Optimizing plasma arc cutting processes using physics-based metaheuristic algorithms: a comparative analysis - Pendokhare, Devendra; Chakraborty, Shankar (International journal on interactive design and manufacturing, 2025)
Plasma arc cutting (PAC) has become a flexible and effective method for precisely cutting complex profiles on various difficult-to-machine materials, including superalloys and composites, due to its many benefits, like higher dimensional accuracy, productivity and ability to cut thicker materials. Process optimization is important for obtaining high-quality cuts, reducing material wastage and increasing overall productivity. However, it is difficult to optimize this process because of involvement of many variables, intricate cutting mechanism, and interaction between the process parameters and responses. In this paper, applications of five newly developed physics-based metaheuristic algorithms, i.e. Archimedes optimization algorithm (AOA), atom search optimization (ASO), nuclear reaction optimization (NRO), electromagnetic field optimization (EFO) and gravitational search algorithm (GSA) are proposed for optimizing two PAC processes. Their optimization performance is compared in terms of computing effort, convergence time and solution quality. To find out the best parametric intermixes for resolving the multi-objective optimization problems, an effort is also put forward to develop the corresponding Pareto optimal fronts. For both the examples, compared to its competitors, EFO appears as the most effective metaheuristic for achieving the best combinations of the relevant process parameters. For the first example, EFO achieves 42.50, 35.85 and 25.55% improvements for single-objective optimization; and 35.71, 18.19 and 9% improvements for multi-objective optimization, in material removal rate, kerf taper and heat affected zone, respectively against the observations of the past researchers. In case of the second example, these improvements are 44.2, 26.42 and 17.12% for single-objective optimization; and 22.1, 10.36 and 6.21% for multi-objective optimization in surface roughness, kerf width and microhardness, respectively. With respect to average computation time, for multi-objective optimization, EFO saves 77.1, 36.3, 65.6 and 98.73% (for example 1); and 49.7, 33.12, 50.3 and 142.01% (for example 2) of the runtime against AOA, ASO, NRO and GSA, respectively. Results of two quality metrics (spacing and hypervolume) and two non-parametric statistical tests (Wilcoxon rank-sum test and Friedman’s mean rank test) also prove superiority of EFO against the other physics-based algorithms under consideration. Thus, the primary objective of this paper is to explore application of five physics-based algorithms, specially EFO in deriving the optimal mixtures of two PAC processes resulting in their superior cutting efficiency, along with higher productivity and surface quality.