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Intell."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Over the past two decades, machine learning (ML) has transformed manufacturing, particularly in optimizing production and quality control. A significant challenge in ML applications is obtaining sufficient training data, which data augmentation aims to address. While widely applied to image, text, and sound data, data augmentation for numerical data in manufacturing has seen limited investigation. This paper empirically compares three data augmentation techniques\u2014generative adversarial networks, variational auto-encoders mixed with long-short-term memory, and warping\u2014on four manufacturing datasets. It also provides a literature review, highlighting that generative models are the most common technique for numerical manufacturing data. Preliminary findings suggest that generative adversarial networks are effective for non-time-series numerical data, especially with datasets featuring many correlated model features, multiple machines, and sufficient instances and labels. This research enhances the understanding of data augmentation in manufacturing ML applications, emphasizing the need for tailored strategies.<\/jats:p>","DOI":"10.1007\/s44244-024-00021-x","type":"journal-article","created":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T04:57:26Z","timestamp":1736225846000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Data augmentation for numerical data from manufacturing processes: an overview of techniques and assessment of when which techniques work"],"prefix":"10.1007","volume":"3","author":[{"given":"Henry","family":"Ekwaro-Osire","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sai Lalitha","family":"Ponugupati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah","family":"Al Noman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dennis","family":"Bode","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klaus-Dieter","family":"Thoben","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,7]]},"reference":[{"issue":"3","key":"21_CR1","doi-asserted-by":"publisher","first-page":"1903","DOI":"10.3390\/app13031903","volume":"13","author":"T Chen","year":"2023","unstructured":"Chen T, Sampath V, May MC, Shan S, Jorg OJ, Aguilar Mart\u00edn JJ, Stamer F, Fantoni G, Tosello G, Calaon M (2023) Machine learning in manufacturing towards industry 4.0: from \u2018for now\u2019 to \u2018four-know.\u2019 Appl Sci 13(3):1903. https:\/\/doi.org\/10.3390\/app13031903","journal-title":"Appl Sci"},{"issue":"23","key":"21_CR2","doi-asserted-by":"publisher","first-page":"15618","DOI":"10.3390\/su142315618","volume":"14","author":"H Ekwaro-Osire","year":"2022","unstructured":"Ekwaro-Osire H, Bode D, Thoben K-D, Ohlendorf J-H (2022) Identification of machine learning relevant energy and resource manufacturing efficiency levers. 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