Abstract
Plastic bottles are in high demand, as they are cheap to make, easy to fill and can carry most liquids. However, if the thickness of the body of the bottle is inconsistent, it cannot be used to package items containing dissolved gas. If the texture of the bottom of the bottle is incomplete, it also will be unable to be used to package these items. In this paper, we propose a concentric circle method of detecting the bottom of a bottle based on the Hough circle. This approach can be used to detect defects in the thickness of the bottle body based on the fact that the circular groove at the bottom of a plastic bottle and the gate feature have the same central position when the thickness of the body is consistent. Our defect detection method for the texture at the bottom of the bottle is based on the special wing flaps at the bottom of plastic bottles, which have left/right and up/down symmetry features when the texture is correct. The proposed method uses a deep learning image recognition framework for recognition of autonomous features and to detect defects in the texture at the bottom of plastic bottles. The proposed defect detection method for the bottle body has an accuracy of 92.9%, whereas the defect detection method for the bottom of the bottle has an accuracy of 96.5%.
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The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Funding
The Industrial Development Bureau, Ministry of Economic Affairs, Chumpower Machinery Corporation and National Science and Technology Council of Taiwan supported this project under grant number MOST 111-2221-E-239-024.
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Supervision, Ming-Fong Tsai; writing — original draft, Ming-Fong Tsai, Bo-Cheng Liu and Shu-Lin Hsieh; all the authors have read and agreed to the published version of the manuscript.
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Tsai, MF., Liu, BC. & Hsieh, SL. Body thickness and bottom defect detection methods for products made by bottle blowing machines to meet quality requirements. Int J Adv Manuf Technol 130, 541–551 (2024). https://doi.org/10.1007/s00170-023-12693-3
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DOI: https://doi.org/10.1007/s00170-023-12693-3