Data-based die spotting in sheet metal forming

在钣金成形中，一个刀具的开发成本中有30%是由于刀具加工造成的。其原因是制造不精确，力流中所有元件的弹性变形以及成形过程中板材厚度的变化。由于在仿真模型中考虑所有的影响和不确定性是不现实的，它们的影响只能在实际模具上进行校正。磨合包括对活动表面进行点焊和机械加工，以根据均匀的点焊图案和确定的材料流生产出良好的零件。模具定位是一项手工的、耗时的、体验性强的工作。必须处理异构数据和抽象信息，并且必须考虑与后续过程的交互。熟练工人的短缺正迫使科学界为自动化工具熟悉创造基础。没有发现斑点图像与消融量之间的相关性的方法。没有描述合并策略的数学形式化。无法确定模具定位的自动化解决方案。最后，没有将已训练的工具状态转移到后续几代工具的记录。在不同的人工智能方法的组合中可以看到自动化模具定位的有前途的方法。需要回答以下研究问题（FF）并测试假设(H): FF 1：考虑到刀机交互，哪种类型的神经网络与哪种网格拓扑适合于自动生成成形工具的活动表面？h1.1：具有空间数据处理能力的神经网络可以解决上述设计问题。H 1.2：表示学习或符号AI算法表示其他解决方案。h1.3：模拟预训练增加了ML模型的鲁棒性，尽管数据量很小。FF 2：根据机器参数，如力和速度历史，哪种类型和拓扑的神经网络适合学习设计函数？H 2.1：设计功能根据成形问题和机器的描述确定参数。必须为两者定义一个正式的描述。同样，在模拟数据上预训练神经网络可以提供一个解决方案。如何实现模具定位的自动化？H 3.1：基于摄像头的系统捕获并分析活动表面的点状图像和成形部件的2D图像，以确定所需的烧蚀量。H 3.2：优化后的机器和工艺参数可以根据之前的数据和仿真计算出来。H 3.3：学习功能g（工具熟悉）的经验可用于改进功能f（工具设计）。

30 % of the development costs of a tool in sheet metal forming are due to the tool machining. The causes are manufacturing inaccuracies, elastic deformations of all elements in the force flow and the change in sheet thickness during forming. Since it is not realistic to consider all influences and uncertainties in simulation models, their effects can only be corrected on the real die. The run-in includes the spotting and mechanical machining of the active surfaces to produce a good part based on a uniform spotting pattern and a defined material flow. The die spotting is manual, time-consuming and experience-based work with strong physical stress. Heterogeneous data and abstract information must be processed and interactions with subsequent processes must be taken into account. The shortage of skilled workers is forcing the scientific community to create foundations for automating tool familiarization. No method of correlation between spotting image and the amount of ablation has been found. No mathematical formalization of an incorporation strategy has been described. No automated solutions for die spotting could be identified. Finally, no transfer of the trained tool state to subsequent generations of tools has been documented. Promising methods for automatable die spotting are seen in the combination of different AI approaches. The following research questions (FF) need to be answered and hypotheses (H) tested: FF 1: Which type of NN with which mesh topology is suitable for automated generation of the active surfaces of the forming tools considering the tool-machine interaction? H 1.1: NNs with the ability to process spatial data can solve the above design problem. H 1.2: Representation learning or symbolic AI algorithms represent other solutions. H 1.3: Pre-training with simulations increases the robustness of ML models despite small amounts of data. FF 2: What type and topology of NN is suitable for learning the design function in terms of machine parameters such as force and velocity histories? H 2.1: The design function identifies parameters based on descriptions of the forming problem and the machine. A formal description must be defined for both. Again, pre-training NNs on simulation data could provide a solution. FF 3: How can the task of die spotting be automated? H 3.1: A camera-based system captures and analyzes spotting images of the active surfaces and 2D images of the formed part to determine the amount of ablation required. H 3.2: Optimized machine and process parameters can be calculated based on previous data and simulations. H 3.3: Experiences from learning function g (tool familiarization) can be used to improve function f (tool design).