Machine Learning to Detect Anomalies in the Welding Process to Support Additive Manufacturing

Abstract: Additive Manufacturing (AM) is a fast-growing technology in manufacturing industries. Applications of AM are spread across a wide range of fields. The aerospace industry is one of the industries that use AM because of its ability to produce light-weighted components and design freedom. Since the aerospace industry is conservative, quality control and quality assurance are essential. The quality of the welding is one of the factors that determine the quality of the AM components, hence, detecting faults in the welding is crucial. In this thesis, an automated system for detecting the faults in the welding process is presented. For this, three methods are proposed to find the anomalies in the process. The process videos that contain weld melt-pool behaviour are used in the methods. The three methods are 1) Autoencoder method, 2) Variational Autoencoder method, and 3) Image Classification method. Methods 1 and 2 are implemented using Convolutional-Long Short Term Memory (LSTM) networks to capture anomalies that occur over a span of time. For this, instead of a single image, a sequence of images is used as input to track abnormal behaviour by identifying the dependencies among the images. The method training to detect anomalies is unsupervised. Method 3 is implemented using Convolutional Neural Networks, and it takes a single image as input and predicts the process image as stable or unstable. The method learning is supervised. The results show that among the three models, the Variational Autoencoder model performed best in our case for detecting the anomalies. In addition, it is observed that in methods 1 and 2, the sequence length and frames retrieved per second from process videos has effect on model performance. Furthermore, it is observed that considering the time dependencies in our case is very beneficial as the difference between the anomalous and the non anomalous process is very small