Rapid planning of deposition strategies for wire arc additive manufacturing: from target-geometry design to dimensional-accuracy control

The accuracy and efficiency of wire arc additive manufacturing (WAAM) depend on the geometrically consistent translation of a target geometry into multi-layer multi-pass bead layouts and robot-executable toolpaths under prescribed process parameters. To bridge this design-to-deposition gap, this study introduces a target-geometry-driven framework that integrates four capabilities: weld bead geometry prediction, primitive-based deposition geometry modelling, layout parameter optimisation, and toolpath generation. The weighted mean of vectors (INFO) algorithm was integrated with a cascade-forward backpropagation (CFBP) neural network to predict bead height, width, and cross-sectional area, yielding RMSE values of 0.08914 mm, 0.1160 mm, and 0.3874 mm², respectively. Based on these predictions, a multi-layer multi-pass deposition geometry model incorporating constraint-state decomposition and an overlap-ratio criterion was developed to rapidly determine the number of layers, beads per layer, inter-bead overlap relationships, and deposition toolpaths. Validation experiments on four component types yielded height and width forming errors relative to the target geometry of -4.3527% to 4.7484% and -6.1409% to 7.9370%, respectively. The manufacturing execution errors relative to model-predicted dimensions were -3.9984% to 5.1799% and -4.0145% to 4.6739%. The proposed framework can rapidly generate deposition layouts and toolpaths from prescribed process parameters and specified target geometries within the investigated process, material, and geometric ranges.