Engineering

A Literature Review on Machine Learning Applications in Mechanical Engineering

Authors

  • Jiayuan Song

    North China University of Technology; Brunel University London
    Author

  • Zonghan Yu

    School of Mechanical and Materials Engineering, North China University of Technology
    Author

  • Xinli Du

    Department of Mechanical and Aerospace Engineering, College of Engineering, Design and Physical Sciences, Brunel University London
    Author

DOI:

https://doi.org/10.71107/n4zczk50

Keywords:

Machine Learning, Predictive Maintenance, Material Property Analysis, Optimization Design

Abstract

The increasing difficulty in accurately predicting equipment failures and optimizing complex mechanical designs has become a critical challenge for engineers working in modern manufacturing and industrial systems. Although machine learning (ML) offers promising solutions, its real-world impact across different areas of mechanical engineering remains insufficiently explored. This study aims to evaluate the effectiveness of ML techniques in addressing key engineering problems, particularly in predictive maintenance, additive manufacturing, and material property prediction. Drawing on 78  peer-reviewed articles published between 2015 and 2024, this work adopts a structured literature review approach, focusing on high-impact applications of supervised, unsupervised, and reinforcement learning methods. The findings reveal that ML-driven predictive maintenance can reduce equipment downtime by up to 30%, while generative design and surrogate modeling accelerate simulation processes by over 40%. Additionally, ML-enhanced defect detection in 3D printing improves accuracy by at least 20%. These insights highlight the growing potential of ML to transform mechanical engineering practices. This study provides engineers, researchers, and decision-makers with a concise, evidence-based understanding of how data-driven technologies can lead to smarter design, improved efficiency, and lower operational costs in engineering systems. 

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Author Biographies

  • Jiayuan Song, North China University of Technology; Brunel University London

    Mechanical Design, Manufacture and Automation BEng, Department of Mechanical and Materials Engineering, North China University of Technology;
    Mechanical Engineering BEng, Department of Mechanical and Aerospace Engineering, College of Engineering, Design and Physical Sciences,Brunel University London

  • Zonghan Yu, School of Mechanical and Materials Engineering, North China University of Technology

    Associate Professor, School of Mechanical and Materials Engineering, North China University of Technology

  • Xinli Du, Department of Mechanical and Aerospace Engineering, College of Engineering, Design and Physical Sciences, Brunel University London

    Department of Mechanical and Aerospace Engineering, College of Engineering, Design and Physical Sciences, Brunel University London https://www.brunel.ac.uk/people/xinli-du

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Published

2026-05-08

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Vol. 2 No. 1 (2026): Conclusions in Engineering

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Copyright (c) 2026 Jiayuan Song, Zonghan Yu, Xinli Du (Author)

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A Literature Review on Machine Learning Applications in Mechanical Engineering. (2026). Conclusions in Engineering, 2(1), 77-90. https://doi.org/10.71107/n4zczk50

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