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Machine learning in manufacturing and industry 4.0 applications

Author

Listed:
  • Rahul Rai
  • Manoj Kumar Tiwari
  • Dmitry Ivanov
  • Alexandre Dolgui

Abstract

The machine learning (ML) field has deeply impacted the manufacturing industry in the context of the Industry 4.0 paradigm. The industry 4.0 paradigm encourages the usage of smart sensors, devices, and machines, to enable smart factories that continuously collect data pertaining to production. ML techniques enable the generation of actionable intelligence by processing the collected data to increase manufacturing efficiency without significantly changing the required resources. Additionally, the ability of ML techniques to provide predictive insights has enabled discerning complex manufacturing patterns and offers a pathway for an intelligent decision support system in a variety of manufacturing tasks such as intelligent and continuous inspection, predictive maintenance, quality improvement, process optimisation, supply chain management, and task scheduling. While different ML techniques have been used in a variety of manufacturing applications in the past, many open questions and challenges remain, from Big data curation, storage, and understanding, data reasoning to enable real-time actionable intelligence to topics such as edge computing and cybersecurity aspects of smart manufacturing. Hence, this special issue is focused on bringing together a wide range of researchers to report the latest efforts in the fundamental theoretical as well as experimental aspects of ML and their applications in manufacturing and productionsystems.

Suggested Citation

  • Rahul Rai & Manoj Kumar Tiwari & Dmitry Ivanov & Alexandre Dolgui, 2021. "Machine learning in manufacturing and industry 4.0 applications," International Journal of Production Research, Taylor & Francis Journals, vol. 59(16), pages 4773-4778, August.
    • Handle: RePEc:taf:tprsxx:v:59:y:2021:i:16:p:4773-4778
    DOI: 10.1080/00207543.2021.1956675
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    Citations

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    Cited by:

    1. Govindan, Kannan & Kannan, Devika & Jørgensen, Thomas Ballegård & Nielsen, Tim Straarup, 2022. "Supply Chain 4.0 performance measurement: A systematic literature review, framework development, and empirical evidence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    2. Daniel Fernández & Álvaro Rodríguez-Prieto & Ana María Camacho, 2024. "Data-Analytics-Driven Selection of Die Material in Multi-Material Co-Extrusion of Ti-Mg Alloys," Mathematics, MDPI, vol. 12(6), pages 1-22, March.
    3. Tsan-Ming Choi & Alexandre Dolgui & Dmitry Ivanov & Erwin Pesch, 2022. "OR and analytics for digital, resilient, and sustainable manufacturing 4.0," Annals of Operations Research, Springer, vol. 310(1), pages 1-6, March.
    4. Yu Yao & Quan Qian, 2024. "Dynamic Industrial Optimization: A Framework Integrates Online Machine Learning for Processing Parameters Design," Future Internet, MDPI, vol. 16(3), pages 1-17, March.
    5. Jiuh‐Biing Sheu & Tsan‐Ming Choi, 2023. "Can we work more safely and healthily with robot partners? A human‐friendly robot–human‐coordinated order fulfillment scheme," Production and Operations Management, Production and Operations Management Society, vol. 32(3), pages 794-812, March.
    6. Sachin Kumar & T. Gopi & N. Harikeerthana & Munish Kumar Gupta & Vidit Gaur & Grzegorz M. Krolczyk & ChuanSong Wu, 2023. "Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 21-55, January.
    7. Alisha Lakra & Shubhkirti Gupta & Ravi Ranjan & Sushanta Tripathy & Deepak Singhal, 2022. "The Significance of Machine Learning in the Manufacturing Sector: An ISM Approach," Logistics, MDPI, vol. 6(4), pages 1-15, October.
    8. Chauhan, Ruchi & Majumder, Arunava & Kumar, Varun, 2023. "The impact of adopting customization policy and sustainability for improving consumer service in a dual-channel retailing," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).

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