JCERSM | 第 86 期学术讲座: 制造工程中不确定性建模的有效数值方法 | 主讲人: Matthias Faes

Numerical tools to approximate the solution of (sets of) differential equations have become essential in modern manufacturing processes, ranging from composite manufacturing to metal forming and additive manufacturing. These tools enable engineers to design, simulate, and optimize components long before the first physical prototype is made. However, discrepancies often arise between these high-fidelity simulations and the actual performance of manufactured components. At the core of this issue lies uncertainty in material behaviour, process variability, and the governing parameters involved in the models used for simulation.

Uncertainties are especially significant in manufacturing processes where material properties, load paths, and boundary conditions are difficult to predict with precision. For example, in composite manufacturing, the complex interplay between fiber orientation, matrix properties, and manufacturing-induced defects introduces significant uncertainty. In metal forming, variability in material properties and forming conditions can lead to deviations in part quality. Similarly, additive manufacturing, particularly when combined with topology optimization, presents challenges in predicting the mechanical properties and durability of the printed structure due to variability in printing conditions and material inconsistencies. These uncertainties cannot always be described crisply, and standard probabilistic models may fail to account for the incomplete or conflicting data available (i.e., epistemic uncertainty).

In this research seminar, I will discuss strategies to model these manufacturing uncertainties under limited data. Specifically, I will show how to define and model imprecise stochastic processes that are robust to missing or conflicting information, and I will present efficient methodologies to propagate these uncertainties through manufacturing simulations. These approaches will be demonstrated in the context of composite material design, precision metal forming, and additive manufacturing with topology optimization, highlighting the impact of uncertainty modelling on process optimization and final product reliability.

Matthias Faes became a full Professor in Reliability Engineering at TU Dortmund at the age of 30, since February 2022. Before, he was a post-doctoral fellow of the Research Foundation Flanders (FWO) working at the Department of Mechanical Engineering of KU Leuven, and was also affiliated to the Institute for Risk and Reliability at the University of Hannover as Humboldt Fellow. He graduated summa cum laude as Master of Science in Engineering Technology in 2013 and obtained his PhD in Engineering Technology from KU Leuven in 2017. Since then, he is working on advanced methodologies for non-probabilistic uncertainty quantification under scarce data and information, including inverse and data-driven methods, stochastic fields and interval techniques. He is a Laureate of the 2017 PhD award of the Belgian National Committee for Applied and Theoretical Mechanics, winner of the 2017 ECCOMAS European PhD award for best PhD thesis in 2017 on computational methods in applied sciences and engineering in Europe, winner of the 2019 ISIPTA - IJAR Young Researcher Award for outstanding contributions to research on imprecise probabilities and the 2023 EASD Junior Research Prize for his contribution to the development of methodologies for structural dynamics, among other awards. He is editor at Mechanical Systems and Signal Processing and Associate Managing Editor of the ASCE-ASME Journal of Risk and Uncertainty in Engineering system parts A and B, among other journals. Matthias Faes is author of more than 85 journal papers and more than 80 conference contributions and he has a Google Scholar H-index of 25 (2300+ citations) since 2016.