Machine learning and molecular dynamics simulations aided insights into condensate ring formation in laser spot welding

Abstract

Abstract Condensate ring formation can be used as a benchmark in welding processes to assess the efficiency and quality of the weld. Condensate formation is critical as the resulting condensate settles into the powder thereby altering the quality of unconsolidated powder. This study investigates the intricate relationship between alloy composition, vapor pressure, and condensate ring thickness as seen in a two-dimensional micrograph. To study the process, laser spot welding was performed on 9 different alloys, and the inner spot weld diameter along with the condensate ring formation was studied. Leveraging machine learning models, experimental observations, and molecular dynamics simulations, we explore the fundamental factors governing condensate ring formation. The models, adept at predicting weld spot diameter and condensate ring thickness, identify laser power as a primary determinant for weld spot diameter followed by physical properties like hardness and density. Conversely, for condensate ring thickness, vapor pressure and melting point descriptors consistently emerge as paramount, as validated across all models. Molecular dynamics simulations on Ni-Cr alloys elucidate the vaporization dynamics, confirming the role of vapor pressure in governing surface vaporization. Our findings underscore the pivotal influence of vapor pressure and melting point descriptors in condensate ring formation. The convergence of machine learning predictions and simulation insights elucidates the dominance of these descriptors, offering crucial insights into alloy design strategies to minimize condensate ring formation in laser welding processes.

Keywords

• Laser spot welding
• Laser spatter
• Condensate ring
• Machine learning
• Molecular dynamics