Artificial Intelligence and Hybrid Optimization Techniques for Non-Traditional Machining: A Comparative Review of Recent Advances and Applications

Non-traditional machining (NTM) processes—electrical discharge machining (EDM), electrochemical machining (ECM), laser beam machining (LBM), abrasive water jet machining (AWJM), and ultrasonic machining (USM)—are indispensable for producing geometrically intricate features in hard-to-cut super alloys, ceramics, and composites. Their governing physics are stiff, coupled, and non-stationary, making first-principles real-time control prohibitively expensive. This review surveys how machine learning surrogates—multilayer perceptron’s (MLPs), Gaussian process regression (GPR), adaptive neuro-fuzzy inference systems (ANFIS), and physics-informed neural networks (PINNs)—and hybrid meta-heuristic optimizers—particle swarm optimization fused with gravitational search (PSO-GSA), and NSGA-III with reference-point decomposition—are being deployed to bridge the gap between laboratory models and shop-floor control. We present the formal governing equations for each process, compare surrogate architectures on five engineering-relevant axes, and synthesize published performance improvements across six NTM modalities in a standardized table. Critical attention is given to small-data generalization failure, the inadequacy of Ra as a surface-integrity proxy, and the architectural requirements for deploying inference on embedded FPGA/PLC controllers.