Multiconstraint quality-probability graph for quality monitoring of laser directed energy deposition manufacturing process

Quality monitoring of the laser directed energy deposition (LDED) manufacturing process by machine vision is essential to improve the reliability and economy of the LDED manufactured parts. For monitoring algorithms, graph-based semi-supervised learning can effectively learn supervised information from labelled data without the requirement of large amounts of manually labelled data. However, traditional graph algorithm for LDED quality monitoring is limited by insufficient use of label supervised information, inadequate consideration of the imbalance data, and lack of physically meaningful explanations and constraints on the propagation process for different types of defects. In this regard, a multiconstraint quality-probability graph (MCQPG) is proposed to monitor the quality during the LDED manufacturing process. MCQPG converts the extracted multifeatures into probability distributions, finds feature sets with similar distributions to the supervised information based on quality level standards, and uses multiconstraints to satisfy few labelled feature data, imbalance data distribution and physical consistency. For physical consistency, a nonlinear defect model is developed for crack and porosity defects, and a novel defect-guided objective prediction function is proposed, resulting in the construction of a quality level standard guided physical consistency constraint term. Experimental studies on several well-known and commonly used monitoring algorithms demonstrate that the proposed MCQPG algorithm achieves 0.96 on all four evaluation metrics (accuracy, precision, recall and F1 score), validating the effectiveness of MCQPG for LDED quality monitoring.