A sequential modelling approach to determine process capability space during laser welding of high-strength Aluminium alloys

Abstract

Remote laser welding (RLW) technology has become a prominent joining technology in automotive industries, offering high production throughput and cost-effectiveness. Recent advancements in RLW processes such as beam oscillation have led to an increased number of input process parameters, enabling precise control over the heat input to weld metallic materials. A critical necessity in laser welding entails selecting robust process parameters that satisfy all weld quality indicators or key performance indicators (KPIs) during two stages: production stage (often implemented as robotic welding); and repair/rework stage (implemented as cobotic/manual welding to identify process parameters for weld defects) as addressing these factors in both stages is necessary to satisfy near-zero-defect strategy for some e-mobility products.. This research presents a comprehensive methodology that encompasses the following key elements: (i) the development of physics-based simulations to establish the correlation between KPIs and process parameters; (ii) the integration of a sequential modelling approach that strikes a balance between accuracy and computation time to survey the parameter space; and (iii) development of the process capability space for the quick selection of robust process parameters.

Three physical phenomena are considered in the development of numerical models, which are (i) heat transfer, (ii) fluid flow and (iii) material diffusion to investigate the effect of process parameters on the weld thermal cycle, solidification parameters and solute intermixing layer during laser welding of dissimilar high-strength aluminium alloys. The governing physical phenomena are decoupled sequentially, and KPIs are estimated based on the governing phenomena. At each step, the process capability space is defined over the parameters space based on the constraints specific to the current physical phenomena. The process capability space is determined by the constraints based on the KPIs. The process capability space provides the initial combination of process parameter space during the early design stage, which satisfies all the KPIs, thus decreasing the number of experiments. The proposed methodology provides a unique capability to (i) simulate the effect of process variation as generated by the manufacturing process, (ii) model quality requirements with multiple and coupled quality requirements, and (iii) optimise process parameters under competing quality requirements.