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The text comprehensively focuses on the concepts, implementation, and application of evolutionary algorithms for predicting, modeling, and optimizing the various material removal processes from their origin to the current.
Experimental Investigation of Surface Roughness for Turning of UD-GFRP Composite Using PSO, GSA, and PSOGSA Techniques
Multi-response Optimization During High-speed Drilling of Composite Laminate Using Grey Entropy Fuzzy (GEF) and Entropy-Based Weight Integrated Multi-Variate Loss Function
Implementation of Modern Meta-Heuristic Algorithms for Optimizing Machinability in Dry CNC Finish-Turning of AISI H13 Die Steel Under Annealed and Hardened States
Multi-Response Optimization in Turning of UD-GFRP Composites using Weighted Principal Component Analysis (WPCA)
Processes Parameters Optimization on Surface Roughness in Turning of E-glass UD-GFRP Composites Using Flower Pollination Algorithm (FPA)
Application of ANN and Taguchi Technique for Material Removal Rate by Abrasive Jet Machining with Special Abrasive Materials
Investigation of MRR in Face Turning Unidirectional GFRP Composites by Using Multiple Regression Methodology and an Artificial Neural Network
Optimization of CNC Milling Parameters for Al-CNT Composites Using an Entropy-Based Neutrosophic Grey Relational TOPSIS Method
Experimental Investigation of EDM Potential to Machine AISI 202 Using a Copper-Alloy Electrode and Its Modelling by an Artificial Neural Network
Prediction and Neural Modeling of Material Removal Rate in Electrochemical Machining of Nimonic-263 Alloy
Optimization of End Milling Process Variables Using a Multi-Objective Genetic Algorithm