Published Papers

Volume 31 - Issue 1 (Regular Issue)
Transactions Papers

Learning-based defect inspection of laptop covers with a laser profile sensor

Jian Liu, Yuxin Cheng, Shuxin Wang, Ziyan Lu, Ming Ge and Edmond Lai
Pages: 1-10Published: 31 Jul 2024
DOI: 10.33430/V31N1THIE-2023-0022
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Liu J, Cheng Y, Wang S, Lu Z, Ge M and Lai E, Learning-based defect inspection of laptop covers with a laser profile sensor, HKIE Transactions, Vol. 31, No. 1 (Regular Issue), Article 20230022, 2024, 10.33430/V31N1THIE-2023-0022

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Abstract:

Quality inspection is a crucial process in the manufacturing industry, which significantly impacts the productivity and profits of a factory. To reduce costs and enhance quality control, companies are increasingly driving automation of this process due to the limitations of manual labour. However, the lack of flexible solutions that can adapt to the changing environment in factories has been a challenge. Computer-vision-based inspection methods often require hefty mechanisms and strict lighting conditions, which limit the investment in such solutions. Furthermore, with increasing demands for precision, traditional algorithms are struggling to cope with the problem, while data-driven solutions like deep learning require significant effort in acquiring and labelling data. To address these problems, this paper proposes an eye-in-hand system, i.e., a sensor attached to a robotic manipulator, integrated with unsupervised learning algorithms. One typical product, laptop bottom covers, is tested and discussed with regard to the system to validate our method since it has tiny defects and light-absorbing coatings. With only nine samples of the product, our system solved the case and demonstrated high-precision results and a flexible workflow. In this paper, our main contributions are: 1) we propose a flexible quality inspection system and workflow that needs extremely small volumes of data with unsupervised learning algorithms; and 2) we first propose using a laser profile sensor for collecting 2D intensity images, that are resilient to ambient light disturbance, of matt laptop cover data without lighting equipment.

Keywords:

Laser profile sensor; defect detection; unsupervised learning; laptop covers; manufacturing products; image processing

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