Multi-response optimization in WEDM process of Al–Si alloy using TLBO-graph theory algorithm towards sustainability

One of the challenges facing manufacturing industries is optimizing the power consumption for the development of sustainable manufacturing processes. To precisely measure the wire cut electric discharge matching (WEDM) performance of aluminum–silicon (Al–Si) alloy, the present study proposed a hybrid teaching and learning–based optimization (HTLBO) to take on the challenge. The HTLBO comprises teaching and learning–based optimization technique and graph theory algorithm to improve WEDM performance. The power consumption, kerf width, surface quality, and metal removal rate are considered performance characteristics. First, an auxiliary electrode was placed on the top surface of the Al–Si alloy and reduced surface defects including micro-cracks, micro-voids, and micro-globules from the machined surface around the kerf and also improved metal removal rate. The proposed methodology was used in the second stage and optimized the process parameters. The optimal working condition was as follows: 3.8 A of discharge current, 10 µs of discharge duration, 24 µs of discharge interval, 20 V of discharge voltage, and 17 N of wire tension. At optimal working condition, the metal removal rate, power consumption, surface roughness, and kerf width are found as 19.72 mm 3 /min, 49 W, 0.7 µm, and 351 µm, respectively. Moreover, the HTLBO took less time in optimization when compared with conventional TLBO.