Correlation analysis between warpage deformation and optical sensing signals during LPBF process

Abstract Laser Powder Bed Fusion (LPBF) technology exhibits the potential to revolutionize traditional manufacturing processes. However, manufacturing repeatability and quality assurance have yet to be optimized. A pivotal obstacle is the lack of effective real-time monitoring, which is crucial for reducing defect incidence and maintaining melt pool stability. Furthermore, a comprehensive analysis that combines real-time monitoring signals, process parameters, and part quality is essential for effective monitoring. Therefore, a suite of test experiments was conducted using standard overhanging specimens, varying support column dimensions and scanning strategies. In-situ monitoring techniques were employed to provide an in-depth investigation of the interactions between optical sensing signals, specimen warpage deformation and process parameters. And based on the correlation analysis among the three, this paper developed a method to qualitatively predict specimen warpage. Finally, this study validated and evaluated the proposed warpage deformation classification method using multiple machine learning models. The primary findings of the analysis indicate that the light intensity signal from the initial overhanging layer captures critical process information that is essential for specimen fabrication and significantly affects the light intensity signal of subsequent layers. The overall light intensity signals of the overhanging layers exhibit a trend of increasing initially and then stabilizing as the number of sintered layers grows. From the smart island scanning group to the normal island scanning group, and subsequently to the zigzag scanning group, a consistent increase in warpage deformation is observed. An inverse correlation exists between the dimensions of support columns and the warpage deformation of overhanging specimens in zigzag scanning.