From in-situ monitoring toward high-throughput process control: cost-driven decision-making framework for laser-based additive manufacturing

Abstract

Abstract Additive manufactured (AM) parts are subject to low repeatability compared to their traditional counterparts. The complex Process-Structure-Property relationship that governs the laser-based AM processes calls for advanced analytics approaches for quality control. Tremendous efforts have been dedicated to the in-situ monitoring of AM processes by leveraging the thermal history during fabrication. Melt pool image is regarded as one of the most informative process signatures for real-time porosity detection. Attempting to control/correct all microstructure defects during the AM fabrication may significantly reduce the process throughput, which has been a bottleneck of the AM technology for its wider industrial adoption. Without distinguishing different types of defects from one another, and without formally characterizing the cost/impact of microstructural defects on part property, an efficient in-process control strategy cannot be obtained. In this paper, a cost-driven decision-making framework is proposed to formulate costs of the spatial distribution of microstructural defects and the corresponding control actions, based on in-situ melt pool images. A case study based on thin wall fabrication using a Laser Engineered Net Shaping process is used to illustrate the effectiveness in both classification accuracy and misclassification cost. This work is expected to lay a theoretical foundation for the development of an efficient in-process control strategy, which aims to improve the mechanical properties of fabricated part while maintaining high process throughput.