TOPSIS Model

936 Words 4 Pages
Simultaneous Optimization of Arithmetic Average Roughness (Ra), Geometric Average Roughness (Rq) and Ten Point Height Average Roughness (Rz) Using TOPSIS

The present work is to explore the effect of EDM process parameters on the surface roughness characteristics Ra, Rq and Rz. For the experimentation, twenty seven alternatives of EDM process parameters, Pulse on time (TON), Pulse off time (TOFF), Wire Tension (WT) and Wire Feed (WF) were considered as per the Taguchi’s standard L27 Orthogonal Array. The Roughness characteristics of Arithmetic average (Ra), Geometric average (or) RMS value (Rq) and Ten point height average (Rz) were considered as the experimental responses. Multi-criterion decision making method, TOPSIS has been employed for
…show more content…
It was developed by Hwang and Yoon in 1980. The basic concept of this method is that the selected alternative should have the shortest distance from the ideal solution and the farthest distance from the negative-ideal solution in any geometrical sense. [6-7] The TOPSIS method assumes that each criterion has a tendency of monotonically increasing or decreasing utility. Therefore, it is easy to define the positive ideal and negative-ideal solutions. The Euclidean distance approach was proposed to evaluate the relative closeness of the alternatives to the ideal solution. Thus, the preference order of the alternatives can be derived from a series of comparisons of these relative distances. The TOPSIS method first converts the various criteria, dimensions into non-dimensional criteria. Generally A+ indicates the most preferable alternative or the ideal solution. Similarly, alternative A- indicates the least preferable alternative or the negative ideal solution. [8-9] The relative importance or weight of a criterion indicates the priority assigned to the criterion by the decision-maker while ranking the alternatives in a Multi criteria Decision-Making (MCDM) environment. The Entropy Method estimates the weights of the various criteria from the given payoff matrix and is independent of the views of the decision-maker. This method is particularly useful to explore contrasts between sets of data. These sets of data can be mapped as a set of alternative solutions in the payoff matrix where each alternative solution is evaluated in terms of its outcome. The philosophy of this method is based on the amount of information available and its relationship with the importance of the criterion. If the entropy value is high, the uncertainty contained in the criterion vector is high, diversification of the information is low and correspondingly the criterion is less important. This method is advantageous as

Related Documents