[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123995-en":3,"doc-seo-123995-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123995,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Machine Learning Applications in Manufacturing - Challenges, Trends, and Future Directions","Industry 4.0 (I4.0) reshapes manufacturing into interconnected, dynamic, and data-rich environments, enabling industrial machine learning (I-ML) to support key production goals. The article presents a systematic literature review grounded in PRISMA guidelines and meta-analyzes, mapping how machine learning is used in manufacturing and highlighting current challenges and future trends. It examines practical scenarios such as predictive maintenance, anomaly detection, and quality control, while addressing needs for sustainable, reproducible, and reliable industrial performance. The work proposes strategies to overcome adoption barriers and outlines future research directions to bridge ML advances with scalable deployment in industry.","Received 27 May 2024; revised 26 June 2024 and 9 July 2024; accepted 12 July 2024. Date of publication 19 July 2024;  \ndate of current version 30 September 2024. The review of this article was arranged by Associate Editor S. Karnouskos.  \nDigital Object Identifier 10.1109/OJIES.2024.3431240  \nMachine Learning Applications in Manufacturing—Challenges, Trends, and Future Directions  \nALEXANDRE MANTA-COSTA 1,2,4, SARA OLEIRO ARAÚJO 1,3,4, RICARDO SILVA PERES 1,2,4,  \nAND JOSÉ BARATA 1,2,4 (Member, IEEE)  \n1UNINOVA—Centre of Technology and Systems (CTS), FCT Campus, 2829-516 Caparica, Portugal  \n2Department of Electrical and Computer Engineering, NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal  \n3Earth Sciences Department (DCT), NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal  \n4Associated Laboratory of Intelligent Systems (LASI), NOVA University of Lisbon, 2829-516 Caparica, Portugal CORRESPONDING AUTHOR: ALEXANDRE MANTA-COSTA (e-mail: [alexandre.costa@uninova.pt](alexandre.costa@uninova.pt)).  \nThis work supported by Fundação para Ciência e Tecnologia through the program under Grant UIDB/00066/2020 and Center of Technology and Systems (CTS) .  \nABSTRACT The emergence of Industry 4.0 (I4.0) has significantly transformed manufacturing landscapes, introducing interconnected, dynamic, and data-rich environments. This article focuses on the application of industrial machine learning (I-ML) within these evolving manufacturing contexts, exploring both the challenges and future prospects of its integration. A systematic literature review, following the preferred reporting items for systematic reviews and meta-analyzes (PRISMA) guidelines, forms the foundation of our analysis, characterizing the role of machine learning (ML) in modern manufacturing, its current challenges, and future trends. This research delves into the implications of I-ML in various manufacturing scenarios, including predictive maintenance, anomaly detection, and quality control, providing a comprehensive overview of practical applications along with an identification of related emerging technologies and trends. We also address the critical need for sustainable, reproducible, and reliable performance in industrial applications and explore strategies for overcoming barriers to ML adoption in the industry. Recommendations for future research directions are provided, aiming to bridge the gap between ML advancements and their practical, scalable implementation in industrial settings, paving the way to future research in the field. Lastly, we aim to contribute to the identification of challenges and future research directions for the ongoing digital transformation of manufacturing industries, offering insights into how ML can be effectively leveraged in the era of I4 .0.  \nINDEX TERMS Industrial artificial intelligence (I-AI), industrial machine learning (I-ML), Industry 4.0 (I4.0), machine learning (ML), manufacturing, systematic review.  \nNOMENCLATURE  \nAD Anomaly detection.  \nAI Artificial intelligence.  \nAR Augmented reality.  \nAT Analytics technologies.  \nBB Balanced bagging.  \nBDT Bounded decision tree.  \nBiFPN Weighted Bi-directional feature pyramid net  \nwork.  \nBiLSTM Bidirectional long short-term memory.  \nBRF Balanced random forest.  \nCAE Convolutional autoencoder.  \nCART Classification and regression trees.  \nCMEANS Fuzzy C-means clustering.  \nCNN Convolutional neural network.  \nCPS Cyber-physical systems.  \nDAGM Deutsche Arbeitsgemeinschaft für Muster  \nerkennung dataset. DIANA DIvisive ANAlysis Clustering.  \nDNN Deep neural network.  \nDPCA Principal components analysis.  \nDRN Deep recurrent network.  \n© 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see  \nVOLUME 5, 2024 [https://creativecommons.org/licenses/by-nc-nd/4.0/ 1085](https://creativecommons.org/licenses/by-nc-nd/4.0/ 1085)  \n","cbCaik0Jhu39XB7Z","https://ap.wps.com/l/cbCaik0Jhu39XB7Z","pdf",2342791,1,19,"English","en",105,"# Introduction\n# Machine Learning in Modern Manufacturing\n## Key Applications: Predictive Maintenance, Anomaly Detection, Quality Control\n# Challenges and Barriers to ML Adoption\n## Sustainability, Reproducibility, and Reliability Requirements\n# Emerging Technologies and Future Directions\n## Recommendations for Future Research","[{\"question\":\"What manufacturing environments enable industrial machine learning under Industry 4.0?\",\"answer\":\"Industry 4.0 creates interconnected, dynamic, and data-rich manufacturing environments that support industrial machine learning integration.\"},{\"question\":\"Which manufacturing use cases does the article focus on for industrial machine learning?\",\"answer\":\"The review addresses predictive maintenance, anomaly detection, and quality control, covering practical application patterns and implications.\"},{\"question\":\"How does the article structure its analysis of industrial machine learning?\",\"answer\":\"It uses a systematic literature review following PRISMA guidelines and meta-analyzes to characterize the role of machine learning, current challenges, and future trends.\"}]","Machine Learning Applications in Manufacturing - 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