Conventional Methods and AI models for Solving an Industrial Problem

摘要

This study presents a research that identifies and applies unsupervised connectionist models in conjunction with modelling systems, in order to determine optimal conditions to perform laser milling of metallic components. This industrial problem is defined by a data set relayed through sensors situated on a laser milling centre that is a machine-tool used to manufacture high value micro-molds and micro-dies. The results of the study and the application of the connectionist architectures allow the identification, in a second phase, of a model for the milling machine process based on low-order models such as Black Box, which are capable of approximating the optimal form of the model. Finally, it is shown that the most appropriate model to control these industrial tasks is the Box-Jenkins algorithm, which calculates the function of a linear system from its input and output samples.