In consequence of high cost pressure and the progressive globalization of markets, blanking, which represents the most economical process in the value chain of manufacturing companies, is particularly dependent on reducing machine downtimes and increasing the degree of utilization. For this purpose, it is necessary to be able to make a real-time prediction about the current and future process conditions even at high production rates. Therefore, this study investigates the influence of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine to classify abrasive wear states during blanking based on force signals. The performance of the model was quantitatively evaluated based on the model accuracy and the separability of the classes. As a result, it was shown, that the deviation of time series represents the key parameter for the resulting performance of the classification model and strongly depends on the sensor type and position, the preprocessing procedure as well as the feature extraction and selection. Furthermore, it is shown that the consideration of domain knowledge in the phases of data acquisition, preprocessing and transformation improves the performance of the classification model and is essential to successfully implement AI projects. Summarizing the findings of this study, trustworthy data sets play a crucial role for implementing an automated process monitoring as a basis for resilient manufacturing systems.

由于高成本压力和市场逐渐全球化，下料是制造公司价值链中最经济的过程，特别依赖于减少机器停机时间和提高利用率。为此，即使在高生产率下，也必须能够对当前和未来的工艺条件进行实时预测。因此，本研究调查了数据采集、预处理和转换对多类支持向量机性能的影响，以基于力信号对冲裁过程中的磨料磨损状态进行分类。基于模型精度和类的可分离性定量评估模型的性能。结果显示，时间序列的偏差代表了分类模型最终性能的关键参数，并且在很大程度上取决于传感器类型和位置、预处理过程以及特征提取和选择。此外，研究表明，在数据采集、预处理和转换阶段对领域知识的考虑提高了分类模型的性能，对于成功实施 AI 项目至关重要。总结这项研究的结果，值得信赖的数据集在实施自动化过程监控方面发挥着至关重要的作用，作为弹性制造系统的基础。预处理过程以及特征提取和选择。此外，研究表明，在数据采集、预处理和转换阶段对领域知识的考虑提高了分类模型的性能，对于成功实施 AI 项目至关重要。总结这项研究的结果，值得信赖的数据集在实施自动化过程监控方面发挥着至关重要的作用，作为弹性制造系统的基础。预处理过程以及特征提取和选择。此外，研究表明，在数据获取、预处理和转换阶段对领域知识的考虑提高了分类模型的性能，对于成功实施 AI 项目至关重要。总结这项研究的结果，值得信赖的数据集在实施自动化过程监控方面发挥着至关重要的作用，作为弹性制造系统的基础。