Multiscale basis modeling of 3D melt-pool morphological variations for manufacturing process monitoring

Abstract

Laser powder bed fusion is a key technology of additive manufacturing that enables the fabrication of metal parts with complex geometry through a multilayer process. Despite its great promise in design flexibility, wide application of this technology is hindered by a lack of quality assurance in fabrication parts. Melt-pool morphological characteristics are eminent indicators for manufacturing process stability and part quality. However, existing studies on melt-pool morphology focused on key geometric properties (e.g., length, width, size) extracted from melt-pool images for characterizing its variations, and tend to overlook 3D morphological variations of melt pools and ejected spatters. In this paper, we develop a multiscale modeling framework to represent, characterize, and monitor melt-pool variations through 3D morphological features, including multiscale basis function modeling of 3D melt-pool morphology and an iterative search of predominant components for sparse representation of morphological variations in melt-pool images. A case study with real-world experimental data shows that the proposed framework effectively characterizes 3D melt-pool morphological variations and provides salient features for tracking process variations, predicting melt-pool sizes, and detecting spatters. This framework is generally flexible for a wide variety of additive manufacturing (AM)applications such as melt-pool simulation, process monitoring, and anomaly detection.