Investigation and Prediction of ECMM characteristics of Hardened Die Steel with Nanoparticle Added Electrolytes Using Hybrid Deep Neural Network

Abstract In our work, the process efficiency of ECMM should be improved by using different combinations nano-particles and added electrolytes. The superior aim this work is to improve predict machining characteristics die hardened steel, namely material removal rate (MRR), Tool wear (TWR) Surface Roughness (Ra). conditions are optimized Response Methodology (RSM) based on Box Behnken Design. better Nano electrolyte Deer Hunting Optimization (DHO) machined outcomes, performances predicted a hybrid Deep Neural Network (DNN) DHO. DNN-DHO outcome MRR 0.361 mg/min, TWR 0.272 mg/min Ra 2.511 μm. validation results show that proposed model performed well obtained above 0.99 regression for both training DNN-DHO, where root mean square error ranges between 0.018 0.024.