| Title: | Enhancing automated defect detection through sequential clustering and classification: an industrial case study using the sine-cosine algorithm, possibilistic fuzzy c-means, and artificial neural network |
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| Authors: | ID Nguyen, T. P. Q. (Author)
ID Yang, C. L. (Author)
ID Le, M. D. (Author)
ID Nguyen, T. T. (Author)
ID Luu, M. T. (Author) |
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| Files: |  APEM18-2_237-249.pdf (1,05 MB)
MD5: 8821E82030FC9C5633172F03CCD80E69
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | Most existing inspection models solely classify defects as either good or bad, focusing primarily on separating flaws from perfect ones. The sequential clustering and classification technique (SCC) is used in this work to not only identify and categorize the defects but also investigate their root causes. Conventional clustering techniques like k-means, fuzzy c-means, and self-organizing map are employed in the first stage to find the defects in the finished products. Then, a novel clustering method, that combines a sine-cosine algorithm and possibilistic fuzzy c-means (SCA-PFCM), is proposed to classify the detected defects into multiple groups to identify the defect categories and analyze the root causes of failures. In the second stage, the ground truth labels taken from the clustering technique are used to construct an automated inspection system using back propagation neural networks (BPNN). The proposed approach is applicable for detecting and identifying the causes of errors in manufacturing industry. This study applies a case study in nipper manufacture. The SCA-PFCM algorithm can detect 97 % of defects and classify them into four types while BPNN shows a predicted accuracy of up to 96 %. Additionally, an automated inspection system is developed to reduce the time and cost of the inspection process. |
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| Keywords: | back propagation neural network, clustering, classification, combined SCA-PFCM, defect detection, nipper manufacturing, possibilistic fuzzy c-means, root cause analysis, PFCM, sine-cosine algorithm, SCA |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 24.04.2023 |
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| Article acceptance date: | 23.06.2023 |
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| Publication date: | 23.07.2023 |
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| Publisher: | Fakulteta za strojništvo, Inštitut za proizvodno strojništvo |
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| Year of publishing: | 2023 |
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| Number of pages: | str. 237-249 |
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| Numbering: | Vol. 18, no. 2 |
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| PID: | 20.500.12556/DKUM-97123  |
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| UDC: | 658.5 |
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| ISSN on article: | 1854-6250 |
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| COBISS.SI-ID: | 268815107 |
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| DOI: | 10.14743/apem2023.2.470 |
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| Copyright: | Content from this work may be used under the terms of the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation, and DOI. |
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| Publication date in DKUM: | 19.02.2026 |
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| Views: | 142 |
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| Downloads: | 1 |
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| Metadata: |    |
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| Categories: | Misc.
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