Experimental validation of machine learning models for prediction of the thickness distribution of directionally rolled copper strips under scaling law

Abstract

Predicting the results of multi-stage deep drawing in determining the thickness distribution and thinning of the workpiece are measures that will help reduce production costs by saving materials and production time. Machine Learning (ML) technique is a promising method for predicting the thickness distribution (TD) of drawn copper metal. Macro and micro products are currently used in sensitive electronic and structural applications. In this study, ML defines the relationship between the scaled thickness and the respective number of stages governed by the scaling law. It includes the development of Artificial Neural Network (ANN) tools based on machine learning to model the relationship between thickness by scaling law and stages based on multi-stage deep-drawn cups. TD is a measure of consistency. The TD is measured from the initial blank of 1500 mm long, 750 mm wide, and 6 mm thick Cu strip, reduced by 50% in nine successive steps until a final thickness of 0.1875 mm. Two ANN models used for TD prediction are Bayesian regularization (BR) and Levenberg-Marquardt (LM) algorithms. A trained machine learning model can successfully predict and verify the unseen data of 1.5 and 0.38 mm TD. ANN is used to predict the Finite Element Analysis (FEA) results and confirm them through the experimental results. The developed model can predict the TD of the multi-stage cup with the die design parameters. The difference between the TD predicted value and the measured value is based on the simulation results of multi-stage cups using the finite element method. When the predicted and measured TD, the difference in cup drawing depth is 0.5%–2.0%. The results show that the LM model is suitable for predicting the TD of formed copper cups following the scaling law.