Artificial Intelligence in Mechanical Engineering: Foundations, Opportunities, Challenges, and Responsibilities
Authors: A. Cunha, C. Nataraj, L. M. Lorenzo, H. Panetto, A. Scotti, J. V. C. Vargas
Organizations: National Laboratory of Scientific Computing, Av. Getúlio Vargas, 333, Petrópolis, 25651-075, RJ, Brazil · Rio de Janeiro State University, Rua São Francisco Xavier, 524, Rio de Janeiro, 20550-900, RJ, Brazil · Moritz Endowed Professor, Villanova Center for Analytics of Dynamic Systems (VCADS), Villanova University, Villanova, PA 19085, USA · Dassault Systèmes, Av. Jornalista Roberto Marinho, 85, São Paulo, 04576-010, SP, Brazil · University of Lorraine, CNRS, CRAN, Nancy, F-54000, France · Federal University of Uberlândia, Rodovia BR-050, km 78 - Bloco 1DCG, Uberlândia, 38410-337, MG, Brazil · Federal University of Paraná, CP 19011, Curitiba, 81531–980, PR, Brazil
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.
Figures & tables
Figure 1: System-level view of artificial intelligence in ME. Engineering applications are enabled by AI methodologies, including machine learning, physics-informed learning, generative models, and emerging agentic systems that coordinate simulations, optimization, and data-driven workflows. These capabilities remain grounded in physical laws, domain knowledge, experimental evidence, energy infrastructure, and governance frameworks. Validation, uncertainty quantification, certification, and traceability act as cross-cutting requirements across all layers, ensuring that AI systems can be deployed responsibly in engineering practice. The diagram emphasizes that AI functions as an embedded component of engineering systems rather than as an autonomous substitute for engineering judgment.
Figure 2: Conceptual architecture of a human-agent engineering workflow. Engineering objectives are pursued through collaboration between a human engineer and an agentic AI system, which interact with an ecosystem of computational tools including CAD models, physics solvers, optimization engines, engineering databases, sensors, and digital twins. The agentic AI system assists with planning, reasoning, information retrieval, and workflow orchestration, while the human engineer provides domain expertise, physical reasoning, engineering judgment, validation, and accountability. Intermediate results generated throughout the workflow undergo verification, validation, and uncertainty quantification before supporting engineering decisions and deployment. Physical constraints, governance requirements, and ethical considerations act as cross-cutting influences across all stages. The diagram emphasizes that agentic AI can coordinate complex engineering workflows, but trustworthy deployment ultimately depends on human oversight, physical consistency, uncertainty awareness, and professional responsibility.
Figure 3: Schematic representation of the energy flow and infrastructure requirements of AI-driven data centers. Electrical power from primary sources is delivered through distribution and conditioning systems to high-density computing hardware (GPUs, CPUs, and networking equipment), which generate substantial heat loads. Thermal management subsystems—including liquid- and air-cooling—require additional electrical power, thereby increasing total facility power demand and shaping the scalability and sustainability of AI infrastructure.
Figure 4: Conceptual framework for AI-enabled ME education. Future engineers must integrate four complementary domains of competence: (i) strong engineering fundamentals grounded in physical laws, modeling, and analysis; (ii) artificial intelligence and data-driven methods; (iii) ethical awareness and professional responsibility; and (iv) human-agent teaming and agentic workflow supervision. The latter includes competencies such as physics-aware interaction with AI systems, supervision of AI-assisted workflows, inspection and validation of AI-generated analyses, and critical assessment of intermediate and final computational results. The integration of these domains enables the responsible, effective, and trustworthy use of AI in engineering practice.
Responsible AI studies how to design, evaluate, and govern AI systems to promote fairness, safety, transparency, accountability, privacy, and sustainability.
This work examines how AI, especially agentic systems, is being adopted in engineering and manufacturing workflows, what value it provides today, and what is needed for broader deployment. This is an exploratory and qualitative state-of-practice study grounded in over 30 interviews across four stakeholder groups (large enterprises, small/medium firms, AI developers, and CAD/CAM/CAE vendors). We find that near-term AI gains cluster around structured, repetitive work and data-intensive synthesis, while higher-value agentic gains come from orchestrating multi-step workflows across tools. Adoption is constrained less by model capability than by fragmented and machine-unfriendly data, stringent security and regulatory requirements, and limited API-accessible legacy toolchains. Reliability, verification, and auditability are central requirements for adoption, driving human-in-the-loop frameworks and governance aligned with existing engineering reviews. Beyond technical barriers there are also organizational ones: a persistent AI literacy gap, cultural heterogeneity, and governance structures that have not yet caught up with agentic capabilities. Together, the findings point to a staged progression of AI utility from low-consequence assistance toward higher-order automation, as trust, infrastructure, and verification mature. This highlights key breakthroughs needed, including integration with traditional engineering tools and data types, robust verification frameworks, and improved spatial and physical reasoning.
Kristen M. Edwards, Maxwell Bauer, Claire Jacquillat +2
The rapid maturation of artificial intelligence (AI) and machine learning (ML) has catalyzed a profound shift in how chemical engineering problems are formulated, analyzed, and solved. Advances in computing, data availability, and learning algorithms have enabled AI/ML methods to impact applications spanning atomic-scale simulations, materials and catalyst discovery, transport and thermodynamics, separations, process systems engineering, and industrial operations. This article provides a perspective on recent methodological developments and representative applications, emphasizing how AI/ML tools are being integrated with first-principles models to address challenges of predictive accuracy, data scarcity, extrapolation, interpretability, and model lifecycle management. Across domains, a unifying trend is the move away from purely black-box approaches toward hybrid and physics-informed frameworks that explicitly respect conservation laws, thermodynamic consistency, and known structural constraints. These approaches not only improve robustness and reliability, but also enable meaningful human-AI collaboration by providing information at an appropriate level of abstraction for the task and decision context. We conclude that AI and ML are not replacing the core principles of chemical engineering; rather, they are amplifying them. As the field advances toward increasingly autonomous, adaptive, and sustainable systems, the thoughtful integration of AI/ML with first-principles understanding and domain expertise will be essential to realizing their full potential across both research and industrial practice.
Michael Baldea, Linda J. Broadbelt, Marianthi G. Ierapetritou +17
McKetta Department of Chemical Engineering and Oden Institute for Chemical Engineering and Sciences, The University of Texas at Austin, Austin, TX 78731, USA · Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA · Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, USA +13
Engineering workflows such as design optimization, simulation-based diagnosis, control tuning, and model-based systems engineering (MBSE) are iterative, constraint-driven, and shaped by prior decisions. Yet many AI methods still treat these activities as isolated tasks rather than as parts of a broader workflow. This paper presents Agentic Engineering Intelligence (AEI), an industrial vision framework that models engineering workflows as constrained, history-aware sequential decision processes in which AI agents support engineer-supervised interventions over engineering toolchains. AEI links an offline phase for engineering data processing and workflow-memory construction with an online phase for workflow-state estimation, retrieval, and decision support. A control-theoretic interpretation is also possible, in which engineering objectives act as reference signals, agents act as workflow controllers, and toolchains provide feedback for intervention selection. Representative automotive use cases in suspension design, reinforcement learning tuning, multimodal engineering knowledge reuse, aerodynamic exploration, and MBSE show how diverse workflows can be expressed within a common formulation. Overall, the paper positions engineering AI as a problem of process-level intelligence and outlines a practical roadmap for future empirical validation in industrial settings.
Tong Duy Son, Zhihao Liu, Piero Brigida +4
The authors are with Siemens Digital Industries Software, Leuven, Belgium.