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

• Applicants: Professor Dr.-Ing. Andreas Maier; Professorin Dr.-Ing. Marion Merklein
• Subject Area: Primary Shaping and Reshaping Technology, Additive Manufacturing ; Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
• Term: from 2017 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 325262702

The aim of the research project is an objective classification of failure behaviour of sheet metals during forming processes with the aid of pattern recognition methods. In order to extend the FLC with failure stages, the video sequences of the forming processes were annotated by experts and the individual images of the sequences were assigned to the following different failure classes: homogeneous forming, diffuse necking, localised necking and crack initiation. Based on the expert annotations, a supervised pattern recognition method was developed that provides automatic classification into the respective failure stages. In particular, a sensitivity of up to 92% was achieved for the local necking class, whereas the consistency of the expert annotations and artifacts in the strain distributions negatively impaired the quality of the results. In order to achieve expert independence and transferability to new materials, an unsupervised pattern recognition method was additionally developed, which as a result provides an FLC with different failure probabilities and achieves higher forming limits compared to conventional evaluation procedures with the same safety margin. These results were additionally validated by metallographic investigations. In the new project phase an extension of the methodology to additional material characterization tests will be investigated. The challenges are not only the development of the machine learning approach to potentially more robust deep learning methods, but also the comprehensive analysis of material mechanisms under different strain conditions. The transferability of the evaluation method is evaluated by means of uniaxial tensile, notch tensile and hydraulic bulge tests and the application spectrum is additionally extended to the bending test. The limit strains of the experiments, determined by machine learning, are validated with metallographic investigations and material-specific behavior patterns are additionally identified in the individual experiments. Furthermore, in the first phase it was ascertained that discontinuity in the strain distribution, such as local maxima, can be detected automatically. However, the determination of an objective failure definition is complex and an objective failure definition cannot yet be conclusively established. On the one hand, the metallographic results can only be transferred to pattern recognition to a limited extent, since the observed discontinuities are in the micrometer range and are therefore only partially captured by the strain measurement. On the other hand, the discontinuities are strain localizations that may not coincide with the material failure. Hereby, the effects of the discontinuity on the methodology are investigated. In order to quantify the material properties, a stochastic evaluation based on more than 100 tensile tests is carried out to determine which material properties, forming stages and patterns can be detected during material aging.

• DFG Programme: Research Grants