Development of Algorithms for Roughness Prediction in Electrical Discharge Machining, Concave and Convex 3-Axis Machining on Cold-Work and Hot-Work Steels with a Hardness Range of 50 to 65 HRC

This research project, led by the Universidad Politécnica Salesiana, focuses on optimizing manufacturing processes for the mold and die industry in Ecuador. Given the increasing demand for precision in machining hardened steels (50-65 HRC), the study addresses the lack of local technical data for heat-treated materials. The research team develops predictive algorithms based on statistical analysis (Taguchi and ANOVA) to estimate surface roughness in concave and convex geometries. The methodology integrates laboratory testing, mechanical characterization, and CAD/CAM/CAE simulations to validate cutting parameters. By providing a technical guide for machine calibration and tool selection, the project aims to reduce production times and operating costs for local SMEs. Furthermore, the project has a strong academic component, involving engineering students in conducting tests and generating scientific publications, thereby strengthening the link between academia and the metalworking industrial sector.<br/><br/><b>Goal</b>: <br/>Develop algorithms to predict surface roughness in electrical discharge machining and 3-axis machining processes for heat-treated steels. The goal is to optimize surface quality in parts with hardness ranging from 50 to 65 HRC.<br/><br/><b>Research lines</b>: <br/>New materials and transformation processes