Beat the machine (learning): metal additive manufacturing and closed loop control

Abstract

Abstract 3D printing (additive manufacturing) is an emerging technology with the ability to make complex, free-form shapes from materials including plastics, metals and ceramics. While additive manufacturing has many advantages over more traditional processes, it can be difficult to control, which can then lead to defects in the finished part. Closed-loop control is a key part of most modern manufacturing and household processes, improving efficiency and reducing variation. Machine learning is an extension of this, where the controller learns how changes in the input variables affect the output. Here we provide an overview of the different types of metal additive manufacturing processes, and their relative strengths and weaknesses. We also describe how closed-loop control and thermal cameras are being used to improve these processes. Finally, we provide a link to a free-to-download app which allows students to control their own simulation of an additive manufacturing build, and see first-hand the need for control algorithms. Pseudo-code is provided in an appendix to help students who wish to take this further by building their own control algorithms.