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Forming Processes

Our experience with the latest materials science and materials engineering findings enables us to optimize forming tools and processes, while numerical simulations facilitate the avoidance of trial and error loops. We develop material models that describe material behavior and determine relevant material and process parameters.

The latest news from the »Forming Processes« group

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DFG project „Taylored material properties through microstructure optimization: Machine learning for modeling and inversion of structure-property relations and their application on sheet metal“ started

 

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 "structured-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. The developed methods will be applied to sheet metals within the frame of the project as a proof of concept.

The 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.

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Grey-Box-Models

Project Grey-box models: integrating expert knowledge within machine learning


The main issue of project Grey-Box models is to improve machine learning methods (black-box models) by integrating domain knowledge, using for example deterministic models (white-box models). Grey-box models aim especially at industrial applications, as there are typically only few significant (distributed over the space of process parameters) data available, whereas the processes themselves are relatively well known. During the project, we will work on three use-cases focusing on material science: automatic crack surface detection in microstructural images, surrogate modeling of deep drawing processes and the identification for material model parameters using neural networks.

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DFG Project on Simulating Hot Forming and Heat Treatment Enters Second Round


Within the Priority Program 1713 of the German Research Foundation (DFG), we are working on a novel simulation tool for hot forming and heat treatment of metallic materials. Our approach links the thermomechanical material behavior and microstructure evolution using a comprehensive thermodynamic framework. This allows us to efficiently represent elastic-plastic material behavior, recovery, recrystallization, grain coarsening, texture evolution and precipitation as well as the related hardening and softening processes. After a successful first project phase, a second phase of three years duration was granted and started in December 2017.

More information


Lukas Kertsch
Telephone +49 761 5142-479
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What we offer

 

Comprehensive forming process services via our diverse combination of experimentation, testing methodologies, many years’ experience in numerical simulation and expertise in material modeling.

We find and clarify the physical reasons for weak points in manufacturing processes, thus enabling our clients to avoid problems in the initial design phase. By identifying optimization potential in a given production process, we provide our clients the means to more accurately produce components while reducing faulty parts.

We focus upon describing forming processes, evaluating forming limits and predicting the development of relevant material properties within forming processes.

We describe interactions between the parts and components to be formed and forming tools while determining friction and wear.

Through our "Virtual Lab" we link the microstructures in materials with macroscopic material properties in order to simulate property changes during processing.

In cooperation with various other departments at the Fraunhofer IWM such as tribology, fatigue, joints and crash dynamics, we address target-oriented, far-reaching issues concerning sheet metal processing and forming.

Topics

 

Experimental identification of characteristic values for forming simulations


The latest simulation methods help us to help our partners to design and assess their sheet metal and solid material forming as well as cold and hot working processes. As a matter of course, we describe anisotropy as a result of texture, incorporate thermomechanical coupling phenomena, describe the tribological properties of contact objects and model damage with the help of micromechanically based damage...

 

"Virtual Lab": Numerically determining characteristic values for forming simulations


The crystalline structure of metals in forming processes can have a significant influence on mechanical behavior, thus effecting the forming process: for example the development of grain orientation (crystallographic texture) and grain morphology through plastic deformations and thermally-activated phase formation and recrystallization processes in metals. We simulate such changes in the... 

 

Thermophysical and thermomechanical characterization


The modern equipment and procedures in the Fraunhofer IWM thermophysical and thermomechanical labs enable us to determine temperature dependent material properties. These properties provide the essential basis for evaluating the effects of thermal loads on components. This substantiated data is necessary for FE- simulation in order to optimize production processes, contour accuracy and energy...

 

 

Process simulation and forming simulation for components


Using the most modern simulation methodologies, we optimize forming processes for our clients who require bulk forming, sheet forming, hot forming and cold forming. We identify and clarify the physical reasons for weak links that may exist in manufacturing steps - as well as the causes for these - and manage their effects and impacts in the design phase and when in actual use. On the basis of experimental testing, we analyze, determine and...

 

Dimensioning connectors and electrical bonds


On the basis of continuum mechanical models and cutting-edge simulation methodologies, we analyze, evaluate and optimize design processes and forming processes for connectors, including tools and manufacturing steps. We identify the physical causes for potential problems in manufacturing processes. The material microstructure can be linked with material properties, enabling us to simulate changes that occur in material properties during...

 

Novel Approaches for Simulating Hot Forming and Heat Treatment

Utilizing novel concepts, we make detailed simulations of hot forming and heat treatment of metallic materials possible. Our approach links thermomechanical material behavior and microstructure evolution using a comprehensive thermodynamic framework. This allows us to efficiently represent elastic-plastic material behavior, recovery, recrystallization, grain coarsening, texture evolution and precipitation as well as the related hardening and softening processes. Despite the complexity of the material model, parameter identification only...

 

Machine Learning


Nowadays, machine learning is used in almost all areas of research and thereby heavily changes the sight on many existing problems. Machine learning therefore has a high potential to create new innovations. This also holds for material sciences and especially for forming processes. As experts in characterizing materials, material modeling and numerical simulation of forming processes, we investigate how machine learning can be applied to solve problems concerning materials sciences in forming industry.

 

Publications regarding Forming Processes


Contributions to newspapers, books and conferences as well as dissertations and project reports...

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TWIP-Steel simulation for sheet metal forming


TWIP-steel features a tensile strength of approx. 1000 MPa with a breaking elongation of 40-50 %. By using TWIP steels, both the energy absorption of components and the structural safety of the vehicle can be significantly improved. The strength of this material allows for a reduction of the sheet thickness used in components and contributes to a more efficient use of resources. Scientists at the Fraunhofer IWM have developed an appropriate material model so that the mechanical properties of TWIP steel can be accurately described. An essential characteristic of this model is the physically based description of microstructural properties and especially the development of the twin volume fractions depending on deformation and state of stress.

More about TWIP-Steel simulation for sheet metal forming

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Process chain simulations


The entire process chain can be virtually described through the linking of either very similar or quite different simulation methodologies. At the Fraunhofer IWM we develop methods for the linking of subsequent process steps: cold rolling simulations realized via the finite element method (FEM) are combined with heat treatment descriptions. The ensuing results are then used in microstructure simulations to predict macroscopic, mechanical characteristics which are incorporated into material models for component forming simulations. This enables us to test the influence of individual process parameters on material properties.

More about process chain simulations

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The "Virtual Lab"


The "Virtual Lab" is a simulation tool for the numerical determination of macroscopic material properties which takes the microstructure into account. Data produced through our "Virtual Lab" can be used in exactly the same manner as experimental data and is especially applicable to the complex material models which are required when working with modern, high-strength sheet metal materials. These complex material models utilize many parameters which can be identified by the additional data obtained from the "Virtual Lab".

More about the virtual determination of parameters for sheet metal forming simulations

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University lectures


Dr. Dirk Helm:

Forming technology process simulation
at the Karlsruhe Institute of Technology KIT

Continuum Mechanics I/II
at the University of Freiburg

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