20.500.12556/DKUM-97123 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 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. 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 nevronske mreže kalsifikacija odkrivanje napak izdelava klešč true false true Fakulteta za strojništvo, Inštitut za proizvodno strojništvo Angleški jezik Slovenski jezik https://apem-journal.org/index.html 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. Članek v reviji 2026-02-19 07:03:08 2026-02-19 07:03:13 2026-03-20 07:09:59 0000-00-00 00:00:00 2023 0 0 str. 237-249 no. 2 Vol. 18 Jun. 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2023-04-24 2023-06-23 2023-07-23 658.5 268815107 10.14743/apem2023.2.470 1854-6250 APEM18-2_237-249.pdf APEM18-2_237-249.pdf 1 8821E82030FC9C5633172F03CCD80E69 8e597f0558dda9c727d336469abd8ab054ab1a0ebbb693eb4b8c1647905bfc6c 2aa8b2b6-0d5a-11f1-a5b8-00155d580e1a https://dk.um.si/Dokument.php?lang=slv&id=203488 Fakulteta za strojništvo Advances in production engineering & management 0 0 0