Theoretical Framework for Integrating Artificial Intelligence into Mechanical Engineering: A Conceptual Review and Future Perspectives
DOI:
https://doi.org/10.65150/EP-gjetr/V1E2/2025-05Keywords:
Hybrid Physics–AI Models, Predictive Maintenance, Digital Twins, Explainable AI, Optimization, Computational ModelingAbstract
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.
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Copyright (c) 2025 Ali F. Ali FADIEL, Moktar A Hashem Mohamad (Author)
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