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

Yash Jain Jain, Tejaswi .., Dr. Manish Kumar

Abstract


Non-traditional machining (NTM) processes—electrical discharge machining (EDM), electrochemical machin-ing (ECM), laser beam machining (LBM), abrasive water jet machining (AWJM), and ultrasonic machining (USM)—are indispensable for producing geometrically intricate fea-tures in hard-to-cut superalloys, ceramics, and composites. Their governing physics are stiff, coupled, and non-stationary, making first-principles real-time control prohibitively expen-sive. This review surveys how machine learning surrogates—multilayer perceptrons (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.

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References


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