Title: Imputation method based on collaborative filtering and clustering for the missing data of the squeeze casting process parameters

Abstract

The development of a highly efficient methodology for establishing squeeze casting process parameters from past data is essential. However, designing squeeze casting process parameters based on past data is difficult when there are many missing values. Conventional missing data approaches are fraught with additional computational challenges when applied to highdimensional multivariable missing data, especially material process data with correlation. As the relationship between material composition and process parameters has similar characteristics with that between users and information of interest, we proposed a method for missing data imputation based on a clustering-based collaborative filtering (ClubCF) algorithm to address this challenge. Data samples with and without missing values were divided into two groups. K-means clustering based on a canopy algorithm was applied to the data samples without missing values to obtain k subclass data, whose values were then selected to fill data samples with missing values via a collaborative filtering theory based on Pearson similarity user filling. The missing squeeze casting process parameters data of aluminum alloys were used to evaluate the method, and more comparative experiments were carried out to understand their performance and features. Two different indicators, including the mean absolute error and the standard deviation, were utilized to quantify the imputation performance, which was compared with those of three conventional methods (mean interpolation, regression interpolation, and the expectation maximization algorithm). The results indicate that the proposed approach is effective and outperforms conventional methods in processing high-dimensional correlated data.

Biography

Jianxin Deng is currently working as a professor and a master supervisor of Mechanical Engineering at Guangxi University and the vice director of Guangxi Key Lab of Manufacturing System and Advanced Manufacturing Technology. He received his master degree in industrial engineering from Chongqing University (China) in 2004 and his Ph.D. degree in mechanical engineering from South China University of Technology (China) in 2010. His research interests include manufacturing systems and informatics, squeeze casting technology, E-manufacturing, product axiomatic design, manufacturing service technology.