Enhancing automated defect detection through sequential clustering and classification: a industrial case study using the sine-cosine algorithm, possibilistic fuzzy c-means, and artificial neural network

URN:NBN:SI:DOC-F2PC8HUS2023Le, M. D.Luu, M. T.Nguyen, T. P. Q.Nguyen, T. T.Yang, C. L.documents/doc/F/URN_NBN_SI_doc-F2PC8HUS_001.pdfdocuments/doc/F/URN_NBN_SI_doc-F2PC8HUS_001.txt182articlestr. 237-24910.14743/apem2023.2.4701854-6250268815107URN:NBN:SI:doc-F2PC8HUSengFakulteta za strojništvo, Inštitut za proizvodno strojništvoAdvances in production engineering and managementBYback propagation neural networkclassificationclusteringcombined SCA-PFCMdefect detectionizdelava kleščkalsifikacijanevronske mreženipper manufacturingodkrivanje napakPFCMpossibilistic fuzzy c-meansroot cause analysisSCAsine-cosine algorithmEnhancing automated defect detection through sequential clustering and classification: a industrial case study using the sine-cosine algorithm, possibilistic fuzzy c-means, and artificial neural network