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Applications of artificial intelligence in engineering and manufacturing: a systematic review

Author

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  • Isaac Kofi Nti

    (Sunyani Technical University
    University of Energy and Natural Resources)

  • Adebayo Felix Adekoya

    (University of Energy and Natural Resources)

  • Benjamin Asubam Weyori

    (University of Energy and Natural Resources)

  • Owusu Nyarko-Boateng

    (University of Energy and Natural Resources)

Abstract

Engineering and manufacturing processes and systems designs involve many challenges, such as dynamism, chaotic behaviours, and complexity. Of late, the arrival of big data, high computational speed, cloud computing and artificial intelligence techniques (like machine learning and deep learning) has reformed how many engineering and manufacturing professionals approach their work. These technologies offer thrilling innovative ways for engineers and manufacturers to tackle real-life challenges. On the other hand, the field of Artificial Intelligence (AI) is extensive. Several diverse theories, algorithms, and methods are available, which presents a challenge and a barrier in choosing the right AI technique for the appropriate engineering process or manufacturing process and environments. Besides, the pertinent literature is disseminated over various journals, conference proceedings, and research communities. Hence, conducting a systematic survey to scrutinise and classify the existing literature is worthwhile. However, it is challenging, but previous review studies have not adequately addressed AI’s use and advancement in engineering and manufacturing (EM). Besides, some concentrated on single AI models, and others focused on a specific area in EM. This paper presents a comprehensive systematic review of studies on AI and its application in EM. To limit the scope of the current study, we conducted a keyword search in official publisher websites and academic databases, such as Springer, Elsevier, Scopus, Science Publication, Taylor & Francis, Directory of Open Access Journals (DOAJ), Association for Computing Machinery (ACM), Wiley online library, Inderscience and Google scholar. The search results (173 articles) were filtered according to a proposed framework, which resulted in ninety-one (91) relevant research articles. We reviewed the articles based on a proposed taxonomy (the year of publication, the AI algorithm and machine learning task adopted, the application area in EM, the train and test split of data, the error, and accuracy metrics used, the potential benefits). Our assessment using the proposed taxonomy gave a helpful insight into the literature’s anatomy on various AI applications in engineering and manufacturing. Also, we identified opportunities for future research in AI application in the field of EM.

Suggested Citation

  • Isaac Kofi Nti & Adebayo Felix Adekoya & Benjamin Asubam Weyori & Owusu Nyarko-Boateng, 2022. "Applications of artificial intelligence in engineering and manufacturing: a systematic review," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1581-1601, August.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:6:d:10.1007_s10845-021-01771-6
    DOI: 10.1007/s10845-021-01771-6
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    References listed on IDEAS

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    1. Thi-Kien Dao & Tien-Szu Pan & Trong-The Nguyen & Jeng-Shyang Pan, 2018. "Parallel bat algorithm for optimizing makespan in job shop scheduling problems," Journal of Intelligent Manufacturing, Springer, vol. 29(2), pages 451-462, February.
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    5. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
    6. Chengdong Li & Zixiang Ding & Dongbin Zhao & Jianqiang Yi & Guiqing Zhang, 2017. "Building Energy Consumption Prediction: An Extreme Deep Learning Approach," Energies, MDPI, vol. 10(10), pages 1-20, October.
    7. Lage Junior, Muris & Godinho Filho, Moacir, 2010. "Variations of the kanban system: Literature review and classification," International Journal of Production Economics, Elsevier, vol. 125(1), pages 13-21, May.
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    2. Iñigo Flores Ituarte & Suraj Panicker & Hari P. N. Nagarajan & Eric Coatanea & David W. Rosen, 2023. "Optimisation-driven design to explore and exploit the process–structure–property–performance linkages in digital manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 219-241, January.

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