Optimization of Multi-Parameter Processes for Surface Quality

Abstract

This work offers a thorough method for improving surface quality via multi-parameter process optimization. The goal of the study is to create a cohesive framework that combines computational modeling, experimental validation, and sophisticated optimization approaches to enhance surface finish in a variety of manufacturing processes. To find the best process parameters and how they interact, the suggested methodology combines statistical analysis, machine learning algorithms, and met heuristic optimization techniques. Including mathematical models that can forecast both the high quality of a precisely machined surface and the high productivity of the process in WEDM of tool steels, this article outlines a suggested method for multiparametric optimization of the quality of machined surfaces. Using the full DoE factorial design method, which contains four technological parameters, the experimental study was conducted.). Metal Matrix based on aluminum AMMC is a material that is in high demand in the automotive, aircraft, sports, marine, and defense industries due to its composite material qualities, which include light weight, high strength, and resistance to corrosion. Nevertheless, the Aluminum Matrix's abrasive reinforcements result in quick tool wear or failure, higher machining costs, longer production times, and lower-quality machined parts. Therefore, the best method to get around these issues is Wire Electrical Discharge Machining.