Multi-Layered Neural Prediction and Metaheuristic Optimization of Wire Cut Electrical Discharge Machining Process for AISI D2 Steel

The traditional wire-cut electrical discharge machining process suffers from several critical drawbacks, such as uneven surface quality, suboptimal material hardness, and ineffective tuning of process parameters, due to the multi-variable nature of machining dynamics. The research introduces a new hybrid optimization system, which combines the Piranha Predation Optimization Algorithm- Brownian Motion-Levy Flight Mechanism (PPOA-BM-LFM) and a Multi-level Stacked Houghless Network-based Bidirectional Long Short-Term Memory (MultiSHTM) for predictive modeling. The main contribution of this research is the development of an intelligent system that combines a deep learning model with a piranha-based hybrid predation optimization algorithm to enable adaptive data-driven control and immediate adjustments to wire-cut electrical discharge machining parameters. Using historical data design and MATLAB-based simulation, key wire-cut electrical discharge machining parameters- current, pulse-on time, pulse-off time, and wire speed are optimized to minimize surface roughness and maximize hardness. Complex proportional assessment-based multi-criteria analysis identified pulse on time and wire speed as major factors influencing surface roughness and hardness, achieving R 2 = 0.8914 and R 2 = 0.9521, respectively. The optimal setting (1 A, 12.83 μs T ON , 6 μs T OFF , 5 m/min wire speed) yielded 0.274 μm surface roughness, 123.43 hardness, and 0.96 desirability. The proposed model attained 97.6% accuracy and the lowest mean absolute percentage error of 1.35%, outperforming existing models with superior precision and computational efficiency.