Layer-Wise Profile Monitoring of Laser-Based Additive Manufacturing

Abstract

Additive manufacturing (AM) is a novel fabrication technique capable of producing highly complex parts. Nevertheless, a major challenge is improving the quality of fabricated parts. While there are a number of ways of approaching this problem, developing data-driven methods that use AM process signatures to identify these part anomalies can be rapidly applied to improve overall part quality during build. The objective of this study is to build a new layer-wise process signature model to create the thermal-microstructure relationship. In this study, we derive novel key process signatures for each layer (from melt pool thermal images), which are reduced using multilinear principal component analysis (MPCA) and are directly correlated with layer-wise quality of the part. Using these key process signatures, a Gaussian SVM classifier model is trained to detect the existence of anomalies inside a layer. The proposed models are validated through a case study of real-world direct laser deposition experiment where the layer-wise quality of the part is predicted on the fly. The accuracy of the predictions is calculated using three measures (recall, precision, and f-score), showing reasonable success of the proposed methodology in predicting layer-wise quality. The ability to predict layer-wise quality enables process correction to eliminate anomalies and to ultimately improve the quality of the fabricated part.