Automated Quality and Process Control for Additive Manufacturing using Deep Convolutional Neural Networks

Abstract

Additive Manufacturing (AM) is a crucial component of the smart manufacturing industry. In this paper, we propose an automated quality grading system for the fused deposition modeling (FDM) process as one of the major AM processes using a developed real-time deep convolutional neural network (CNN) model. The CNN model is trained offline using the images of the internal and surface defects in the layer-by-layer deposition of materials and tested online by studying the performance of detecting and grading the failure in AM process at different extruder speeds and temperatures. The model demonstrates an accuracy of 94% and specificity of 96%, as well as above 75% in measures of the F-score, the sensitivity, and the precision for classifying the quality of the AM process in five grades in real-time. The high-performance of the model could not be achieved with the values usually used for printing temperature and printing speed, only in addition with much higher values. The proposed online model adds an automated, consistent, and non-contact quality control signal to the AM process. The quality monitoring signal can also be used by the AM machine to stop the AM process and eliminate the sophisticated inspection of the printed parts for internal defects. The proposed quality control model ensures reliable parts with fewer quality hiccups while improving performance in time and material consumption.