Global Journal of Engineering and Technology Research (GJETR) · Research article · Open access
Theoretical Framework for Integrating Artificial Intelligence into Mechanical Engineering: A Conceptual Review and Future Perspectives
Ali F. Ali FADIEL, Moktar A Hashem Mohamad
30 October 2025 · Vol. 1, Issue 2, pp. 75-81
DOI: 10.65150/EP-gjetr/V1E2/2025-05
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What is this article about?
Machine artificial intelligence (AI) transforms design, optimization, predictive maintenance, and control systems. Even the systematic unification of AI and the laws of physics governing the mechanical systems can be clearly observed as a gap, despite the significant progress of the field of discrete applications.
Article at a glance
- Published
- 30 October 2025
- Volume / issue / pages
- Vol. 1, Issue 2, pp. 75-81
- ISSN
- 3051-3782 (online) · 3051-3774 (print)
- Licence
- CC BY 4.0 — free to read and reuse with attribution
- Peer review
- Double-blind, at least two independent reviewers
- Publisher
- EP Journals Group
Abstract
Machine artificial intelligence (AI) transforms design, optimization, predictive maintenance, and control systems. Even the systematic unification of AI and the laws of physics governing the mechanical systems can be clearly observed as a gap, despite the significant progress of the field of discrete applications. The paper follows a conceptual and analytical review approach, which involves the synthesis of recent developments into a stratified theoretical model that has not been directly experimented with or industrialized. This paper fills this gap by conceptualizing recent developments and a layered theoretical structure for implementing AI in mechanical engineering. The framework is structured into four layers: (1) the input layer, which consolidates experimental, simulation, and historical operational data; (2) the AI modeling layer, which applies machine learning, deep learning, and evolutionary algorithms; (3) the hybrid physics–AI layer, which integrates data-driven approaches with governing physical equations through methods such as physics-informed neural networks and surrogate modeling; and (4) the output layer, which delivers optimized design, predictive maintenance, accelerated simulations, and adaptive control strategies. This study reiterates building models that strike a compromise between data-driven methodologies and physical interpretability in an effort to improve innovation and reliability. For the future of the field, this and other viewpoints stress the significance of digital twins, explainable AI, sustainable engineering application cases, and interdisciplinary work synergies. This framework positions AI systems as essential catalysts for the upcoming generation of mechanical engineering. The framework can be used for upcoming studies and business ventures because conceptualizations and foundations are balanced.
Keywords: Hybrid Physics–AI Models, Predictive Maintenance, Digital Twins, Explainable AI, Optimization, Computational Modeling
Cite this article
Ali F. Ali FADIEL, & Moktar A Hashem Mohamad (2025). Theoretical Framework for Integrating Artificial Intelligence into Mechanical Engineering: A Conceptual Review and Future Perspectives. Global Journal of Engineering and Technology Research, 1(2), 75-81. https://doi.org/10.65150/EP-gjetr/V1E2/2025-05
@article{Ali2025,
title = {Theoretical Framework for Integrating Artificial Intelligence into Mechanical Engineering: A Conceptual Review and Future Perspectives},
author = {Ali F. Ali FADIEL and Moktar A Hashem Mohamad},
journal = {Global Journal of Engineering and Technology Research},
year = {2025},
volume = {1},
number = {2},
pages = {75-81},
doi = {10.65150/EP-gjetr/V1E2/2025-05},
url = {https://doi.org/10.65150/EP-gjetr/V1E2/2025-05}
}Questions about this article
Who wrote this article?
Ali F. Ali FADIEL, Moktar A Hashem Mohamad.
Where and when was it published?
In Global Journal of Engineering and Technology Research, Volume 1, Issue 2, pages 75-81, published by EP Journals Group on 30 October 2025.
Is this article free to read?
Yes. It is open access under a Creative Commons Attribution 4.0 (CC BY 4.0) licence, so anyone can read, share and reuse it with attribution.
What is the DOI of this article?
10.65150/EP-gjetr/V1E2/2025-05 (https://doi.org/10.65150/EP-gjetr/V1E2/2025-05).
How do I cite this article?
APA: Ali F. Ali FADIEL, & Moktar A Hashem Mohamad (2025). Theoretical Framework for Integrating Artificial Intelligence into Mechanical Engineering: A Conceptual Review and Future Perspectives. Global Journal of Engineering and Technology Research, 1(2), 75-81. https://doi.org/10.65150/EP-gjetr/V1E2/2025-05
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