论文标题
行业4.0示例：使用机器学习在非侵入性传感器数据上使用机器学习的实时质量控制
An Industry 4.0 example: real-time quality control for steel-based mass production using Machine Learning on non-invasive sensor data
论文作者
论文摘要
质量不足的大规模生产质量可能会对工具，生产下降和低质量产品造成极为昂贵的损害。非常需要自动，快速和廉价的策略，以估算质量控制，降低风险和预测故障的重要材料特性。在这项工作中，我们分析了高吞吐量的基于钢铁产品的生产线。目前，使用手动破坏性测试检查材料质量，该测试缓慢，浪费，仅覆盖材料的一小部分。为了实现完整的测试覆盖，我们的工业合作者开发了一种无接触式的，非侵入性的电磁传感器，以实时测量所有材料。我们的贡献是三个方面：1）我们在受控的实验中表明，传感器可以通过故意改变特性区分钢。 2）对48个钢管进行了全面测量，并对样品进行了其他破坏性测试，以作为地面真理。拟合线性模型可以从非侵入性测量值中预测两个关键材料特性（屈服强度和拉伸强度），通常是通过破坏性测试获得的。在剩余的交叉验证中评估性能。 3）所得模型用于分析用非侵入性传感器测量的〜108 km处理材料的实际生产数据上的材料特性和与记录的产品故障的关系。该模型实现了出色的性能（F3得分为0.95），预测材料的拉伸强度规格不足。模型预测和已记录的产品故障的组合表明，如果大量估计收益应力值不超出规范，则产品故障的风险很高。我们的分析证明了实时质量控制，风险监控和故障检测的有希望的方向。
Insufficient steel quality in mass production can cause extremely costly damage to tooling, production downtimes and low quality products. Automatic, fast and cheap strategies to estimate essential material properties for quality control, risk mitigation and the prediction of faults are highly desirable. In this work we analyse a high throughput production line of steel-based products. Currently, the material quality is checked using manual destructive testing, which is slow, wasteful and covers only a tiny fraction of the material. To achieve complete testing coverage our industrial collaborator developed a contactless, non-invasive, electromagnetic sensor to measure all material during production in real-time. Our contribution is three-fold: 1) We show in a controlled experiment that the sensor can distinguish steel with deliberately altered properties. 2) 48 steel coils were fully measured non-invasively and additional destructive tests were conducted on samples to serve as ground truth. A linear model is fitted to predict from the non-invasive measurements two key material properties (yield strength and tensile strength) that normally are obtained by destructive tests. The performance is evaluated in leave-one-coil-out cross-validation. 3) The resulting model is used to analyse the material properties and the relationship with logged product faults on real production data of ~108 km of processed material measured with the non-invasive sensor. The model achieves an excellent performance (F3-score of 0.95) predicting material running out of specifications for the tensile strength. The combination of model predictions and logged product faults shows that if a significant percentage of estimated yield stress values is out of specification, the risk of product faults is high. Our analysis demonstrates promising directions for real-time quality control, risk monitoring and fault detection.
