Parametric analysis of dot-by-dot wire arc additive manufacturing for monitoring assessment and process parameters prediction

Abstract Dot-by-dot (DBD) is an emerging novel deposition strategy for wire arc additive manufacturing (WAAM) that offers unique advantages, including high material efficiency and suitability for producing complex lattice structures, which are unattainable with conventional WAAM. DBD-WAAM is characterized by the discrete and intermittent deposition of molten droplets (dots) stacked to form structural elements, resulting in frequent arc starting and stopping phases. However, no study has explicitly investigated the relationship between commanded (i.e., input) process parameters and the resulting effective parameters (i.e., output parameters). They are critical for process programming, reproducibility, sustainability prediction and assessment, and providing reliable data to support monitoring, deep learning, and artificial intelligence. This paper examines the individual and synergic effects of process parameters on low-carbon steel DBD-WAAM, providing novel insights into the discrepancies between commanded and effective process parameters and their impact on geometry. The findings emphasize the importance of relying on effective parameters for evaluating sustainability indicators and implementing a process monitoring solution. Moreover, a new regression-based model was developed to accurately predict process outcomes, improving the reliability of automated AM deposition programming. Results demonstrate the model’s good accuracy. Despite the inherently rapid nature of the DBD-WAAM process, the findings indicate that both the diameter and layer height are affected by variations in wire feed speed and deposition time. This study contributes to a deeper understanding of the DBD-WAAM process and provides valuable guidelines for process monitoring and sustainability assessment, including the calculation of specific energy consumption.