Quality Control and Defect Detection for Wire Arc Additive Manufacturing Using 3D Scanning and Point Cloud Analysis

Abstract

Wire Are Additive Manufacturing (WAAM) is a technique used for 3D printing or repairing metal parts by progressively layering metal until the desired 3D structure is fully formed. This research focuses on developing realtime, in-process monitoring and quality control techniques to evaluate multiple quality indicators continuously throughout the WAAM process. It further explores methods for detecting and classifying defects, such as porosity, cracks, lack of fusion, and geometric deviations, that may occur during WAAM. To improve defect detection and streamline processes, predictive models are built using machine learning or physics-based simulations. These models analyze shifts in process parameters to anticipate potential flaws, empowering teams to make timely, data-driven corrections. 3D scanning is employed to capture the physical form of manufactured parts as point cloud data, which is processed on a computer using Visual Studio code. A complete 360° scan takes approximately two minutes, with the data processing requiring only about five seconds. The resulting point cloud data is then visualized and processed where geometrical attributes such as curvature and volume density are calculated and represented as distribution curves, aiding in clarity and precision during analysis. By examining these visualized parts, specific defects can be identified and categorized based on distinctive characteristics. Furthermore, Gaussian distribution curves are utilized to calculate the percentage of defective areas, offering quantitative insights for effective quality control and helping ensure that the final product meets desired specifications.