Determination and Enhancement of the Forming Limit Curve for Sheet Metal Materials using Machine Learning

Future legal standards for European automobiles will require a considerable reduction in CO2 emissions by 2021. In order to meet these requirements, an optimization of the automobiles is required, comprising technological improvements of the engine and aerodynamics, or even more important, weight reductions by using light-weight components. The properties of light-weight materials differ considerably from those of conventional materials and therefore, it is essential to correctly define the formability of high-strength steel or aluminum alloys. In sheet metal forming, the forming capacity is determined by means of the forming limit curve that specifies the maximum forming limits for a material. However, current methods are based on heuristics and have the disadvantage that only a very limited portion of the evaluation area is considered. Moreover, the methodology of the industry standard is user-dependent with simultaneously varying reproducibility of the results. Consequently, a large safety margin from the experimentally determined forming limit curves is required in process design. This thesis introduces pattern recognition methods for the determination of the forming limit curve. The focus of this work is the development of a methodology that circumvents the previous disadvantages of location-, time-, user- and material dependencies. The dependency on the required a priori knowledge is successively reduced by incrementally improving the proposed methods. The initial concept proposes a supervised classification approach based on established textural features in combination with a classifier and addresses a four-class problem consisting of the homogeneous forming, the diffuse and local necking, as well as the crack class. In particular for the relevant class of local necking, a sensitivity of up to 92% is obtained for high-strength materials. Since a supervised procedure would require expert annotations for each new material, an unsupervised classification method to determine the local necking is preferred, so that anomaly detection is feasible by means of predefined features. A probabilistic forming limit curve can thus be defined in combination with Gaussian distributions and consideration of the forming progression. In order to further reduce the necessary prior knowledge, data-driven features are learned based on unsupervised deep learning methods. These features are adapted specifically to the respective forming sequences of the individual materials and are potentially more robust and characteristic in comparison to the predefined features. However, it was discovered that the feature space is not well-regularized and thus not suitable for unsupervised clustering procedures. Consequently, the last methodology introduces a weakly supervised deep learning approach. For this purpose, several images of the beginning and end of the forming sequences are used to learn optimal features in a supervised setup while regularizing the feature space. Through unsupervised clustering, this facilitates the class membership determination for individual frames of the forming sequences and the definition of the probabilistic forming limit curve. Moreover, this approach enables a visual examination and interpretation of the actual necking area.