Taylored Material Properties via Microstructure Optimization: Machine Learning for Modelling and Inversion of Structure-Property-Relationships and their Application to Sheet Metals

用于生产具有某些所需性能的材料的工艺控制动作的推导是因果链“工艺控制”--“微观结构实例化”--“材料性能”的“逆问题”。拟议项目的主要目标是利用机器学习领域的现代方法为解决这一问题创造一个新的基础。该项目将由两个明确分离的部分组成：“性能-结构-映射”和“结构引导的最优过程控制”。该项目的重点在于研究和开发能够对材料的结构-性能-关系进行反演的方法，这些方法在行业中是相关的。这种反转是微结构设计和相关生产过程的最佳控制的基础。另一个目标是开发最优控制方法，准确地产生那些具有所需特性的结构。开发的通用方法如何用来解决具有特定性质的材料的生产的具体反问题，将通过将它们应用于板材生产过程来展示。其核心目标包括开发技术上相关的“结构-属性-映射”的倒置方法，以及通过监督和非监督机器学习有效地表示微观结构的方法。基于强化学习的自适应加工路径优化方法将被开发用于制造过程的自适应优化控制。我们预计，这项工作的结果将导致对材料的技术相关过程-结构-性能-关系的深入了解。该项目产生的仪器还将促进新材料和工艺控制的经济高效开发。

The derivation of processing control actions for the production of materials with certain, desired properties is the "inverse problem" of the causal chain "process control" - "microstructure instantiation" - "material properties". The main goal of the proposed project is the creation of a new basis for the solution of this problem by using modern approaches from the field of Machine Learning. The inversion will be composed of two explicitely separated parts: "Property-Structure-Mapping" and "Structure-guided Optimal Process Control".The focus of the project lies on the investigation and development of methods which allow an inversion of the structure-property-relations of materials, which are relevant in the industry. This inversion is the basis for the design of microstructures and for the optimal control of the related production processes. Another goal is the development of optimal control methods yielding exactly those structures which have the desired properties. How the developed generic methods are used to solve a concrete inverse problem of the production of a material with dedicated properties, will be shown by applying them to sheet metal production processes. The core goals include the development of methods for inverting technologically relevant "Structure-Property-Mappings" and methods for efficient microstructure representation by supervised and unsupervised machine learning. Adaptive processing-path-optimization methods, based on reinforcement learning, will be developed for adaptive optimal control of manufacturing processes. We expect that the results of this work will lead to an increasing insight into technological relevant process-structure-property-relationships of materials. The instruments resulting from the project will also promote the economically efficient development of new materials and process controls.