Process parameters design of squeeze casting through an improved KNN algorithm and existing data
Process parameters design of squeeze casting through an improved KNN algorithm and existing data
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基于改进KNN算法和现有数据的挤压铸造工艺参数设计
DOI：
10.1016/j.jmapro.2022.10.074
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发表时间：
2022-11-14
影响因子：
6.2
通讯作者：
Liang, Jiawei
中科院分区：
文献类型：
--
作者：
Deng, Jianxin;Xie, Bin;Liang, Jiawei
挤压铸造工艺参数是影响挤压铸造生产和铸件质量的关键因素，传统的工艺参数获取方法都是通过实验获得，成本高，耗时长。提出了一种基于大数据的数据驱动的挤压铸造工艺参数设计方法。针对挤压铸造工艺参数的潜在影响特征和工艺影响因素的差异，基于相似性原理和已有数据，通过改进k-最近邻（KNN）数据聚类算法，提出了一种E-TKNN算法来获取新挤压铸造工艺参数。为提高相似度的准确性，首先通过对工艺参数和影响因素的数据进行矩阵组装，并计算其数据矩阵的熵值，对工艺影响因素的影响差异进行加权;其次，发展了一种考虑微量元素影响的变化趋势度量方法。在此基础上，建立了E-TKNN在挤压铸造工艺参数设计中的应用框架（SR-E-TKNN），引入支持向量机-递归特征消除（SVM-RFE）算法去除冗余因素，优化E-TKNN的数据输入。通过两个实验验证了该方法的可行性和优越性。结果表明，设计的工艺参数可以帮助实现实际的生产质量。与传统的设计方法相比，基于E-TKNN的设计方法的精度优于线性回归等传统的数据建模方法。该方法利用了现有的相关挤压铸造工艺数据，省去了繁琐的研究过程，为其他工艺参数的设计提供了新的思路。
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 po-tential 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.