Zhang Yixiang, Gao Zenggui, Sun Jiachen, Liu Lilan
Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.
Sensors (Basel). 2023 Jul 27;23(15):6719. doi: 10.3390/s23156719.
Quality-related prediction in the continuous-casting process is important for the quality and process control of casting slabs. As intelligent manufacturing technologies continue to evolve, numerous data-driven techniques have been available for industrial applications. This case study was aimed at developing a machine-learning algorithm, capable of predicting slag inclusion defects in continuous-casting slabs, based on process condition sensor data. A large dataset consisting of sensor data from nearly 7300 casting samples has been analyzed, with the empirical mode decomposition (EMD) algorithm utilized to process the multi-modal time series. The following machine-learning algorithms have been examined: K-Nearest neighbors, support vector classifier (linear and nonlinear kernels), decision trees, random forests, AdaBoost, and Artificial Neural Networks. Four over-sampling or under-sampling algorithms have been adopted to solve imbalanced data distribution. In the experiment, the optimized random forest outperformed other machine-learning algorithms in terms of recall and ROC AUC, which could provide valuable insights for quality control.
连铸过程中的质量相关预测对于铸坯的质量和过程控制至关重要。随着智能制造技术的不断发展，众多数据驱动技术已可用于工业应用。本案例研究旨在基于过程条件传感器数据开发一种能够预测连铸板坯夹渣缺陷的机器学习算法。分析了一个由近7300个铸造样本的传感器数据组成的大型数据集，并利用经验模态分解（EMD）算法处理多模态时间序列。研究了以下机器学习算法：K近邻、支持向量分类器（线性和非线性核）、决策树、随机森林、AdaBoost和人工神经网络。采用四种过采样或欠采样算法来解决数据分布不均衡问题。在实验中，优化后的随机森林在召回率和ROC曲线下面积方面优于其他机器学习算法，可为质量控制提供有价值的见解。
