Nguyen HG, Habiboglu R, Franke J (2022)
Publication Type: Journal article
Publication year: 2022
Book Volume: 107
Pages Range: 1263-1268
DOI: 10.1016/j.procir.2022.05.142
The wiring harness manufacturing follows a high-variant manufacturing philosophy with a high degree of manual labor to produce customized products. Therefore, the process step of optical inspection is crucial to monitor, assess, and proactively safeguard the quality of produced wiring harnesses. To automate the optical inspection, which is currently manually conducted by operators, deep learning has become a powerful algorithm outperforming traditional computer vision approaches. Deep learning-based automated optical inspection systems can robustly and reliably detect rigid and deformable components, such as connectors, clips, single wires, and wire bundles. However, the bottleneck for scalable deep learning solutions in the industrial environment and high deep learning model performance is the database for model training and optimization. To address this research gap, we propose a deep learning-based data processing pipeline for automated optical inspection of wiring harnesses using real and synthetically generated point clouds. This paper outlines the process of real and synthetic data generation and evaluates the potential of synthetic data to enrich real data for model training. The data processing pipeline is implemented and experimental findings are generated to deduct important parameters for synthetic data generation and deep learning model training which impact model performance.
APA:
Nguyen, H.G., Habiboglu, R., & Franke, J. (2022). Enabling deep learning using synthetic data: A case study for the automotive wiring harness manufacturing. Procedia CIRP, 107, 1263-1268. https://dx.doi.org/10.1016/j.procir.2022.05.142
MLA:
Nguyen, Huong Giang, Resul Habiboglu, and Jörg Franke. "Enabling deep learning using synthetic data: A case study for the automotive wiring harness manufacturing." Procedia CIRP 107 (2022): 1263-1268.
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