Development of a Machine Learning Based Fast Running Model to Determine Rapidly the Process Conditions in Drawing Process

Abstract

This study proposes a fast running model that interconnects input and output data for a single-pass cold bar drawing process through the use of Artificial Neural Network (ANN) and automatically generated a large volume of elastic-plastic finite element (FE) analysis results. The prediction accuracy of the FE analysis was verified by comparing the FE analysis with measurements from a drawing experiment. A Python-based script that automatically controls ABAQUS was coded to sequentially produce output data that varies according to the input data, which is a combination of 18 grades of steel and 1,000 process conditions. The ANN was trained using input and output data, and then a nine-dimensional fast running model was developed. The fast running model predicted the values of output variables (drawing force, strain at the center, strain on the surface, accumulated damage at the center, contact pressure, and the fracture (or non-fracture) of the material) in 0.1 second no matter how the mechanical properties of the steels and process conditions change. With this fast running model, engineers in the drawing industry can easily determine or modify the process conditions to improve productivity and product quality even when a grade of steel that has never been employed before is drawn.