| Summary: | In automotive manufacturing, high strength materials, and aluminum alloys are widely used to address the requirement of ensuring a lightweight car body and correspondingly, reducing pollution. In this context of complexity of materials and structures, an optimized process design with finite element analyses (FEA) is mandatory, as well as a correct definition of the material forming limits. For this purpose, in sheet metal forming, the forming limit curve (FLC) is used. The FLC is defined by the onset of necking. The standard evaluation method according to DIN EN ISO 12004-2 is based on the cross-section method and assumes that the failure occurs due to a clear localized necking. However, this approach has its limitations, specifically in the case of brittle materials that do not exhibit a distinct necking phase. To overcome this challenge, a pattern recognition-based evaluation is proposed. Although pattern recognition and machine learning techniques have been widely employed in the medical field, few studies have investigated them in the context of analyzing metal sheet forming limits. The application of pattern recognition in metal forming is subject to the exact definition of the forming behaviors. Thereby, it is challenging to relate patterns on the strain distribution during Nakajima tests with the onset of necking for the FLC determination. Thus, the first approach was based on the crack evaluation, since this class is well-defined. However, of substantial interest is the evaluation of the general material instabilities that precede failure. Therefore, in the present study, the analysis of the material behavior during stretching is conducted in order to characterize instability classes. The results of Nakajima tests are investigated using an optical measurement system. A conventional pattern recognition approach based on texture features, considering the outcomes of expert interviews for the definition of classes is used for the FLC determination. Moreover, an analysis of the validity of the supervised learning is conducted. The results show a good prediction of the onset of necking, even for high strength materials with a recall of up to 92%. Some deviations are observed in the determination of the diffuse necking. The discrepancies of the different experts’ prognoses highlight the user-dependency of the FLC, suggesting further investigations with an data-driven approach, could be beneficial.
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