Improved characterization of failure behaviours of sheet metals based on pattern recognition methods

Third party funded individual grant


Start date : 01.04.2017

End date : 31.12.2019


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Project details

Short description

The aim of the research project is an objective classification of failure behaviour of sheet metals during Nakajima tests with the aid of pattern recognition methods. In this project, pattern recognition methods are used to determine the failure stages during a sheet metal forming process. Prerequisite for the method development is a comprehensive metallographic analysis, the description of the material physical characteristics and the classification of different failure stages, depending on the material as well as on the stress and strain state.

Pattern recognition methods will be developed in order to detect and classify the failure stages based on surface characteristics. In contrast to the previous approaches, the pattern recognition method will fulfil the criteria of objectivity and user-independence.

The project is in cooperation with the Department of Pattern Recognition in Erlangen.

Scientific Abstract

The aim of the research project is an objective classification of failure behaviour of sheet metals during Nakajima tests with the aid of pattern recognition methods. In this project, pattern recognition methods are used to determine the failure stages during a sheet metal forming process. Prerequisite for the method development is a comprehensive metallographic analysis, the description of the material physical characteristics and the classification of different failure stages, depending on the material as well as on the stress and strain state.

Pattern recognition methods will be developed in order to detect and classify the failure stages based on surface characteristics. In contrast to the previous approaches, the pattern recognition method will fulfil the criteria of objectivity and user-independence.

The project is in cooperation with the Department of Pattern Recognition in Erlangen.

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