A Novel Multi-Objective Process Parameter Interval Optimization Method for Steel Production

• Published in: Metals (Basel ) 2021-10, Vol.11 (10), p.1642
• Main Authors: Yan, Yifan, Lv, Zhimin
• Format: Article
• Language: English
• Subjects: Algorithms; Continuous casting; Control equipment; Customization; Deep learning; Design optimization; Genetic algorithms; hot-rolling; Iterative methods; Manufacturing; Mathematical models; Mechanical properties; Morris sensitivity analysis; multi-objective optimization; Multiple objective analysis; Neural networks; Optimization techniques; Parameter sensitivity; Particle beams; Particle swarm optimization; particle swarm optimization algorithm; Prediction models; process control capability; Process controls; Process parameters; Product quality; Production costs; Quality control; Steel production; Steel products; Support vector machines; Sustainable development; Tensile strength
• ISSN: 2075-4701; 2075-4701
• DOI: 10.3390/met11101642

Customized small batch orders and sustainable development requirements pose challenges for product quality control and manufacturing process optimization for steel production. Building a multi-quality objective process parameter optimization method that converts the original single target optimization into multi-objective interval capability optimization has become a new method to ensure product quality qualification rate and reduce production costs. Aiming at the multi-quality objective control problem of plate products, we proposed a novel multi-objective process parameter interval optimization model (MPPIO) with equipment process control capability and parameter sensitive analysis. The multi-output support vector regression method was used to establish a multi-quality objective prediction model, which was settled as a verification model for the process parameter optimization results based on the particle swarm optimization algorithm (PSO). The process control capability functions of key parameters were fitted based on the real data in production. With these functions, each optimized particle of the classical PSO was converted into the particle beam of the MIPPO. The iteration process was weight controlled by calculating the Morris sensitivity between each input parameter and output index in the multi-quality objective prediction model, and finally the processing control window of each key parameter was determined according to the process parameter optimization results. The experimental results show that the MPPIO model can obtain the optimal parameter optimization results with the maximum processing capacity and meet the customized processing range requirements. The MPPIO model can reduce the difficulty of control and save production costs while ensuring the product properties is qualified.