Deep learning-based augmented reality work instruction assistance system for complex manual assembly

Li, Wang; Xu, Aibo; Wei, Ming; Zuo, Wei; Li, Runsheng

doi:10.1016/j.jmsy.2024.02.009

Journal of Manufacturing Systems

2024

DOI: 10.1016/j.jmsy.2024.02.009

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Deep learning-based augmented reality work instruction assistance system for complex manual assembly

Wang Li,

Aibo Xu,

Ming Wei

et al.

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2024

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Cited by 2 publications

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Enhancing metal additive manufacturing training with the advanced vision language model: A pathway to immersive augmented reality training for non-experts

Fan,

Zhang,

et al. 2024

Journal of Manufacturing Systems

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Enhancing metal additive manufacturing training with the advanced vision language model: A pathway to immersive augmented reality training for non-experts

Fan,

Zhang,

et al. 2024

Journal of Manufacturing Systems

View full text Add to dashboard Cite

Research on intelligent auxiliary assembly technology based on deep learning

Yan,

Wei,

Baitao

2024

Cobot

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Background Auxiliary assembly refers to guiding and prompting the assembly process to help operators complete complex assembly operations. Due to the complex structure of products, the similar shape of parts and human factors, the misassembly and missing assembly of parts still occur in the process of product assembly, so it is of great significance to detect the assembly correctness of complex products. Methods Aiming at the problem that manual inspection is inefficient and depends heavily on the level of inspectors in the process of complex product assembly inspection, this paper proposes an assembly correctness detection method based on deep learning. Through the three steps of view transformation, semantic segmentation and template matching, the automatic judgment of assembly errors such as wrong assembly, missing assembly and redundancy is realized, and the method is verified by the computer motherboard. Results Taking the computer motherboard as the verification object to test the correctness of assembly, the experimental re sults show that the perspective adjustment of the image after homography transformation is very obvious. The evaluation index of the semantic segmentation network detection object is calculated, and each accuracy meets the requirements of assembly correctness detection. A visualization module is also used to visually display the results of assembly correctness detection based on template matching. Conclusions The assembly correctness detection method can provide a guarantee for the manual assembly process and reduce the error rate of assembly. The machine vision detection technology can be used for automatic detection of assembly quality to improve the efficiency and automation level of detection.

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Deep learning-based augmented reality work instruction assistance system for complex manual assembly

Cited by 2 publications

References 49 publications

Enhancing metal additive manufacturing training with the advanced vision language model: A pathway to immersive augmented reality training for non-experts

Enhancing metal additive manufacturing training with the advanced vision language model: A pathway to immersive augmented reality training for non-experts

Research on intelligent auxiliary assembly technology based on deep learning

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