In the context of Industry 4.0 and general digitalization along the value chain, advanced modelling and simulation methods are one of the most important components. The reliable digital twin or cyber-physical system requires validated and predictive computer models more than ever. To meet the demands of ever faster product cycles for customized design and production concepts while using novel materials, the early virtual prototype becomes of utmost importance. This presentation will show some illustrative examples and discuss modern machine learning approaches. Future engineering education will be of particular importance, as it will have to master a skillful balancing act between basic engineering content and computer science competences.

　　Bernd Markert教授于1998年、2005年在德国斯图加特大学分别获学士、博士学位，现任德国亚琛工业大学校长代表、亚琛工业大学校友基金会主席、亚琛工业大学机械学院副院长、RWTH通用力学研究所（IAM）终身教授兼所长，并于2024年成为清华大学荣誉访问教授。主要从事固体力学和生物力学的研究工作，包括微纳米力学、生物材料力学、细观损伤与断裂力学等方面，建立了多孔多相介质材料跨尺度力学理论和相场计算方法，发展现代力学方法与理论在生物组织工程和关键结构健康智能监控中应用。发表期刊论文450多篇，被引用次数超过7000次。

　　Surface protective coatings on magnesium alloys have been developed to control the corrosion rate of biomedical magnesium implants under mechano-chemical loadings. Quantifying the effect of coating’s microstructural features on the corrosion behaviour of magnesium alloys facilitates the innovative design of biodegradable magnesium implants from the surface to the bulk. The presentation focuses on exploring the applicability of deep learning methods for efficiently predicting the in vitro pitting corrosion behaviour of coated magnesium alloys. The proposed machining learning method employs different CNN models for predicting the corrosion curve and the evolution of corrosion interfaces. In the proposed deep learning method, phase field simulations with varying coating microstructures are used to generate the required corrosion datasets for training and validating the models. The method is applied to a PEO-coated WE43 magnesium alloy to assess its feasibility based on in vitro experiments. Performance analysis shows that the multi-input CNN is superior to the single-input CNN in predicting the corrosion curve. The proposed encoder–decoder architecture can predict the evolution of corrosion interfaces with an average error about 1%. These results demonstrate that the proposed CNN models provide a promising alternative to conventional simulation methods for evaluating the protective performance of coatings.

　　马松云博士，德国亚琛工业大学通用力学研究所终身高级研究员，德国亚琛工业大学通用力学学会会长，中车集团长春轨道客车德国中心技术顾问。研究方向为先进结构与材料强度计算，围绕先进材料与结构设计中的前沿实验方法和理论计算，针对材料与结构非线性损伤本构行为，微动磨损，环境交互作用下腐蚀疲劳等课题，开展多尺度分析和疲劳强度方法方向的科研工作。发表论文和专著60余篇。作为项目申请人和负责人主持由德国教育部资助的创新研究项目“医用可降解镁合金腐蚀和疲劳交互作用下评估结构完整性的仿真设计平台”（研究经费总额约200万欧元）。