ABSTRACT An electric-resistance-welded (ERW) tube is one of the leading contributor products for automotive manufacturers. Such tubes tend to fail due to weak weld quality that may be the result of several parameters, such as inappropriate selection of weld power, positions of forming rolls and fin pass rolls, mill operating speed and many more. This paper presents an application of classifier algorithm to predict weak welded tube during real production process. Real-time data with 86 independent variables are trained using logistic regression as classifier. A logistic regression model is validated on real-time test data with capability of predicting failure 82.55% of the time (often termed as sensitivity). Operating ranges for mill parameters are estimated with the help of model results. Significant yield improvement has been observed after deployment of model recommended operating ranges as recipe in manufacturing unit. Working of newly developed human machine interface (HMI) has been explained. This exercise has been performed for three different product segments being manufactured in Tubes Division, Tata Steel Limited. This work demonstrates an application of artificial intelligence in manufacturing plant for quality improvement.

中文翻译：

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用于电阻焊管制造过程中故障预测和产量提高的机器学习算法

摘要 电阻焊 (ERW) 管是汽车制造商的主要贡献产品之一。这种管子往往会由于焊接质量差而失败，这可能是几个参数的结果，例如焊接功率选择不当、成形辊和翅片轧辊的位置、轧机运行速度等等。本文介绍了分类器算法在实际生产过程中预测弱焊管的应用。使用逻辑回归作为分类器训练具有 86 个自变量的实时数据。逻辑回归模型在实时测试数据上得到验证，具有 82.55% 的预测失败率（通常称为灵敏度）。在模型结果的帮助下估计轧机参数的运行范围。在将模型推荐的操作范围部署为制造单元的配方后，观察到了显着的产量提高。解释了新开发的人机界面 (HMI) 的工作原理。该练习针对塔塔钢铁有限公司管材部门制造的三个不同产品段进行。这项工作展示了人工智能在制造工厂中的应用，以提高质量。