A NOVEL METHODOLOGY FOR PROCESS PARAMETER OPTIMIZATION BASED ON SUPPORT VECTOR DATA DESCRIPTION - Xu, Gang; Dong, Qianqian; Zhang, Xiaotong; Li, Min (International Journal of Industrial Engineering: Theory, Applications and Practice, 2021)
A novel methodology for process parameter optimization based on support vector data description (SVDD) is proposed to improve the quality of steel products and reduce the mass quality problems caused by the inability to timely adjust the production process parameters. First, the control limit is calculated by the SVDD method with normal samples. Next, the normal samples adjacent to abnormal samples are selected based on the Euclidean distance and used to construct the optimization rules, which are adopted to optimize the production process. The effectiveness of the proposed method is verified using a dataset from a steel production process. The results indicate that the methodology has good applicability in complex production processes with nonlinear and strong correlation characteristics.