Artificial Intelligence in Industry 4.0: A Review of Integration Challenges for Industrial Systems
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866915067979628544 |
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| author | Windmann, Alexander Wittenberg, Philipp Schieseck, Marvin Niggemann, Oliver |
| author_facet | Windmann, Alexander Wittenberg, Philipp Schieseck, Marvin Niggemann, Oliver |
| contents | In Industry 4.0, Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and production planning. However, despite the demonstrated potential of AI, its widespread adoption in sectors like manufacturing remains limited. Our comprehensive review of recent literature, including standards and reports, pinpoints key challenges: system integration, data-related issues, managing workforce-related concerns and ensuring trustworthy AI. A quantitative analysis highlights particular challenges and topics that are important for practitioners but still need to be sufficiently investigated by academics. The paper briefly discusses existing solutions to these challenges and proposes avenues for future research. We hope that this survey serves as a resource for practitioners evaluating the cost-benefit implications of AI in CPS and for researchers aiming to address these urgent challenges. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18580 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence in Industry 4.0: A Review of Integration Challenges for Industrial Systems Windmann, Alexander Wittenberg, Philipp Schieseck, Marvin Niggemann, Oliver Artificial Intelligence Machine Learning I.2.1 In Industry 4.0, Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and production planning. However, despite the demonstrated potential of AI, its widespread adoption in sectors like manufacturing remains limited. Our comprehensive review of recent literature, including standards and reports, pinpoints key challenges: system integration, data-related issues, managing workforce-related concerns and ensuring trustworthy AI. A quantitative analysis highlights particular challenges and topics that are important for practitioners but still need to be sufficiently investigated by academics. The paper briefly discusses existing solutions to these challenges and proposes avenues for future research. We hope that this survey serves as a resource for practitioners evaluating the cost-benefit implications of AI in CPS and for researchers aiming to address these urgent challenges. |
| title | Artificial Intelligence in Industry 4.0: A Review of Integration Challenges for Industrial Systems |
| topic | Artificial Intelligence Machine Learning I.2.1 |
| url | https://arxiv.org/abs/2405.18580 |