Abstract

The present study highlights a surface roughness (Ra) prediction model that considers a subset of elements involved in the milling process that is related to the machined piece, the tool, and characteristics of the machine tool. Due to the excellent results it produces in terms of surface finish and financial advantages, high-speed machining (HSP) continues to be a technique of great interest in the production of metal parts. The industry has a propensity to use data management and analysis methods to generate data that can be used to improve the results of machining for Ra. In this work, we use real training data and we have also obtained a graphical representation of knowledge using classic decision trees to complement the results obtained by GBT, in this way the joint result provides greater graphic expressivity regarding conditional influences and the values of the predictor variables on the class labels than for example Bayesian networks. The results are contrasted with prior experiences that use the same experimental design but with different soft-computing techniques and they are also contrasted with the results of similar previous works.