Machine Learning Applications in Manufacturing—Challenges, Trends, and Future Directions
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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of the Industrial Electronics Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10605047/ |
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| _version_ | 1841526359213473792 |
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| author | Alexandre Manta-Costa Sara Oleiro Araujo Ricardo Silva Peres Jose Barata |
| author_facet | Alexandre Manta-Costa Sara Oleiro Araujo Ricardo Silva Peres Jose Barata |
| author_sort | Alexandre Manta-Costa |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9186cf053e5e45b983d5db5ccb00f247 |
| institution | Kabale University |
| issn | 2644-1284 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Industrial Electronics Society |
| spelling | doaj-art-9186cf053e5e45b983d5db5ccb00f2472025-01-17T00:01:05ZengIEEEIEEE Open Journal of the Industrial Electronics Society2644-12842024-01-0151085110310.1109/OJIES.2024.343124010605047Machine Learning Applications in Manufacturing—Challenges, Trends, and Future DirectionsAlexandre Manta-Costa0https://orcid.org/0000-0002-2210-157XSara Oleiro Araujo1https://orcid.org/0000-0001-6192-8409Ricardo Silva Peres2https://orcid.org/0000-0003-3777-1346Jose Barata3https://orcid.org/0000-0002-6348-1847UNINOVA—Centre of Technology and Systems (CTS), FCT Campus, Caparica, PortugalUNINOVA—Centre of Technology and Systems (CTS), FCT Campus, Caparica, PortugalUNINOVA—Centre of Technology and Systems (CTS), FCT Campus, Caparica, PortugalUNINOVA—Centre of Technology and Systems (CTS), FCT Campus, Caparica, PortugalThe 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.https://ieeexplore.ieee.org/document/10605047/Industrial artificial intelligence (I-AI)industrial machine learning (I-ML)Industry 4.0 (I4.0)machine learning (ML)manufacturingsystematic review |
| spellingShingle | Alexandre Manta-Costa Sara Oleiro Araujo Ricardo Silva Peres Jose Barata Machine Learning Applications in Manufacturing—Challenges, Trends, and Future Directions IEEE Open Journal of the Industrial Electronics Society Industrial artificial intelligence (I-AI) industrial machine learning (I-ML) Industry 4.0 (I4.0) machine learning (ML) manufacturing systematic review |
| title | Machine Learning Applications in Manufacturing—Challenges, Trends, and Future Directions |
| title_full | Machine Learning Applications in Manufacturing—Challenges, Trends, and Future Directions |
| title_fullStr | Machine Learning Applications in Manufacturing—Challenges, Trends, and Future Directions |
| title_full_unstemmed | Machine Learning Applications in Manufacturing—Challenges, Trends, and Future Directions |
| title_short | Machine Learning Applications in Manufacturing—Challenges, Trends, and Future Directions |
| title_sort | machine learning applications in manufacturing x2014 challenges trends and future directions |
| topic | Industrial artificial intelligence (I-AI) industrial machine learning (I-ML) Industry 4.0 (I4.0) machine learning (ML) manufacturing systematic review |
| url | https://ieeexplore.ieee.org/document/10605047/ |
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