{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T14:13:45Z","timestamp":1774534425124,"version":"3.50.1"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030870935","type":"print"},{"value":"9783030870942","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,11,18]],"date-time":"2021-11-18T00:00:00Z","timestamp":1637193600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,11,18]],"date-time":"2021-11-18T00:00:00Z","timestamp":1637193600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-87094-2_47","type":"book-chapter","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T07:06:45Z","timestamp":1637132805000},"page":"532-544","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Defect Prediction on Production Line"],"prefix":"10.1007","author":[{"given":"Souhaiel","family":"Khalfaoui","sequence":"first","affiliation":[]},{"given":"Eric","family":"Manouvrier","sequence":"additional","affiliation":[]},{"given":"Alexandre","family":"Briot","sequence":"additional","affiliation":[]},{"given":"David","family":"Delaux","sequence":"additional","affiliation":[]},{"given":"St\u00e9phane","family":"Butel","sequence":"additional","affiliation":[]},{"given":"Jesutofunmi","family":"Ibrahim","sequence":"additional","affiliation":[]},{"given":"Tatenda","family":"Kanyere","sequence":"additional","affiliation":[]},{"given":"Bola","family":"Orimogunje","sequence":"additional","affiliation":[]},{"given":"Amr","family":"Abdullatif","sequence":"additional","affiliation":[]},{"given":"Daniel","family":"Neagu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,11,18]]},"reference":[{"issue":"1","key":"47_CR1","first-page":"23","volume":"4","author":"T Wuest","year":"2016","unstructured":"Wuest, T., Weimer, D., Irgens, C.: Machine learning in manufacturing: advantages, challenges, and applications. J. Prod. Manuf. Res. 4(1), 23\u201345 (2016)","journal-title":"J. Prod. Manuf. Res."},{"issue":"5","key":"47_CR2","doi-asserted-by":"publisher","first-page":"2835","DOI":"10.1007\/s00170-018-2117-4","volume":"97","author":"I Baturynska","year":"2018","unstructured":"Baturynska, I.: Statistical analysis of dimensional accuracy in additive manufacturing considering STL model properties. J. Adv. Man. Tech. 97(5), 2835\u20132849 (2018)","journal-title":"J. Adv. Man. Tech."},{"issue":"5","key":"47_CR3","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1243\/095440505X32274","volume":"219","author":"DT Pham","year":"2005","unstructured":"Pham, D.T., Afify, A.A.: Machine-learning techniques and their applications in manufacturing. Procs. IMechE Part B: J. Eng. Manuf. 219(5), 395\u2013412 (2005)","journal-title":"Procs. IMechE Part B: J. Eng. Manuf."},{"issue":"18\u201319","key":"47_CR4","doi-asserted-by":"publisher","first-page":"4175","DOI":"10.1080\/00207540600632216","volume":"44","author":"A Kusiak","year":"2006","unstructured":"Kusiak, A.: Data mining: manufacturing and service applications. Int. J. Prod. Res. 44(18\u201319), 4175\u20134191 (2006)","journal-title":"Int. J. Prod. Res."},{"key":"47_CR5","unstructured":"Auschitzky, E., Hammer, M., Rajagopaul, A.: How big data can improve manufacturing. McKinsey Company 822 (2014)"},{"key":"47_CR6","doi-asserted-by":"crossref","unstructured":"Feldkamp, N., Bergmann, S., Strassburger, S.: Knowledge discovery in manufacturing simulations. In: 3rd ACM SIGSIM Advanced Discrete Simulation, pp. 3\u201312 (2015)","DOI":"10.1145\/2769458.2769468"},{"key":"47_CR7","doi-asserted-by":"crossref","unstructured":"Zhang, D., Xu, B., Wood, J.: Predict failures in production lines: a two-stage approach with clustering and supervised learning. Big Data 2016, 2070\u20132074. IEEE (2016)","DOI":"10.1109\/BigData.2016.7840832"},{"key":"47_CR8","unstructured":"Bosch. Production line performance (2016)"},{"key":"47_CR9","unstructured":"\u00c9cole Normale Sup\u00e9rieure of Paris and Coll\u00e8ge de France. Challenge data (2020). https:\/\/challengedata.ens.fr\/challenges\/year\/2020"},{"key":"47_CR10","doi-asserted-by":"crossref","unstructured":"Paolanti, M., Romeo, Felicetti et al: Machine learning approach for predictive maintenance in industry 4.0. In: 14th IEEE\/ASME MESA, pp. 1\u20136. IEEE (2018)","DOI":"10.1109\/MESA.2018.8449150"},{"key":"47_CR11","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1016\/j.egyr.2020.04.035","volume":"6","author":"DAC Narciso","year":"2020","unstructured":"Narciso, D.A.C., Martins, F.G.: Application of machine learning tools for energy efficiency in industry: a review. Energy Rep. 6, 1181\u20131199 (2020)","journal-title":"Energy Rep."},{"key":"47_CR12","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1146\/annurev-fluid-010719-060214","volume":"52","author":"SL Brunton","year":"2020","unstructured":"Brunton, S.L., Noack, B.R., Koumoutsakos, P.: Machine learning for fluid mechanics. Annu. Rev. Fluid Mech. 52, 477\u2013508 (2020)","journal-title":"Annu. Rev. Fluid Mech."},{"key":"47_CR13","doi-asserted-by":"publisher","unstructured":"Candanedo, I.S., Nieves, E.H., Gonz\u00e1lez, S.R., Mart\u00edn, M.T.S., Briones, A.G.: Machine learning predictive model for industry 4.0. In: Uden, L., Hadzima, B., Ting, I.-H. (eds.) Knowledge Management in Organizations, KMO 2018. CCIS, vol. 877, pp. 501\u2013510. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-95204-8_42","DOI":"10.1007\/978-3-319-95204-8_42"},{"key":"47_CR14","doi-asserted-by":"publisher","first-page":"79908","DOI":"10.1109\/ACCESS.2019.2923405","volume":"7","author":"RS Peres","year":"2019","unstructured":"Peres, R.S., Barata, J., Leitao, P., Garcia, G.: Multistage quality control using machine learning in the automotive industry. IEEE Access 7, 79908\u201379916 (2019)","journal-title":"IEEE Access"},{"key":"47_CR15","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898718317","volume-title":"Credit Scoring and Its Applications","author":"LC Thomas","year":"2002","unstructured":"Thomas, L.C., Crook, J., Edelman, D.: Credit Scoring and Its Applications. Society for Industrial and Applied Mathematics, USA (2002)"},{"key":"47_CR16","unstructured":"Kerber, R.: Chimerge: Discretization of numeric attributes. In: AAAI 92 Proceedings of the 10th National Conference on Artificial Intelligence, pp. 123\u2013128. AAAI Press (1992)"},{"key":"47_CR17","unstructured":"Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued at-tributes for classification learning. In: IJCAI, pp. 1022\u20131029 (1993)"},{"issue":"3","key":"47_CR18","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1198\/106186006X133933","volume":"15","author":"T Hothorn","year":"2006","unstructured":"Hothorn, T., Hornik, K., Zeileis, A.: Unbiased recursive partitioning: a conditional inference framework. J. Comp Graph. Stat. 15(3), 651\u2013674 (2006)","journal-title":"J. Comp Graph. Stat."},{"key":"47_CR19","unstructured":"Navas-Palencia, G.: Optimal binning: mathematical programming formulation. CoRR,abs\/2001.08025 (2020)"},{"key":"47_CR20","doi-asserted-by":"crossref","unstructured":"Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 1189\u20131232 (2001)","DOI":"10.1214\/aos\/1013203451"},{"key":"47_CR21","volume-title":"Classification and Regression Trees","author":"L Breiman","year":"1984","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth and Brooks, Monterey, CA (1984)"},{"issue":"23\u201358","key":"47_CR22","first-page":"81","volume":"11","author":"CJC Burges","year":"2010","unstructured":"Burges, C.J.C.: From Ranknet to LambdaRank to LambdaMART: an overview. Learning 11(23\u201358), 81 (2010)","journal-title":"Learning"},{"key":"47_CR23","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"issue":"6","key":"47_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3136625","volume":"50","author":"J Li","year":"2018","unstructured":"Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R.P., Tang, J., Liu, H.: Feature selection. ACM Comput. Surv. 50(6), 1\u201345 (2018)","journal-title":"ACM Comput. Surv."},{"issue":"1","key":"47_CR25","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.chemolab.2005.05.004","volume":"80","author":"CD Brown","year":"2006","unstructured":"Brown, C.D., Davis, H.T.: Receiver operating characteristics curves and related decision measures: a tutorial. Chems. Intell. Lab. Sys. 80(1), 24\u201338 (2006)","journal-title":"Chems. Intell. Lab. Sys."}],"container-title":["Advances in Intelligent Systems and Computing","Advances in Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87094-2_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T07:14:25Z","timestamp":1637133265000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87094-2_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,18]]},"ISBN":["9783030870935","9783030870942"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87094-2_47","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"value":"2194-5357","type":"print"},{"value":"2194-5365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,18]]},"assertion":[{"value":"18 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"UKCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"UK Workshop on Computational Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Aberystwyth","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ukci2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ukci2021.dcs.aber.ac.uk\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}