Author:
Broeckmann Christoph,Bührig-Polaczek Andreas,Hallstedt Bengt,Krupp Ulrich,Rajaei Ali,Rom Michael,Rudack Maximilian,Schmitz Georg J.,Wesselmecking Sebastian
Abstract
AbstractMaterials serve as the foundation of the technical framework on which modern society relies every day. Generations have developed new materials, tried to understand the origins of their properties, and found ways to predict them. Modern computational tools have vastly expanded our capabilities to make predictions, not only of material properties but also of component properties and of the component health status over its life cycle. Integrated Computational Materials Engineering (ICME) aims at simulating the material and component properties along the complete process chain and across the length scales from microstructure to component scale. In this way a digital twin of the material or component can be generated, which can be leveraged to facilitate gains in productivity and service life of technical systems. By reducing the complexity of models for the digital twin where necessary, combining them with in-process data using innovative sensor technology and suitable mathematically driven approximation procedures such as machine learning, it is possible to conceive a digital material shadow that resolves elements of the dilemma between data granularity, data volume, and processing speed to enable process monitoring and control for materials processing. To enable communication between humans and machines it is necessary to create a strictly defined language in the form of ontologies. Ontologies are typically domain-specific, but care must be taken to make them consistent across domains. Integrated Structural Health Engineering (ISHE) aims at predicting and monitoring the health state of components over their entire life cycle, enabling timely replacement of components and avoiding costly and possibly life-threatening failures. In particular when components are subjected to cyclic loading, their structural health does not primarily depend on the average material properties, but on the presence of more or less statistically distributed defects. These defects are intrinsic to materials processing, cannot be completely avoided, and evolve during various stages of the production process. The objective of ISHE is to predict their formation and evolution during the production process and their impact on the component structural health during its life cycle. It is clear that the material and component properties are strongly dependent on the process by which they are produced. Therefore, many of the topics discussed in this part have relational counterparts in Part IV: Production.
Publisher
Springer International Publishing
Reference59 articles.
1. Aluminium Alloy (2022) https://en.wikipedia.org/wiki/Aluminium_alloy. Accessed 29 April 2022
2. Ansys Granta (2022) https://grantadesign.com/industry/products/data/. Accessed 29 April 2022
3. Arp R, Smith B, Spear AD (2015) Building ontologies with basic formal ontology. MIT, Cambridge
4. Ashino T (2010) Materials ontology: an infrastructure for exchanging materials information and knowledge. Data Sci J 9:54–61. https://doi.org/10.2481/dsj.008-041
5. Atkin A (2010) Peirce’s theory of signs. https://plato.stanford.edu/entries/peirce-semiotics/. Accessed 29 April 2022
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献