Using Unsupervised Learning for Regulating Deposition Speed During Robotic Wire Arc Additive Manufacturing

Robotic Wire Arc Additive Manufacturing (WAAM) is the layer-by-layer deposition of molten metal to build a three-dimensional part. In this process, the fed metal wire is melted using an electric arc as a heat source. The process is sensitive to the arc conditions, such as arc length. While building WAAM parts, the metal beads overlap at corners causing material accumulation. Material accumulation is undesirable as it leads to uneven build height and process failures caused by arc length variation. This paper introduces a deposition speed regulation scheme to avoid the corner accumulation problem and build parts with uniform build height. The regulated speed has a complex relationship with the corner angle, bead geometry, and molten metal dynamics. So we need to train a model that can predict suitable speed regulations for corner angles encountered while building the part. We develop an unsupervised learning technique to characterize the uniformity of the bead profile of a WAAM built layer and check for anomalous bead profiles. We train a model using these results that can predict suitable speed regulation parameters for different corner angles. We test this model by building a WAAM part using our speed regulation scheme and validate if the built part has uniform build height and reduced corner defects.