{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T20:26:18Z","timestamp":1746303978475,"version":"3.37.3"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,2,26]],"date-time":"2022-02-26T00:00:00Z","timestamp":1645833600000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003052","name":"MOTIE","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,2,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>With the growing interest in smart factories, defect-prediction algorithms using data analysis techniques are being developed and applied to solve problems caused by defects at manufacturing sites. Cost benefit is an important factor to consider, and can be obtained by applying such algorithms. Existing defect-prediction algorithms usually aim to reduce the error rate of the prediction model, rather than focusing on the cost benefit for the practical application of defect-prediction models. Therefore, this study develops a defect-prediction algorithm considering costs and systematization for field application. To this end, a type error-weighted deep neural network (TEW-DNN) is proposed that applies a loss function to set a different weight for each type error, and cost analysis is conducted to search the optimal type error weight. A cost analysis-based defect-prediction system is designed considering the TEW-DNN algorithm and a cyber-physical system environment. The efficacy of the designed system is demonstrated through a case study involving the application of the system in a die-casting factory in South Korea.<\/jats:p>","DOI":"10.1093\/jcde\/qwac006","type":"journal-article","created":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T15:32:57Z","timestamp":1642001577000},"page":"380-392","source":"Crossref","is-referenced-by-count":7,"title":["Development of a cost analysis-based defect-prediction system with a type error-weighted deep neural network algorithm"],"prefix":"10.1093","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6574-9245","authenticated-orcid":false,"given":"Jun","family":"Kim","sequence":"first","affiliation":[{"name":"Digital Transformation R&D Department, Korea Institute of Industrial Technology, 143 Hangaul-ro, Ansan 34141, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ju Yeon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Mechanical System Design Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Seoul 01811, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,2,25]]},"reference":[{"issue":"5","key":"2022022603110692400_bib1","doi-asserted-by":"crossref","first-page":"2245","DOI":"10.1007\/s10845-017-1388-1","article-title":"A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction","volume":"30","author":"Bai","year":"2019","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"4","key":"2022022603110692400_bib2","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.jcde.2019.02.001","article-title":"Maintenance in aeronautics in an Industry 4.0 context: The role of augmented reality and additive manufacturing","volume":"6","author":"Ceruti","year":"2019","journal-title":"Journal of Computational Design and Engineering"},{"issue":"3","key":"2022022603110692400_bib3","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1016\/j.eswa.2007.07.037","article-title":"A neural network-based approach for dynamic quality prediction in a plastic injection molding process","volume":"35","author":"Chen","year":"2008","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"2022022603110692400_bib4","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1093\/jcde\/qwaa088","article-title":"Middleware for providing activity-driven assistance in cyber-physical production systems","volume":"8","author":"Dhiman","year":"2021","journal-title":"Journal of Computational Design and Engineering"},{"key":"2022022603110692400_bib5","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.4028\/www.scientific.net\/AMR.889-890.1231","article-title":"Dynamic quality prediction of manufacturing process based on extreme learning machine","volume":"889","author":"Guo","year":"2014","journal-title":"Advanced Materials Research"},{"issue":"3","key":"2022022603110692400_bib6","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1093\/jcde\/qwaa027","article-title":"A review of smart manufacturing reference models based on the skeleton meta-model","volume":"7","author":"Han","year":"2020","journal-title":"Journal of Computational Design and Engineering"},{"issue":"3","key":"2022022603110692400_bib7","doi-asserted-by":"crossref","first-page":"3057","DOI":"10.1109\/TIA.2017.2661250","article-title":"Deep learning-based approach for bearing fault diagnosis","volume":"53","author":"He","year":"2017","journal-title":"IEEE Transactions on Industry Applications"},{"issue":"5786","key":"2022022603110692400_bib8","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"issue":"4","key":"2022022603110692400_bib9","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1109\/TASLP.2015.2409733","article-title":"Maximum F1-score discriminative training criterion for automatic mispronunciation detection","volume":"23","author":"Huang","year":"2015","journal-title":"IEEE\/ACM Transactions on Audio, Speech, and Language Processing"},{"issue":"3","key":"2022022603110692400_bib10","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1093\/jcde\/qwaa029","article-title":"Modeling and assessing cyber resilience of smart grid using Bayesian network-based approach: A system of systems problem","volume":"7","author":"Ibne\u00a0Hossain","year":"2020","journal-title":"Journal of Computational Design and Engineering"},{"key":"2022022603110692400_bib11","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1016\/j.neucom.2017.07.032","article-title":"A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines","volume":"272","author":"Jia","year":"2018","journal-title":"Neurocomputing"},{"key":"2022022603110692400_bib13","first-page":"61","article-title":"Application of XGBoost algorithm in manufacturing quality prediction","volume":"6","author":"Jiang","year":"2017","journal-title":"Journal of Computational Intelligence and Applications"},{"issue":"7","key":"2022022603110692400_bib12","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/JAS.2021.1004051","article-title":"Remaining useful life prediction for a roller in a hot strip mill based on deep recurrent neural networks","volume":"8","author":"Jiao","year":"2021","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"2022022603110692400_bib15","doi-asserted-by":"crossref","first-page":"102040","DOI":"10.1016\/j.rcim.2020.102040","article-title":"Server-Edge dualized closed-loop data analytics system for cyber-physical system application","volume":"67","author":"Kim","year":"2021","journal-title":"Robotics and Computer-Integrated Manufacturing"},{"issue":"4","key":"2022022603110692400_bib14","doi-asserted-by":"crossref","first-page":"247","DOI":"10.7736\/JKSPE.019.136","article-title":"Development of intelligence data analytics system for quality enhancement of die-casting process","volume":"37","author":"Kim","year":"2020","journal-title":"Journal of the Korean Society for Precision Engineering"},{"article-title":"Adam: A method for stochastic optimization","year":"2014","author":"Kingma","key":"2022022603110692400_bib16"},{"key":"2022022603110692400_bib18","article-title":"Intelligent fault diagnosis for large-scale rotating machines using binarized deep neural networks and random forests","author":"Li","year":"2021","journal-title":"IEEE Transactions on Automation Science and Engineering"},{"key":"2022022603110692400_bib17","doi-asserted-by":"crossref","first-page":"21980","DOI":"10.1109\/ACCESS.2018.2827422","article-title":"Multi-context integrated deep neural network model for next location prediction","volume":"6","author":"Liao","year":"2018","journal-title":"IEEE Access"},{"issue":"13","key":"2022022603110692400_bib19","doi-asserted-by":"crossref","first-page":"9981","DOI":"10.1007\/s00500-019-04515-0","article-title":"EEG signal classification using LSTM and improved neural network algorithms","volume":"24","author":"Nagabushanam","year":"2020","journal-title":"Soft Computing"},{"issue":"4","key":"2022022603110692400_bib20","doi-asserted-by":"crossref","first-page":"271","DOI":"10.7232\/JKIIE.2013.39.4.271","article-title":"A product quality prediction model using real-time process monitoring in manufacturing supply chain","volume":"39","author":"Oh","year":"2013","journal-title":"Journal of Korean Institute of Industrial Engineers"},{"issue":"5","key":"2022022603110692400_bib21","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1093\/jcde\/qwaa047","article-title":"A method to improve workers\u2019 well-being toward human-centered connected factories","volume":"7","author":"Papetti","year":"2020","journal-title":"Journal of Computational Design and Engineering"},{"issue":"2","key":"2022022603110692400_bib22","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.ijpe.2012.03.012","article-title":"Measuring supply chain cost","volume":"143","author":"Pettersson","year":"2013","journal-title":"International Journal of Production Economics"},{"issue":"9","key":"2022022603110692400_bib23","doi-asserted-by":"crossref","first-page":"094002","DOI":"10.1088\/1361-6579\/aad9ee","article-title":"Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG","volume":"39","author":"Plesinger","year":"2018","journal-title":"Physiological Measurement"},{"issue":"2","key":"2022022603110692400_bib24","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1093\/jcde\/qwaa012","article-title":"Systematic analysis of needs and requirements for the design of smart manufacturing systems in SMEs","volume":"7","author":"Rauch","year":"2020","journal-title":"Journal of Computational Design and Engineering"},{"issue":"9","key":"2022022603110692400_bib25","doi-asserted-by":"crossref","first-page":"3721","DOI":"10.1109\/TNNLS.2020.3001602","article-title":"A wide-deep-sequence model-based quality prediction method in industrial process analysis","volume":"31","author":"Ren","year":"2020","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"4","key":"2022022603110692400_bib26","doi-asserted-by":"crossref","first-page":"35","DOI":"10.3390\/logistics4040035","article-title":"Machine learning methods for quality prediction in production","volume":"4","author":"Sankhye","year":"2020","journal-title":"Logistics"},{"issue":"3","key":"2022022603110692400_bib27","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1093\/jcde\/qwaa022","article-title":"A critical review of symbiosis approaches in the context of Industry 4.0","volume":"7","author":"Scaf\u00e0","year":"2020","journal-title":"Journal of Computational Design and Engineering"},{"issue":"1","key":"2022022603110692400_bib28","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1109\/TIM.2017.2759418","article-title":"Intelligent bearing fault diagnosis method combining compressed data acquisition and deep learning","volume":"67","author":"Sun","year":"2017","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"issue":"4","key":"2022022603110692400_bib29","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/j.jcde.2019.04.002","article-title":"The state of framework development for implementing reasoning mechanisms in smart cyber-physical systems: A literature review","volume":"6","author":"Tepjit","year":"2019","journal-title":"Journal of Computational Design and Engineering"},{"key":"2022022603110692400_bib30","doi-asserted-by":"crossref","first-page":"105683","DOI":"10.1016\/j.asoc.2019.105683","article-title":"A generative neural network model for the quality prediction of work in progress products","volume":"85","author":"Wang","year":"2019","journal-title":"Applied Soft Computing"},{"issue":"2","key":"2022022603110692400_bib32","doi-asserted-by":"crossref","first-page":"425","DOI":"10.3390\/s17020425","article-title":"A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals","volume":"17","author":"Zhang","year":"2017","journal-title":"Sensors"},{"issue":"4","key":"2022022603110692400_bib31","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1109\/TASE.2013.2287347","article-title":"A quality-relevant sequential phase partition approach for regression modeling and quality prediction analysis in manufacturing processes","volume":"11","author":"Zhao","year":"2013","journal-title":"IEEE Transactions on Automation Science and Engineering"}],"container-title":["Journal of Computational Design and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jcde\/article-pdf\/9\/2\/380\/42616808\/qwac006.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jcde\/article-pdf\/9\/2\/380\/42616808\/qwac006.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,26]],"date-time":"2022-02-26T03:12:18Z","timestamp":1645845138000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jcde\/article\/9\/2\/380\/6537178"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,25]]},"references-count":32,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,2,25]]}},"URL":"https:\/\/doi.org\/10.1093\/jcde\/qwac006","relation":{},"ISSN":["2288-5048"],"issn-type":[{"type":"electronic","value":"2288-5048"}],"subject":[],"published-other":{"date-parts":[[2022,4]]},"published":{"date-parts":[[2022,2,25]]}}}