Digitizing material intensive value chains

Digitalization within materials technology

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Efficient digital workflows

Fraunhofer IWM Video Series: Materials Digitalization

Material data space and digital representations

Realizing digitalization in materials engineering

Projects and Activities

 

Digitalizing materials for better products and processes

 

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.

Efficient digital workflows

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

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Fraunhofer IWM Video Series: Materials Digitalization

Prof. Dr. Chris Eberl

Why is there a need for digitalization in materials technology?

What is the significance of digitalization within materials science?

What is a digital twin in materials technology?

Creating a digital representation of a material

Creating digital twins of materials regarding product life cycles

To watch a large version of the video, click the word “YouTube” on the lower right of the video frame.

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Material data space and digital representations

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?

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Implementing digitalization in materials technology and engineering

The services of the Fraunhofer IWM

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

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Projects and Activities

 

Creating digital twins of materials


To ensure the digital networking of production systems and the optimization of material-specific requirements, we need to measure, analyze and replicate the changes in material properties in a process in which “digital twins” of materials are created. The materials data space developed by Fraunhofer researchers has laid the groundwork for this process.

 

MagnetPredictor: predicting the magnetic properties of materials


Permanent magnets used in electric cars and wind turbines currently contain rare earth metals. Reducing the amount of these elements in magnets is important, as mining them is harmful both to health and the environment. Researchers have now developed a new machine learning tool to assist in quickly and easily predicting the ferromagnetic crystal properties of novel material compositions.

 

Determining materials data for forming process simulations in the Virtual Lab


Sheet metal materials are often stressed to their limits during the forming process. Computer simulations are used to test how far it is possible to go in the production stage. However, the simulations are only as exact as the data upon which they’re based. A team at the Fraunhofer Institute for Mechanics of Materials IWM in Freiburg has now developed a virtual test laboratory that allows for the examination of metal materials at different load states and for the determination of precise mechanical data.