Integration of Fuzzy AHP and Fuzzy TOPSIS Methods for Wire Electric Discharge Machining of Titanium (Ti6Al4V) Alloy Using RSM

Abstract

Titanium and its alloys exhibit numerous uses in aerospace, automobile, biomedical and marine industries because of their enhanced mechanical properties. However, the machinability of titanium alloys can be cumbersome due to their lower density, high hardness, low thermal conductivity, and low elastic modulus. The wire electrical discharge machining (WEDM) process is an effective choice for machining titanium and its alloys due to its unique machining characteristics. The present work proposes multi-objective optimization of WEDM on Ti6Al4V alloy using a fuzzy integrated multi-criteria decision-making (MCDM) approach. The use of MCDM has become an active area of research due to its proven ability to solve complex problems. The novelty of the present work is to use integrated fuzzy analytic hierarchy process (AHP) and fuzzy technique for order preference by similarity to ideal situation (TOPSIS) to optimize the WEDM process. The experiments were systematically conducted adapting the face-centered central composite design approach of response surface methodology. Three independent factors—pulse-on time (Ton), pulse-off time (Toff), and current—were chosen, each having three levels to monitor the process response in terms of cutting speed (VC), material removal rate (MRR), and surface roughness (SR). To assess the relevance and significance of the models, an analysis of variance was carried out. The optimal process parameters after integrating fuzzy AHP coupled with fuzzy TOPSIS approach found were Ton = 40 µs, Toff = 15 µs, and current = 2A.

Multi-Response Optimization of Al2O3 Nanopowder-Mixed Wire Electrical Discharge Machining Process Parameters of Nitinol Shape Memory Alloy

“…Discharge energy gets converted into thermal energy. Thermal energy melts and vaporizes the material from the work surface [ 50 ]. Thus, higher thermal energy erodes more material from work, and thereby, increases the MRR.…”

Sustainability Assessment of a Low-Income Building: A BIM-LCSA-FAHP-Based Analysis

“…Among them, the Analytic Hierarchy Process (AHP) has the advantages of simplicity of application, repeatability, and consistency of results [57], and for that reason, it gained recognition, and currently is one of the most employed MCDM techniques by academics, and by industry in general [58][59][60]. The AHP method was developed by Saaty [61] and its approach is based on the fragmentation of the decision process into smaller parts, constituting a hierarchical system of objectives, metrics, and scenarios [62][63][64]. The methodology converts qualitative judgments into a quantitative comparison through pairwise observations between criteria and priorities, helping decision makers to reach the best possible solution to an MCDM problem [58,60,64,65].…”

“…The AHP method was developed by Saaty [61] and its approach is based on the fragmentation of the decision process into smaller parts, constituting a hierarchical system of objectives, metrics, and scenarios [62][63][64]. The methodology converts qualitative judgments into a quantitative comparison through pairwise observations between criteria and priorities, helping decision makers to reach the best possible solution to an MCDM problem [58,60,64,65].…”

“…However, despite being widely used, traditional AHP has some limitations, as its application depends directly on the prioritization of criteria assigned by experts and decision makers that can be potentially biased [65,66]. Furthermore, the use of crisp numbers derived from linguistic responses is generally not efficient in dealing with uncertainty and ambiguity, intrinsic characteristics of human thought [64,67]. Thus, conventional AHP may not reflect the true opinion of those involved in the decision-making process.…”

“…where: Step 2: Compute fuzzy weights using geometric mean method The fuzzy mean geometric value of the ith criterion against each criterion was determined using the geometric mean method, computing the fuzzy triangular vector r i Equation (3). The relative fuzzy criteria weight of the ith criterion is represented as a TFN and computed applying Equation ( 4) [64][65][66]82].…”