Jaremenko Christian, Ravikumar Nishant, Affronti Emanuela, Merklein Marion, Maier Andreas
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg Martensstr. 3, 91058 Erlangen, Germany.
Institute of Manufacturing Technology, Friedrich-Alexander-Universität Erlangen-Nürnberg Egerlandstr. 13, 91058 Erlangen, Germany.
Materials (Basel). 2019 Mar 30;12(7):1051. doi: 10.3390/ma12071051.
The forming limit curve (FLC) is used to model the onset of sheet metal instability during forming processes e.g., in the area of finite element analysis, and is usually determined by evaluation of strain distributions, derived with optical measurement systems during Nakajima tests. Current methods comprise of the standardized DIN EN ISO 12004-2 or time-dependent approaches that heuristically limit the evaluation area to a fraction of the available information and show weaknesses in the context of brittle materials without a pronounced necking phase. To address these limitations, supervised and unsupervised pattern recognition methods were introduced recently. However, these approaches are still dependent on prior knowledge, time, and localization information. This study overcomes these limitations by adopting a Siamese convolutional neural network (CNN), as a feature extractor. Suitable features are automatically learned using the extreme cases of the homogeneous and inhomogeneous forming phase in a supervised setup. Using robust Student's t mixture models, the learned features are clustered into three distributions in an unsupervised manner that cover the complete forming process. Due to the location and time independency of the method, the knowledge learned from formed specimen up until fracture can be transferred on to other forming processes that were prematurely stopped and assessed using metallographic examinations, enabling probabilistic cluster membership assignments for each frame of the forming sequence. The generalization of the method to unseen materials is evaluated in multiple experiments, and additionally tested on an aluminum alloy AA5182, which is characterized by Portevin-LE Chatlier effects.
成形极限曲线（FLC）用于模拟成形过程中金属薄板失稳的起始情况，例如在有限元分析领域，通常通过评估在 Nakajima 试验期间使用光学测量系统得出的应变分布来确定。当前方法包括标准化的 DIN EN ISO 12004-2 或与时间相关的方法，这些方法启发式地将评估区域限制为可用信息的一部分，并且在没有明显颈缩阶段的脆性材料的情况下存在弱点。为了解决这些限制，最近引入了监督和无监督模式识别方法。然而，这些方法仍然依赖于先验知识、时间和定位信息。本研究通过采用暹罗卷积神经网络（CNN）作为特征提取器克服了这些限制。在监督设置中，使用均匀和非均匀成形阶段的极端情况自动学习合适的特征。使用稳健的学生 t 混合模型，将学习到的特征以无监督方式聚类为三种分布，涵盖整个成形过程。由于该方法与位置和时间无关，从成形试样直到断裂所学到的知识可以转移到其他过早停止并使用金相检查进行评估的成形过程中，从而能够为成形序列的每一帧分配概率聚类成员。在多个实验中评估了该方法对未见过的材料的通用性，并在具有 Portevin-LE Chatlier 效应特征的铝合金 AA5182 上进行了额外测试。
