Application Research of Online Quality Control Method to Metallurgical Products A novel methodology of online monitoring, diagnosis and optimization for process parameters 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. Then, the online diagnosing method is proposed through the contribution chart of the process parameters. Finally, the normal samples which are adjacent to abnormal samples are selected as target samples and used to construct the optimization rules. The effectiveness of the proposed method is verified using a dataset from a steel production process. The results indicate that the methodology has a good applicability in complex production processes with nonlinear and strong correlation characteristics.

Process Management of Customized Product Manufacturing for Steel Structures Steel structure manufacturing enterprises mainly according to orders, the process management of steel structure products is characterized by randomness and uncertainty of orders, simultaneous operation of multiple products and large volume of products. In this paper, the simultaneous production of customized steel products with similar process flow is divided into one class and put on one line for production during the technological process setted. As the components of customized products are large, we set up virtual libraries between processes, and the virtual libraries are divided into several subvirtual libraries to distinguish the components of customized products. In order to determine the processing sequence of parts, the rules of inputting and outgoing storage are set in the virtual library, In order to ensure the efficiency and normal operation of production, while selecting multiple indicators to supervise production, the process is divided into several steps to guide production. And a process management system is developed to manage the production process.