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

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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.