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<p>This paper reviews the current applications of artificial intelligence (AI) and machine learning (ML) techniques in industrial manufacturing processes. It provides a comprehensive overview of the three main learning paradigms, supervised, unsupervised, and reinforcement learning and maps their algorithmic families to four major process domains: forming, machining, joining, and additive manufacturing. For each domain, sensor modalities, data characteristics, data acquisition feasibility, and control challenges are analyzed to assess the conditions influencing ML adoption. Differences in maturity levels are shown to arise primarily from variations in data availability, sensor integration complexity, and process automation readiness. The review offers an application-oriented synthesis covering predictive maintenance, real-time process optimization, and automated quality inspection. A publication-weighted maturity matrix demonstrates that supervised learning is well established in machining and additive manufacturing, where standardized sensors and abundant labeled data enable mature AI solutions. By contrast, unsupervised methods are commonly applied for anomaly detection in domains with limited labeled data, such as joining. Reinforcement learning is emerging as a viable approach for closed-loop control, particularly in contexts where reliable digital twins and robust simulation environments support model training. Cross-cutting techniques including physics-informed learning, explainable AI, multi-fidelity surrogate modeling, and edge-to-cloud computing architectures are also discussed. The paper concludes with a proposed future research agenda emphasizing the development of open benchmark datasets, improved simulator fidelity, and interpretable hybrid models, with the broader goal of bridging the gap between academic research and industrial deployment.</p>

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A review of AI and machine learning applications in manufacturing processes

  • 期刊论文

  • 发表日期：

    2026年3月23日

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  • Mohamed El Amine Gorine,
  • Lyes Nechak,
  • Mohamed Ichchou,
  • Yann Pichenot

摘要

This paper reviews the current applications of artificial intelligence (AI) and machine learning (ML) techniques in industrial manufacturing processes. It provides a comprehensive overview of the three main learning paradigms, supervised, unsupervised, and reinforcement learning and maps their algorithmic families to four major process domains: forming, machining, joining, and additive manufacturing. For each domain, sensor modalities, data characteristics, data acquisition feasibility, and control challenges are analyzed to assess the conditions influencing ML adoption. Differences in maturity levels are shown to arise primarily from variations in data availability, sensor integration complexity, and process automation readiness. The review offers an application-oriented synthesis covering predictive maintenance, real-time process optimization, and automated quality inspection. A publication-weighted maturity matrix demonstrates that supervised learning is well established in machining and additive manufacturing, where standardized sensors and abundant labeled data enable mature AI solutions. By contrast, unsupervised methods are commonly applied for anomaly detection in domains with limited labeled data, such as joining. Reinforcement learning is emerging as a viable approach for closed-loop control, particularly in contexts where reliable digital twins and robust simulation environments support model training. Cross-cutting techniques including physics-informed learning, explainable AI, multi-fidelity surrogate modeling, and edge-to-cloud computing architectures are also discussed. The paper concludes with a proposed future research agenda emphasizing the development of open benchmark datasets, improved simulator fidelity, and interpretable hybrid models, with the broader goal of bridging the gap between academic research and industrial deployment.