Research on welding trajectory planning for aviation Invar alloy molds based on density-adaptive filtering and layered trajectory optimization of welding point clouds

In this study, discontinuous welding trajectory extraction and significant path fitting errors due to abrupt curvature variations, irregular boundaries, and fluctuating gaps in weld seams of aviation Invar alloy molds are addressed, and a welding trajectory planning based on point cloud density-adaptive filtering and layered trajectory optimization, continuous extraction, and stable planning of weld seam trajectories for aviation Invar alloy molds is proposed. Analyzing the density variation characteristics of weld seam point clouds under abrupt curvature changes and irregular contours, this study develops a multi-scale density-adaptive filtering model with curvature-driven dynamic regulation functions to achieve precise feature extraction in regions of curvature mutation and boundary abnormality. Investigating transition behaviors of welding paths under multi-layer gap fluctuations, this work extracts spatial hierarchical weld features and establishes segmented trajectory fitting optimization equations, ensuring accurate path modeling and directional consistency in gap-variation zones. The experimental results show that the MeanDist (Seam Visibility Mean Distance) was reduced to 0.5712, the CurvatureStd (Standard Deviation of Curvature) was optimized to 0.054, and the number of DCC (Direction Change Count) is only 2, which effectively improved the fitting accuracy and path stability of complex weld joint trajectories. This work provides a systematic path extraction method for aviation mold welding trajectories and strong support for the automated implementation of complex component welding operations.