Beyond parabolic weld bead models: AI-based 3D reconstruction of weld beads under transient conditions in wire-arc additive manufacturing
This study presents AIBead, an AI framework designed to predict and reconstruct a 3D geometry of weld beads in wire-arc additive manufacturing, especially under variable conditions where standard parabolic models are inadequate. It tackles the challenge of precise weld bead geometry prediction, offering an improvement by accurately depicting asymmetrical shapes along curved paths, thereby refining automated process planning and parameter optimization.
Details
Type of Work: Scientific Publication
Main Author: Jan Petrik
Affiliation: ETH Zurich
Co-Authors: Benjamin Sydow, Markus Bambach
Date: 9th December 2021
Journal: Journal of Materials Processing Technology
Online: ScienceDirect
Gallery
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The AIBead architecture employs MLP or GRU to model the weld bead's center line with four parameters: plane angle, Euler distance, radius, and welding speed. It predicts the 2D point cloud of the weld bead, compares it to actual data using MSE loss for optimization, and enables 3D model reconstruction from these point clouds. -
Analysis of experimental data examines the geometric characteristics of weld bead cross-sections through centerline cuts, visualizing features such as area, toe angles, height, width, and circumference. It also graphically represents variations in these properties along the centerline, noting significant changes across the radius. -
3D reconstruction of a weld bead based on the deposition path. This was done using the developed machine learning architecture called AIBead.