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This document presents a case study on optimizing machining parameters for AISI H13 hardened die steel using a hybrid approach that integrates Finite Element Method (FEM), Artificial Neural Networks (ANN), and Multi-Objective Optimization (MOO). The study demonstrates the effectiveness of this methodology in accurately predicting temperature and strain while minimizing the need for experimental data, thus enhancing efficiency and reducing costs in manufacturing processes. The results indicate strong validat

Author

amishav2004

Language

EN