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

该研究项目的目的是借助模式识别方法对成形过程中金属板的失效行为进行客观分类。为了扩展破坏阶段的FLC，专家对成形过程的视频序列进行了注释，并将序列的单个图像分配到以下不同的破坏类别：均匀成形、扩散颈缩、局部颈缩和裂纹起裂。在专家标注的基础上，提出了一种基于监督的模式识别方法，对故障阶段进行自动分类。特别是，对于局部颈缩类，灵敏度高达92%，而专家注释和工件在应变分布中的一致性会对结果的质量产生负面影响。为了实现专家的独立性和对新材料的可转移性，提出了一种无监督模式识别方法，该方法可以提供具有不同失效概率的FLC，并且在相同安全裕度的情况下，与传统的评估方法相比，可以获得更高的成形极限。金相研究进一步证实了这些结果。在新的项目阶段，将研究将该方法扩展到其他材料特性测试。挑战不仅在于机器学习方法的发展，而且在于不同应变条件下材料机制的综合分析。通过单轴拉伸、缺口拉伸和水力膨胀试验对评价方法的可转移性进行了评价，并将其应用范围扩展到弯曲试验。由机器学习确定的实验极限应变通过金相研究进行验证，并且在单个实验中还确定了材料特定的行为模式。此外，在第一阶段，确定了应变分布中的不连续，如局部最大值，可以自动检测。然而，确定一个客观的失效定义是复杂的，一个客观的失效定义还不能最终建立。一方面，金相结果只能在有限的程度上转移到模式识别，因为观察到的不连续是在微米范围内，因此只能部分地被应变测量捕获。另一方面，不连续是应变局部化，可能与材料失效不一致。因此，研究了不连续性对方法的影响。为了量化材料性能，基于100多次拉伸试验进行了随机评估，以确定在材料老化过程中可以检测到哪些材料性能、成形阶段和模式。

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.