Multi-objective optimization to enhance surface integrity in WEDM for Al-matrix composite: A comparative assessment of self-weight adjusting MCDMs and objective weight integrated hybrid TOPSIS methods - Gaurav Anand; Santanu Sardar; Satesh Sah; Ashim Guha; Debdulal Das (Results in Surfaces and Interfaces)
Wire-electrical discharge machining (WEDM) is immensely preferred for fabricating intricately shaped components made of advanced materials, such as composite materials, maintaining excellent precision and surface finish. The process involves a large number of input variables associated with workpiece and tool materials as well as machine and dielectric media, while the output responses are often contradictory. Therefore, the commercial adaptation of WEDM requires multi-objective optimization, which could be done by multi-criteria decision-making (MCDM) techniques. The present research performed a comprehensive evaluation of two distinct variants of MCDM techniques, namely, self-weight adjusting (GRA, PSI, SECA, and DEAR) and external weight (SD, Entropy, CRITIC, CILOS, IDOCRIW, and MEREC) integrated TOPSIS methods. These techniques were assessed for the optimization of modification in surface chemistry (MSC), surface roughness (SR), and cutting velocity (CV) in WEDM of an in-situ stir-cast Al–Mg/20 Al3Fe composite employing the experimental results conducted based on RSM-CCD layout considering input variables of pulse-on time, servo voltage, peak current, and wire-feed rate. It was found that the optimal solution varied widely among the self-weight adjusting MCDMs; however, the external weight integrated TOPSIS methods provided the exact optimal solution even though the relative weights of CV, SR, and MSC varied considerably. The DEAR method, which was relatively simple to implement, matched the robust TOPSIS method and, hence, could be explored in manufacturing sectors. The function of process variables on the integrity of the machined surfaces was corroborated through meticulous characterizations of the surfaces and sub-surfaces of the identified best and worst machining conditions.