Detection of material extrusion in-process failures via deep learning

Abstract

Additive manufacturing (AM) is evolving rapidly and this trend is creating a number of growth opportunities for several industries. Recent studies on AM have focused mainly on developing new machines and materials, with only a limited number of studies on the troubleshooting, maintenance, and problem-solving aspects of AM processes. Deep learning (DL) is an emerging machine learning (ML) type that has widely been used in several research studies. This research team believes that applying DL can help make AM processes smoother and make AM-printed objects more accurate. In this research, a new DL application is developed and implemented to minimize the material consumption of a failed print. The material used in this research is polylactic acid (PLA) and the DL method is the convolutional neural network (CNN). This study reports the nature of this newly developed DL application and the relationships between various algorithm parameters and the accuracy of the algorithm.