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Data-Driven Product Quality Monitoring in Quality-Critical Forming Processes

机译：质量关键成形过程中的数据驱动产品质量监测

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Quality-critical production processes influence the final product quality significantly. There is an increasing demand for highly accurate product quality monitoring systems to reduce time- and cost-intensive product inspections. This paper proposes a data-driven product quality monitoring system for execution on devices with low computational power in production environments. Particularly for quality-critical processes, the developed monitoring approach promises to deliver high accuracy. It is based on building a regression model to describe a quality indicator dependent on sensor data. The developed approach is addressed to highly variable production processes with a minimal set of reference data in which the quality assessment must be available in a timely manner. This small set of reference data is used for model building. Therefore, it is estimated that the regression model tends to deliver limited predictive power. The authors consider semi-optimal models explicitly and design a quality classifier sensitive to the prediction model’s predictive power. The presented approach is evaluated on historical data for a use case from powder metallurgy. Furthermore, the approach for product quality monitoring under consideration of semi-optimal regression models provides a one hundred percent accuracy in an exemplary test case. It is shown that the model’s predictive power in quality monitoring must be considered to design monitoring systems with high accuracy.
机译：质量关键生产工艺显著影响最终产品的质量。有高度精确的产品质量监控系统，以减少时间和成本密集型产品检验的需求不断增加。本文提出了一种关于在生产环境中以低计算功率器件执行数据驱动的产品质量监测系统。特别是对于质量的关键过程中，发达国家的监督措施的承诺，提供高精确度。它基于建立一个回归模型来描述依赖于传感器数据的质量指标。所开发的方法是用一组，其中所述质量评估必须及时可用的参考数据的最小寻址到高度可变的生产工艺。这个小组参考数据是用于模型构建。因此，据估计，回归模型趋向于提供有限的预测能力。作者明确地考虑半优化模型和设计质量分类的预测模型的预测能力敏感。所提出的方法是在用于从粉末冶金用例的历史数据进行评价。此外，对于产品质量考虑半最佳回归模型的监测方法提供了在一个示例性测试案例百分之百精度。结果表明，在质量监控模型的预测能力必须考虑以高精确度来设计监控系统。

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