Artificial Intelligence in Mechanical Engineering: Foundations, Opportunities, Challenges, and Responsibilities

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.

人工智能（AI）正在重塑机械工程（ME），涵盖了从建模、仿真和优化到诊断、设计和技术交流的各个方面。本文基于第28届巴西机械工程大会（COBEM 2025）的一次圆桌会议，为巴西及国际机械工程界提供了一个结构化的视角。

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.

本文并非详尽的综述，而是探讨了人工智能如何在不损害科学严谨性的前提下增强工程实践。我们区分了统计学习与人类认知，并讨论了人工智能的能力、局限性以及新兴的代理形式。

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.

随后，我们考虑了工业应用、劳动力变革和人工智能辅助的科学发现，以及人工智能计算对能源和基础设施的需求，及其对可持续性和热管理的影响。讨论强调了将物理知识、结构化推理和领域专业知识整合到人工智能系统中，特别是在与安全相关的应用中。

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.

主题包括物理信息学习与混合学习、物理约束建模、验证、不确定性量化和认证。我们还审视了教育、认识论和伦理方面的挑战，包括课程改革、人机协作、职业责任以及工程知识的保存。

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.

最后，我们总结了在机械工程中负责任地采用人工智能的原则：物理一致性、严格验证、明确处理不确定性、能源可持续性、人工监督和伦理责任。人工智能应当补充而非取代工程判断。