Metaheuristic approach in machinability evaluation of silicon carbide particle/glass fiber–reinforced polymer matrix composites during electrochemical discharge machining process

Abstract

The advanced manufacturing and machining techniques are adopting a population-based metaheuristic algorithm for production, predicting and decision-making. Using the same approach, this paper deals with the application of bees algorithm and differential evolution to forecast the optimal parametric values aiming to obtain maximum material removal rate during electrochemical discharge machining of silicon carbide particle/glass fiber–reinforced polymer matrix composite. The bees algorithm follows swarm-based approach, while differential evolution works on a population-based approach. The experimental design was prepared on the basis of Taguchi’s methodology using an L16 orthogonal array. For the experimental analysis, the main variables in the process, that is, electrolyte concentration (g/L), inter-electrode gap (mm), duty factor and voltage (volts), were selected as main input parameters, and material removal rate (mg/min) was adjudged as output quality characteristic. A comparative investigation reveals that the maximum material removal rate was obtained by the parametric value proposed by differential evolution that follows the bees algorithm and Taguchi’s methodology. Furthermore, the results prove that the differential evolution algorithm has better collective assessment capability with a rapid converging rate.

Comparison of machinability of nickel alloys using WEDM

“…It was proved that the abrasive air-jet technology is very effective for the machining of quartz crystals. Antil et al 13 tried to foresee optimal parameters for obtaining the maximum material removal rate during the WEDM process of silicon carbide particle/glass fibre-reinforced polymer matrix composites. For this prediction they used a metaheuristic approach and algorithm.…”

Optimization of laser-assisted jet electrochemical machining parameters by grey relational analysis and fuzzy logic

“…The optimization of the machining process is very important. It is done to optimize the desired response characteristics by selecting suitable machining parameters (Antil et al , 2018). The optimization is done to improve the efficiency of the machining process, and in the long term, the efficiency of the manufacturing as well as a business enterprise (Antil, 2020; Antil et al , 2020).…”

Multi-objective optimization of hard milling process of AISI H13 in terms of productivity, quality, and cutting energy under nanofluid minimum quantity lubrication condition

“…21,31 To solve the optimization problem, there are several methods that are commonly used in engineering. Some general optimization techniques are employed in metal cutting engineering such as Taguchi [32][33][34][35] and GRA; [36][37][38] however, these techniques cannot give optimal results with a ''real optimal'' solution because they only optimize according to the level of design variables. A simultaneous multi-response optimization that is frequently used in practice by Mia and co-authors is desirability function approach.…”