Artificial Intelligence in Mechanical Engineering: Foundations, Opportunities, Challenges, and Responsibilities
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.
Versions
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BibTeX
@misc{jr2026artificial,
title = {{Artificial Intelligence in Mechanical Engineering: Foundations, Opportunities, Challenges, and Responsibilities}},
author = {A. Cunha Jr and C. Nataraj and L. M. Lorenzo and H. Panetto and A. Scotti and J. V. C. Vargas},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.33105},
eprint = {2610.33105},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.33105},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Artificial Intelligence in Mechanical Engineering: Foundations, Opportunities, Challenges, and Responsibilities
AU - A. Cunha Jr
AU - C. Nataraj
AU - L. M. Lorenzo
AU - H. Panetto
AU - A. Scotti
AU - J. V. C. Vargas
PY - 2026
DA - 2026/10/22
PB - arXiv
DO - 10.48550/arxiv.2610.33105
UR - https://arxiv.org/abs/2610.33105
AB - 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.
ER -