The first step towards intelligent wire arc additive manufacturing: An automatic bead modelling system using machine learning through industrial information integration

Abstract

Wire Arc Additive Manufacturing (WAAM) has revolutionized the manufacturing paradigm for fabricating medium to large scale metallic parts featuring high buy-to-fly ratios such as aerospace components. As a promising technology for the manufacturing industry, it is necessary to develop an automated WAAM system with high efficiency and low labour cost. Generally, to achieve a fully intelligent WAAM system, the first step is to develop an intelligent weld bead modelling system which is able to provide users with appropriate welding parameters in terms of producing components with high accuracy. Knowledge from many disciplines, such as computer science, material engineering, mechanical engineering, and industrial system engineering, is advantageous to develop such an automated system. Thus, an intelligent bead modelling system was developed by integrating a number of industrial sectors in this study. The bead modelling system includes three critical modules, including data generation module, model creation module, and welding parameter generation module. It is worth mentioning that a novel algorithm using Support Vector Machines (SVM) was proposed for creating the model with a high level of accuracy. Optimal combinations of wire feed rate and travel speed under various temperatures were generated accordingly. The experiment results demonstrated that the system can significantly improve product quality and reduce manufacturing costs, including raw material usage and manual labour.