Process parameters design of squeeze casting through an improved KNN algorithm and existing data

Abstract

Process parameters are key to the production and cast quality of squeeze casting, and conventional methods to obtain the process parameters are based on experiments, which is costly and time-consuming. Based on big data, this paper proposes a data-driven method of designing squeeze-casting process parameters. Based on the potential impact features and differences in the process influential factors on squeeze-casting process parameters, an E-TKNN algorithm was developed by improving the k-nearest neighbor (KNN) algorithm for data clustering to obtain the process parameters of a new squeeze cast based on the similarity principle and existing data. To promote similarity accuracy, the impact difference of the process influential factors is first weighted by assembling the data of the process parameter and the factors using a matrix and calculating the entropy of their data matrix; second, a change trend measurement method is developed to consider the impacts of trace elements. Furthermore, an application framework (SR-E-TKNN) to design the process parameter of squeeze-casting through E-TKNN is established, in which a support vector machine–recursive feature elimination (SVM-RFE) algorithm is introduced to remove the redundant factors to optimize the data input of E-TKNN. Two experiments were conducted to verify the feasibility and superiority of the proposed method. The results demonstrated that the designed process parameter can aid in achieving practical production quality. Compared with the traditional design method, the accuracy of the design method based on E-TKNN outperforms conventional data modeling methods such as linear regression. This method utilizes the existing relevant squeeze-casting process data, eliminates the tedious research process, and provides a new concept for the design of other process parameters.