Integrating Deep Learning Into Mechanical Engineering: A Systematic Review of Applications and Educational Implications

Abstract

Deep learning (DL) is reshaping mechanical engineering by offering advanced capabilities for solving complex problems, particularly in fault diagnosis, predictive maintenance, and materials science. While conventional machine learning and physics-based approaches remain prevalent, DL models provide superior performance in terms of accuracy, automation, and adaptability. This systematic review investigates trends in DL applications within mechanical engineering from 2015 to 2024. An initial search using the query “deep learning AND mechanical engineering” across seven major databases—Google Scholar, Web of Science, IEEE Xplore, ERIC, Science Direct, Compendex, and Wiley Online Library—yielded 149 articles. After applying exclusion criteria (published before 2014, non-English, short or work-in-progress papers, not DL and/or mechanical engineering focus, or conceptual papers), 49 studies were selected for in-depth analysis. The results indicate that DL models improve prediction accuracy by 10%–35% over traditional techniques across various applications, including fault detection in rotating machinery and microstructural analysis in materials engineering. Despite notable gains, challenges persist related to data availability, computational intensity, and model interpretability. This review highlights the importance of addressing these limitations and recommends future research efforts toward improving model generalization, incorporating explainable AI techniques, and optimizing DL deployment under limited-data scenarios. Furthermore, the integration of DL with Industry 4.0 technologies—such as IoT, digital twins, and cyber-physical systems—presents a promising direction for real-time, intelligent decision-making in mechanical engineering systems. This review serves as a comprehensive resource for researchers and practitioners seeking to apply or advance DL methods in mechanical engineering contexts.