Extraction and evaluation of melt pool, plume and spatter information for powder-bed fusion AM process monitoring

With the continuous development of additive manufacturing technique, the issue on built quality has caught increasing attentions. To improve the quality of built parts, the process monitoring and control has been empha-sized as a promising solution. Despite a large number of studies on the development of sensors and instrumentations, the investigation on statistical analysis, modelling and automatic anomalies detection is still at an infant stage. To advance the related research, the intelligent classification methods, support vector machines (SVM) and convolutional neural network (CNN), were proposed for quality level identification in this work A vision system with high speed camera was used for process images acquisition. The features of different objects including melt pool, plume and spatter were extracted based on the AM process understanding. The corresponding feature vectors were used as the input for the SVM classification. The results indicated the information from different objects is sensitive to different types of quality anomalies. Moreover, the combination of features from these three objects can significantly improve the classification accuracy to 90.1%. Additionally, the comparison between SVM and CNN was also conducted, the high accuracy of 92.7% for the CNN model demonstrated that it is a promising method for quality level identification by using the vision system. (C) 2018 Published by Elsevier Ltd.