物理与数据双驱动的材料力学AI教学实践

Teaching Practice of AI-Empowered Materials Mechanics Driven by Both Physical Laws and Data

摘要: 目的：针对材料力学课程性能参数获取方式单一、教学内容与工程前沿脱节、学生参与度不高的问题，探索人工智能(AI)与力学理论教学深度融合的路径。方法：提出物理规律与数据双驱动融合的改革方案，确立物理优先、精选融合、反向强化三条原则，在绪论、轴向拉压等5个章节嵌入AI应用，并以轴向拉压为案例构建铁基与铝合金数据集，开发可交互演示文件与操作指南。结果：梯度提升树的预测精度最高(R² = 0.93)；碳含量、晶粒尺寸和热处理温度依次主导屈服强度，与固溶强化、Hall-Petch关系和回火软化规律一致；铁基模型误用于铝合金时预测完全失效，揭示了忽视材料体系物理差异的风险。结论：方案不压缩理论课时、不要求编程基础，可为工科基础课程智能化教学改革提供可复用范例。

Abstract: Objective: Aiming at the problems of the single means of obtaining material property parameters, the disconnection between teaching content and engineering frontiers, and the insufficient participation of students in the course of Materials Mechanics, this study explores a feasible path for the deep integration of artificial intelligence technology into the teaching of mechanics theory. Methods: A teaching reform scheme integrating physics-driven and data-driven paradigms is proposed. Three design principles are established, namely physics first, selective integration and reverse reinforcement. Artificial intelligence applications are embedded into five chapters, including Introduction, Axial Tension and Compression, Stress State Analysis, Strength Theory, and Buckling Stability, without occupying additional class hours. Taking the chapter of axial tension and compression as a representative case, teaching datasets of iron-based alloys and aluminum alloys are constructed, and an interactive demonstration notebook together with a teacher’s operation guide are developed. Linear regression, random forest and gradient boosting models are trained and compared on an independent test set, and the feature importance obtained from the model is systematically compared with classical strengthening theories. Results: Gradient boosting achieves the highest prediction accuracy (R² = 0.93), followed by linear regression and random forest. The feature importance analysis shows that carbon content, grain size and heat treatment temperature dominate the yield strength successively, which is highly consistent with the theory of solid solution strengthening, grain refinement strengthening and temper softening, and the parameter sensitivity analysis further verifies the above conclusions. When the iron-based model is deliberately misapplied to aluminum alloy data, the prediction fails completely, and the comparison between correct and incorrect modeling intuitively reveals the risk of ignoring the physical differences between material systems. Trials by peer teachers indicate that the resource package can be independently deployed within a short preparation time and is regarded as highly reusable. Conclusion: The proposed scheme neither compresses the class hours of mechanics theory nor requires students to have a programming background. It shifts the focus of AI-empowered teaching from what artificial intelligence can do to how to use artificial intelligence correctly, and provides a reusable example for the intelligent teaching reform of fundamental engineering courses.