A Novel Approach to Multi-objective Optimization in Machining
- 26 October 2025
- book chapter
- Published by Springer Nature
- p. 621-629
Abstract
No abstract availableKeywords
This publication has 10 references indexed in Scilit:
- Experimental validation of a semi-active fuzzy control strategy based on deep reinforcement learning for a piezoelectric smart isolation systemEngineering Applications of Artificial Intelligence, 2025
- Employing deep reinforcement learning for machining process planning: An improved frameworkJournal of Manufacturing Systems, 2024
- Machining parameter optimization for a batch milling system using multi-task deep reinforcement learningJournal of Manufacturing Systems, 2024
- Optimization of sustainable milling of SKD11 steel under minimum quantity lubricationProceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 2022
- Investigation and Optimization of Surface Roughness and Material Removal Rate in Face Finishing Milling of Ti-6Al-4V Under MQL Condition2022
- A practical guide to multi-objective reinforcement learning and planningAutonomous Agents and Multi-Agent Systems, 2022
- Cutting Parameter Optimization in Finishing Milling of Ti-6Al-4V Titanium Alloy under MQL Condition using TOPSIS and ANOVA AnalysisEngineering, Technology & Applied Science Research, 2021
- Deep Reinforcement Learning for Multiobjective OptimizationIEEE Transactions on Cybernetics, 2020
- Machining of Titanium Alloys2014
- Optimization Modelling2007