Towards intelligent materials testing with reduced experimental effort for hot forming

Towards intelligent materials testing with reduced experimental effort for hot forming

Markus Bambach1, Muhammad Imran1, Johannes Buhl1, Sebastian Härtel2, Birgit  Awiszus2

1Chair of Mechanical Design and Manufacturing, Brandenburg University of Technology Cottbus – Senftenberg, Konrad – Wachsmann – Allee 17, D-03046 Cottbus, Germany.

2Department Virtual Production Engineering, Institute of Machine Tools and Production Processes, Chemnitz University of Technology, Reichenhainer Straße 70, D-09126 Chemnitz, Germany.



Hot forming processes are typically used to deform metals to the desired shape at lower forming forces and to control the microstructure. During hot deformation, the microstructure evolves by dynamic recrystallization after certain critical conditions are reached. The final recrystallized grain size controls the post-hot forming mechanical properties of metals and components. To predict the evolution of microstructure and flow stress, various material models were developed and implemented in finite element codes. They require a significant number of material-dependent parameters. Currently, experimental designs with a full-factorial approach for a range of temperature and strain rates are utilized to determine the desired parameters, which involve a huge experimental effort. The aim of this paper is to propose a methodology for parameter identification with reduced experimental effort where progression of testing and data evaluation is parallelized. An iterative, sequential approach is presented which optimizes the new testing conditions based upon preceding experimental conditions. The approach is exemplified for the high-temperature material Alloy-800H, using a material model that allows for accurate predictions of the flow stress. The developed strategy allows to achieve the desired accuracy of the material model by utilizing about a half of test matrix representing a full-factorial design. Hence, an efficient cost- and resource-optimized

Cite as:

Bambach, M., Imran, M., Buhl, J., Härtel, S., Awiszus, B. (2017). Towards intelligent materials testing with reduced experimental effort for hot forming. Computer Methods in Materials Science, 17(1), 44 – 50.

Article (PDF):


Hot working, Material models, Design of experiments, Parameter identification


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