Increasing the throughput of laser-induced shock wave indentation testing by an adapted measurement strategy and data evaluation based on machine learning

通过基于机器学习的适应性测量策略和数据评估来提高激光诱导冲击波压痕测试的吞吐量

• 批准号: 530614223
• 负责人: Dr.-Ing. Tim Radel
• 金额: --
• 依托单位: Zentrum für Technomathematik (ZeTeM)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/530614223?language=en
• 关键词: Increasing; throughput; laser; induced; shock

It has already been proven in the past that material properties can be efficiently determined by means of laser-induced shock wave indentation testing. The aim of the project at hand is to increase the throughput of the measurement process on the one hand and to expand the range of material properties that can be determined on the other. The basis of the measurement technology is a confocal sensor that measures the indentation geometry, whereby in particular forming speed-dependent effects can also be recorded. A measurement strategy to be developed will enable the measurement method as a whole to be used for high throughput. In addition, further suitable descriptors are to be identified. An integral part of the measuring system is the evaluation of the results of the confocal sensor by means of neural networks. Here, the recorded data is the input variable and the material parameters are the output. The neural networks perform this task very efficiently based on an appropriate configuration to be worked out in the project. However, their training requires a very large number of available data sets. In order to avoid having to determine these experimentally at great effort, a highly efficient finite element simulation of the indentation process is being developed, which provides the required data. At the same time, the simulation enables various new evaluation approaches, some of which also include neural networks. The evaluation of these different approaches, e.g., with regard to accuracy and computing time, is another component of the project at hand. Finally, the developed measurement methodology will be tested and evaluated using a benchmark.

它已经被证明在过去，材料性能可以有效地确定通过激光诱导冲击波压痕测试。该项目的目的是一方面提高测量过程的吞吐量，另一方面扩大可确定的材料特性范围。测量技术的基础是一个共焦传感器，它测量压痕几何形状，特别是可以记录成形速度相关的影响。待开发的测量策略将使测量方法作为一个整体用于高通量。此外，还将确定其他合适的描述符。测量系统的一个组成部分是通过神经网络评估共焦传感器的结果。在这里，记录的数据是输入变量，材料参数是输出。神经网络基于项目中要制定的适当配置非常有效地执行此任务。然而，它们的训练需要大量的可用数据集。为了避免必须在实验上确定这些在很大的努力，一个高效的有限元模拟的压痕过程正在开发中，它提供了所需的数据。与此同时，模拟使各种新的评估方法，其中一些还包括神经网络。对这些不同方法的评价，例如，关于准确性和计算时间，是手头项目的另一个组成部分。最后，将使用基准测试和评估所制定的衡量方法。