Annealing of Monel 400 Alloy Using Principal Component Analysis, Hyper-parameter Optimization, Machine Learning Techniques, and Multi-objective Particle Swarm Optimization

Abstract The purpose of this paper is to investigate the effect annealing process at 1000 °C on machining parameters using contemporary techniques such as principal component analysis (PCA), hyper-parameter optimization by Optuna, multi-objective particle swarm optimization, and theoretical validation machine learning method. Results after show that there will be a reduction in surface roughness values 19.61%, tool wear 6.3%, an increase metal removal rate 14.98%. PCA results feed more significant than depth cut speed. higher value composite primary represent optimal factors speed 80, 0.2 0.3, components like ( Ψ 1 = 64.5), 2 22.3) 3 13.2). Hyper-parameter represents directly proportional roughness, wear, rate, while are inversely proportional. history plot steady, prediction accuracy 96.96%. Machine employed through Python language Google Colab. estimated from decision tree method for predictions AdaBoost algorithm match well with actual values. As per MOPSO (multi-objective optimization), predicted responses follows; (2.5 μm, 100, 02, 0.45), (0.31 mm, 40, 0.40, 0.60), MRR (material rate) (5145 mm /min, 0.4, 0.15). validated experimentation, small variations varied 1.56%, 6.8%, 2.57%.