arXiv cs.AI· A. Cunha Jr, C. Nataraj, L. M. Lorenzo, H. Panetto, A. Scotti, J. V. C. Vargas·· 12 小时前AI 评分20
人工智能在机械工程中的应用：基础、机遇、挑战与责任
Artificial Intelligence in Mechanical Engineering: Foundations, Opportunities, Challenges, and Responsibilities
AI 导读
基于第28届ABCM国际机械工程大会（COBEM 2025）圆桌讨论，该文系统梳理了AI在机械工程建模、仿真、优化、诊断与设计中的应用前景。文章区分统计学习与人类认知，讨论智能体形态AI的能力与局限，并强调将物理知识、结构化推理与领域专长融入AI系统，尤其在安全相关应用中。
正文
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) |
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| 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)
来源：arXiv cs.AI · arxiv.org