Automatic parameter tuning in aluminum extrusion based on genetic programming

Abstract

This work applies artificial intelligence in the aluminum extrusion process for automatic setting the ram speed of a machine according to the requirements of the industry. The automatic parameter tuning system computes the ram speed with the equation created by genetic programming (GP). In model evaluation, MAE and MAPE are used to measure a predictive performance of the models. In addition to GP, linear and polynomial regression are used to generate the automatic parameter tuning model for comparing a performance with GP. The experimental results on the test set show that GP performs the best in predictive performance with 0.130 of MAE and 4.22% of MAPE . Finally, the GP model has been developed as a software to calculate the ram speed and display it on a screen. This system will help users who are not proficient in aluminum extrusion or new users to have better control of production.