Development of Algorithms for Predicting Roughness in Concave and Convex Machining for M201-M238 and M303 Martensitic Steels without Heat Treatment

This project addresses the need to improve quality and repeatability in surface finishing processes for the manufacturing of injection molds and dies, a key sector for the Ecuadorian economy. Currently, the parameterization of surface finishes on complex curves heavily relies on operator experience, resulting in qualitative evaluations and variable production times. The main objective is to establish validated parameters to increase quality indices and reduce response time in 3-axis machining. The methodology combines Applied and Experimental Research, utilizing available machine tools to manipulate strategies and parameters such as cutting speed and feed rate on convex surfaces. Design of Experiments (DOE) based on the Taguchi methodology will be employed, followed by Analysis of Variance (ANOVA), to obtain statistical behavioral patterns. The final outcome will be algorithms capable of predicting roughness, which will then be validated through subsequent trials. The compiled information will be used for thesis work and scientific publications, providing local industry with robust technical data to optimize their machining processes.<br/><br/><b>Goal</b>: <br/>To develop predictive algorithms for the surface roughness resulting from concave and convex machining applied to martensitic steels M201, M238, and M303, without prior heat treatment.<br/><br/><b>Research lines</b>: <br/>Modeling and simulation applied to industry