable evaluation criteria is importantbecause they have great influence on the outcome of the MCDM methodselection process. However, simply using every criterion in the selectionprocess is not the best approach because the more criteria used, the moreinformation is required, which will result in higher computational cost. In thisstudy, the characteristics of the MCDM methods will be identified by therelevant evaluation criteria in the form of a questionnaire. 10 questions aredefined to capture the advantages, disadvantages, applicability, computationalcomplexity etc. of each MCDM method, as shown in the following. The definedevaluation criteria will be used as the attributes of a MCDM formulation and asthe 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 areeliminated by dominance and conjunctive. An alternative is dominated if thereis another alternative which excels it in one or more attributes and equals it inthe remainder. The dominated MCDM methods are eliminated by thedominance method, which does not require any assumption or anytransformation of attributes. The sieve of dominance takes the followingprocedures. Compare the first two alternatives and if one is dominated by theother, discard the dominated one; then compare the un-discarded alternativewith the third alternative and discard any dominated alternative; and thenintroduce the forth alternative and repeat this process until the last alternativehas been compared. A set of non-dominated alternatives may possess unacceptable or infeasibleattribute values. The conjunctive method is employed to remove theunacceptable alternatives, in which the decision maker set up the cutoff valueshe/she will accept for each of the attributes. Any alternative which has anattribute 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 methodsare expected to remain, otherwise we can directly choose the only one left tosolve the decision making problem. With the 10 evaluation criteria defined in the step 2, the decision maker’spreference information on the evaluation criteria is defined. This will reflectwhich criterion is more important to the decision maker when he/she makesdecisions on method selection.Step 5: Define the MCDM method for selection In existing commonly used MCDM methods are identified and stored in themethod base as candidate methods for selection. The Simple AdditiveWeighting (SAW) method is chosen to select the most suitable MCDM methodsconsidering its simplicity and general acceptability. Basically, the SAW methodprovides a weighted summation of the attributes of each method, and the onewith the highest score is considered as the most appropriate method. ThoughSAW is used in this study, it is worth to noting that other MCDM methods canbe 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 theMCDM methods. The method with the highest AI will be recommended as themost appropriate method to solve the problem under consideration.Step 7: Choose the most suitable method for optimization of specification ofGrindingWheelThe MCDM method which has the highest AI will be selected as the mostappropriate method to solve the given decision making problem. If the DM issatisfied with the final results, he/she can implement the solution and moveforward. Otherwise, he/she can go back to step 2 and modify the input data orpreference information and repeat the selection process until a satisfied outcomeis obtained. be displayed to provide guidance to DM how to get the finalsolution by using the selected method. In addition, the detailed mathematicalcalculation steps are also built in the MATLAB-based DSS, which highlyfacilitates the decision making process. Thus, the DM can input their dataaccording to the instruction, and get the final results by clicking onecorresponding button.Step 8: Conduct analysis In this section, selection of an optimized specification of grinding wheelproblem is conducted to improve the capabilities of the grinding operationproducts by proposed MCDM decision support system. It is observed thatdifferent decision maker often have different preference information on theevaluation criteria and different answers to the 10 questions, thus, analysisshould be performed on the MCDM method selection algorithm in order toanalyze its robustness with respect to parameter variations, such as the variationof decision maker’s preference information and the input data. If the decision maker is satisfied with the final results, he/she can implementthe solution and move forward. Otherwise, he/she can go back to step 2 andmodify the input data or preference information and repeat the selection processuntil a satisfied outcome is obtained. In this implementation, emphasis is put on explaining the holistic process ofthe intelligent MCDM decision support system. Thus, the step by step problemsolving process is explained and discussed for this decision making problem.
CONCLUSION In this project, a systematic MCDM selection process is developed and applied tooptimize the specification of grinding wheel. The selection of the most appropriateMCDM methods is formulated as a complicated MCDM problem and a hybridframework is proposed to deal with this problem and the method evaluation criteriafor selecting the most appropriate method are defined. Study shows that the proposed decision support system can effectively helpdecision maker with selecting the most appropriate method and guide the decisionmaker to get the final decision for the problem. It is worth noting that there is no absolute “best” MCDM method since the MCDMmethod selection is problem specified. The selection of the most suitable MCDMmethod depends on the problem under consideration. In addition, new methods mayemerge during the process of MCDM methods selection as we get more insights onthe characteristics of the methods. For example, by combining the characteristics oftwo or more decision making methods, decision maker may get one hybrid methodwhich is more effective for solving the given problem. This project is a future workthat needs further investigation in the method selection process.