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

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Yayımlandı:IISE Annual Conference. Proceedings (2025), p. 1-7
Yazar: Yang, Ian
Diğer Yazarlar: Kong, Zhenyu James, Kosmal, Tadek, Williams, Christopher
Baskı/Yayın Bilgisi:
Institute of Industrial and Systems Engineers (IISE)
Konular:
Machine learning
Wire
Data processing
Cooling
Quality control
Defects
Scanners
Prediction models
Lasers
Residual stress
Neural networks
Sensors
Three dimensional models
Normal distribution
3-D printers
Three dimensional printing
Visual programming languages
Manufacturing
Additive manufacturing
Process parameters
Online Erişim:Citation/Abstract
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Özet: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.
DOI:10.21872/2025IISE_6682
Kaynak:Science Database