{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:48:19Z","timestamp":1750308499447,"version":"3.41.0"},"reference-count":20,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T00:00:00Z","timestamp":1669680000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGKDD Explor. Newsl."],"published-print":{"date-parts":[[2022,11,29]]},"abstract":"<jats:p>Industry and Quality 4.0 pose the opportunity to integrate artificial intelligence-based technology into the quality management of products\/services. Particularly, quality control procedures of tableware ceramics require a demanding and faulty human manual (visual and acoustic) inspection. In this paper, we propose an uncertainty-aware automated acoustic inspection using a supervised machine learning model based on a set of novel acoustic features to classify ceramic plates, as cracked and uncracked. We conducted experiments on a dataset of 31 ceramic plates (16 cracked and 15 uncracked), collected in the laboratory. Data quality check and augmentation strategies were also performed, resulting in 2900 samples. The main contributions of this paper are: 1) description of 192 features selected for the acoustic inspection of ceramic plates; 2) comparison of model calibration results regarding three different classifiers; 3) study of different sources of uncertainty for classification with rejection option, through uncertainty quantification measures, and the effect of feature selection on it. We performed two experiments that differ in the usage of a supervised feature selection method. We split the augmented dataset into train\/test sets in a proportion of 90\/10. The calibrated SVM was selected as the best classifier based on model calibration and cross-validation results and was used in the prediction on the test set. The uncertainty-based rejection improved the train and test sets' classification results. In the experiment with feature selection, the classification performance remained high, while the uncertainty about the predictions and the percentage of rejected samples decreased.<\/jats:p>","DOI":"10.1145\/3575637.3575653","type":"journal-article","created":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T14:14:48Z","timestamp":1670508888000},"page":"105-113","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Acoustic Structural Integrity Assessment of Ceramics using Supervised Machine Learning and Uncertainty-Based Rejection"],"prefix":"10.1145","volume":"24","author":[{"given":"Maria Lua","family":"Nunes","sequence":"first","affiliation":[{"name":"Associacao Fraunhofer Portugal Research, Porto, Portugal"}]},{"given":"Marilia","family":"Barandas","sequence":"additional","affiliation":[{"name":"Associacao Fraunhofer Portugal Research, Porto, Portugal"}]},{"given":"Hugo","family":"Gamboa","sequence":"additional","affiliation":[{"name":"Associacao Fraunhofer Portugal Research, Porto, Portugal"}]},{"given":"Filipe","family":"Soares","sequence":"additional","affiliation":[{"name":"Associacao Fraunhofer Portugal Research, Porto, Portugal"}]}],"member":"320","published-online":{"date-parts":[[2022,12,8]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Damage detection in ceramic materials using bicoherence analysis. Balkan Journal of Electrical and Computer Engineering, page 300--306","author":"Akgun O.","year":"2020","unstructured":"O. Akgun . Damage detection in ceramic materials using bicoherence analysis. Balkan Journal of Electrical and Computer Engineering, page 300--306 , 2020 . O. Akgun. Damage detection in ceramic materials using bicoherence analysis. Balkan Journal of Electrical and Computer Engineering, page 300--306, 2020."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.18280\/ts.370102"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00685464"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.2478\/v10168-011-0007-y"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.2478\/v10168-012-0036-1"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11030396"},{"key":"e_1_2_1_7_1","volume-title":"Quality 4.0: An overview. Procedia Computer Science, 181:341--346","author":"Carvalho A. V.","year":"2021","unstructured":"A. V. Carvalho , D. V. Enrique , A. Chouchene , and F. Charrua-Santos . Quality 4.0: An overview. Procedia Computer Science, 181:341--346 , 2021 . A. V. Carvalho, D. V. Enrique, A. Chouchene, and F. Charrua-Santos. Quality 4.0: An overview. Procedia Computer Science, 181:341--346, 2021."},{"key":"e_1_2_1_8_1","volume-title":"Jun","author":"Depeweg S.","year":"2018","unstructured":"S. Depeweg , J. M. Hern\u00b4andez-Lobato , F. Doshi-Velez , and S. Udluft . Decomposition of uncertainty in bayesian deep learning for efficient and risk-sensitive learning , Jun 2018 . S. Depeweg, J. M. Hern\u00b4andez-Lobato, F. Doshi-Velez, and S. Udluft. Decomposition of uncertainty in bayesian deep learning for efficient and risk-sensitive learning, Jun 2018."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.06.038"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.sintl.2021.100109"},{"key":"e_1_2_1_11_1","volume-title":"Defect detection for ceramic materials by continuous wavelet analysis","author":"Kaynas T.","year":"2011","unstructured":"T. Kaynas , T. C. Akinci , O. Yilmaz , M. Ozgiray , and S. Seker . Defect detection for ceramic materials by continuous wavelet analysis , 2011 . T. Kaynas, T. C. Akinci, O. Yilmaz, M. Ozgiray, and S. Seker. Defect detection for ceramic materials by continuous wavelet analysis, 2011."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultras.2016.07.008"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1108\/TQM-11-2021-0328"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.3390\/ma11122477"},{"key":"e_1_2_1_15_1","first-page":"338","article-title":"Coherence analysis and transfer function model for ceramic plate vibrations","volume":"14","author":"Nayir A.","year":"2012","unstructured":"A. Nayir . Coherence analysis and transfer function model for ceramic plate vibrations . Journal of Vibroengineering , 14 : 338 -- 342 , 03 2012 . A. Nayir. Coherence analysis and transfer function model for ceramic plate vibrations. Journal of Vibroengineering, 14:338--342, 03 2012.","journal-title":"Journal of Vibroengineering"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06652-w"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/IISA52424.2021.9555499"},{"key":"e_1_2_1_18_1","volume-title":"AIP Conference Proceedings","author":"Whitlow T.","year":"2016","unstructured":"T. Whitlow , E. Jones , and C. Przybyla . Failure prediction in ceramic composites using acoustic emission and digital image correlation . AIP Conference Proceedings , 2016 . T. Whitlow, E. Jones, and C. Przybyla. Failure prediction in ceramic composites using acoustic emission and digital image correlation. AIP Conference Proceedings, 2016."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ceramint.2020.10.065"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ceramint.2020.10.065"}],"container-title":["ACM SIGKDD Explorations Newsletter"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3575637.3575653","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3575637.3575653","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T18:43:52Z","timestamp":1750272232000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3575637.3575653"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,29]]},"references-count":20,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,11,29]]}},"alternative-id":["10.1145\/3575637.3575653"],"URL":"https:\/\/doi.org\/10.1145\/3575637.3575653","relation":{},"ISSN":["1931-0145","1931-0153"],"issn-type":[{"type":"print","value":"1931-0145"},{"type":"electronic","value":"1931-0153"}],"subject":[],"published":{"date-parts":[[2022,11,29]]},"assertion":[{"value":"2022-12-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}