Optimization Analysis of Product Quality Based on Random Forest and Genetic Algorithm

This paper is based on random forest and genetic algorithm to optimize the quality of strip steel products. Firstly, Pearson correlation analysis is used to point out the process parameters that have an important influence on the mechanical properties of the products, and it is determined that some of the key process parameters have a significant influence on the product indexes. Through the establishment of data-driven quality inspection model, the K-Means clustering algorithm is combined with the Random Forest algorithm to classify the product specifications and other characteristic variables. Finally, the genetic algorithm is used to optimize the product process parameters, so that the product index is close to the set target value, and its feasibility in actual production is verified. The advantage of this paper lies in the organic combination of algorithms to achieve the scientific nature of model construction and parameter optimization.