The focus of the Fraunhofer IWM’s work revolves around material information and material data. Via the digitalization of materials, we achieve important contributions regarding the inclusion of processing materials into digitally consistent and connected value chains.
Creating an appropriate materials data space, in which all specific material information can be digitally managed, automatically accessed and reconstructed with respect to their properties and life-cycle conditions is a key aspect. The materials data space is the precondition for an integration of materials into digitally connected systems. Digital material twins, reconstructed from the materials data space by the means of data analysis tools and material models, enable the transfer of both temporally and locally varying material properties along the product life cycle and beyond the boundaries of individual companies.
Managing the efficiency of production, manufacturing and operating processes can be vastly improved through digital workflows. Only by consistently describing material properties and subsequent adjustments made to these, can the management of product life cycles reache a higher level and yield new business models.
At the Fraunhofer IWM, it is our aim to shape digital workflows over several steps to create comprehensive digital representations of the processes under consideration, thus helping to »creating better products through digitalization«.
Success factors for creating value from the digital representation of materials:
Efficiency in data creation (experimentally, numerically, sensorically,...)
Comprehensiveness (and completion) of the available database
Validity of the applied mechanistic or machine learning data analysis tools
Consistency of the data streams regarding the application-specific, portrayed production chain and manufacturing steps as well as implementation and operation
Quality of feedback to improve both design and engineering
Illustrative examples from industry, which call for digital workflows or digital representations:
How can consistent data management achieve a reduction in time to market?
Which aspects need to be considered in future approval processes?
How can production flows be quantified in regards to functionality and performance of materials?
Which requirements should a database fulfill to establish process-structure-property relationships?
How can functional requirements like crash security, structural durability, NVH and surface quality in vehicles become interlinked?
How can existing digital status information (e.g. sensors) assist in correcting actual processes?
Hierarchical description of materials via data achieved from experiments and simulations alongside processes
Creation of digital data sets and representations of materials
Design and development of materials data spaces
Integration of material data in a digital twin via virtual testing (Virtual Lab)
Finding and attaining structure-property-relationships of new material systems
Using material information as a means of control within the production process
Digital imaging and analysis of material histories
Linking material histories to the predicted behavior
Automatic material data creation (3D structure capturing, microstructure analysis, tribofarming)
Top-down/bottom-up informed modelling of material properties ranging from the atomistic to the component scale (multiscale information exchange)
Data analysis for the development of structure-process-property relationships (Deep Learning and statistics)
Automatic material mapping