{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,16]],"date-time":"2026-08-16T02:06:40Z","timestamp":1786846000159,"version":"3.56.0"},"reference-count":38,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003827","name":"National Research, Development and Innovation Office","doi-asserted-by":"publisher","award":["2018\u20131.3.1-VKE-2018-00001"],"award-info":[{"award-number":["2018\u20131.3.1-VKE-2018-00001"]}],"id":[{"id":"10.13039\/501100003827","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003827","name":"National Research, Development and Innovation Office","doi-asserted-by":"publisher","award":["OTKA FK134336"],"award-info":[{"award-number":["OTKA FK134336"]}],"id":[{"id":"10.13039\/501100003827","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003827","name":"National Research, Development and Innovation Office","doi-asserted-by":"publisher","award":["TKP2020 NC, Grant No. BME-NC"],"award-info":[{"award-number":["TKP2020 NC, Grant No. BME-NC"]}],"id":[{"id":"10.13039\/501100003827","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>One of the essential requirements of injection molding is to ensure the stable quality of the parts produced. However, numerous processing conditions, which are often interrelated in quite a complex way, make this challenging. Machine learning (ML) algorithms can be the solution, as they work in multidimensional spaces by learning the structure of datasets. In this study, we used four ML algorithms (kNN, na\u00efve Bayes, linear discriminant analysis, and decision tree) and compared their effectiveness in predicting the quality of multi-cavity injection molding. We used pressure-based quality indexes (features) as inputs for the classification algorithms. We proved that all the examined ML algorithms adequately predict quality in injection molding even with very little training data. We found that the decision tree algorithm was the most accurate one, with a computational time of only 8\u201310 s. The average performance of the decision tree algorithm exceeded 90%, even for very little training data. We also demonstrated that feature selection does not significantly affect the accuracy of the decision tree algorithm.<\/jats:p>","DOI":"10.3390\/s22072704","type":"journal-article","created":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T21:23:55Z","timestamp":1648848235000},"page":"2704","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":73,"title":["Machine Learning in Injection Molding: An Industry 4.0 Method of Quality Prediction"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8902-6818","authenticated-orcid":false,"given":"Rich\u00e1rd Dominik","family":"P\u00e1rizs","sequence":"first","affiliation":[{"name":"Department of Polymer Engineering, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, M\u0171egyetem rkp. 3., H-1111 Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4521-2771","authenticated-orcid":false,"given":"D\u00e1niel","family":"T\u00f6r\u00f6k","sequence":"additional","affiliation":[{"name":"Department of Polymer Engineering, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, M\u0171egyetem rkp. 3., H-1111 Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3732-7296","authenticated-orcid":false,"given":"Tatyana","family":"Ageyeva","sequence":"additional","affiliation":[{"name":"Department of Polymer Engineering, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, M\u0171egyetem rkp. 3., H-1111 Budapest, Hungary"},{"name":"MTA-BME Lend\u00fclet Lightweight Polymer Composites Research Group, M\u0171egyetem rkp. 3., H-1111 Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7391-7085","authenticated-orcid":false,"given":"J\u00f3zsef G\u00e1bor","family":"Kov\u00e1cs","sequence":"additional","affiliation":[{"name":"Department of Polymer Engineering, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, M\u0171egyetem rkp. 3., H-1111 Budapest, Hungary"},{"name":"MTA-BME Lend\u00fclet Lightweight Polymer Composites Research Group, M\u0171egyetem rkp. 3., H-1111 Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,1]]},"reference":[{"key":"ref_1","unstructured":"Schubert, M., Perfetto, S., Dafnis, A., Mayer, D., Atzrodt, H., and Schr\u00f6der, K.-U. 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