[Submitted on 22 Oct 2026]
Title:Artificial Intelligence in Mechanical Engineering: Foundations, Opportunities, Challenges, and Responsibilities
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Abstract:Artificial intelligence (AI) is reshaping mechanical engineering (ME), from modeling, simulation, and optimization to diagnostics, design, and technical communication. Drawing on a round-table at the 28th ABCM International Congress on Mechanical Engineering (COBEM 2025), this article presents a structured perspective for the Brazilian and international ME communities. Rather than an exhaustive survey, it examines how AI can enhance engineering practice without compromising scientific rigor. We distinguish statistical learning from human cognition and discuss the capabilities, limitations, and emerging agentic forms of AI. We then consider industrial adoption, workforce changes, and AI-assisted scientific discovery, alongside the energy and infrastructure demands of AI computation and their implications for sustainability and thermal management. The discussion emphasizes the integration of physical knowledge, structured reasoning, and domain expertise into AI systems, particularly in safety-relevant applications. Topics include physics-informed and hybrid learning, physically constrained modeling, validation, uncertainty quantification, and certification. We also examine educational, epistemological, and ethical challenges, including curriculum reform, human-agent collaboration, professional accountability, and the preservation of engineering knowledge. We conclude with principles for responsible AI adoption in ME: physical consistency, rigorous validation, explicit treatment of uncertainty, energy sustainability, human oversight, and ethical responsibility. AI should complement, not replace, engineering judgment.
Subjects:
Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG); Applications (stat.AP)
MSC classes: 68T01, 68T05, 68T37, 68T42 68T42, 68T05, 68T37, 68T42
ACM classes: I.2.0; I.2.1; J.2; K.7.4; K.3.2
Cite as: arXiv:2610.33105 [cs.AI]
(or arXiv:2610.33105v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.33105
arXiv-issued DOI via DataCite (pending registration)
Journal reference: J. Braz. Soc. Mech. Sci. Eng. 48, 741 (2026)
Related DOI:
https://doi.org/10.1007/s40430-026-06873-x
DOI(s) linking to related resources
Submission history
From: Americo Cunha Jr [view email] [v1] Thu, 22 Oct 2026 17:02:41 UTC (7,048 KB)
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