Assembly quality evaluation for linear axis of machine tool using data-driven modeling approach

Abstract

During the batch assembly analysis of linear axis of machine tool, assembly quality evaluation is crucial to reduce assembly quality fluctuations and improve efficiency. This study presented a data-driven modeling approach for evaluating assembly quality of linear axis based on normalized mutual information and random sampling with replacement (NMI-RSWR) variable selection method, synthetic minority over-sampling technique (SMOTE), and genetic algorithm (GA)-optimized multi-class support vector machine (SVM). First, a variable selection method named NMI-RSWR was proposed to select key assembly parameters which affected assembly quality of linear axis. Then, a hybrid method based on SMOTE and GA-optimized multi-class SVM was presented to construct assembly quality evaluation model. In this method, Class imbalance problem was solved by using SMOTE, and parameters optimization problem was solved by using GA. Finally, the assembly-related data from the batch assembly of x-axis of a three-axis vertical machining center were collected to validate the proposed method. The results indicate that the proposed NMI-RSWR approach has capacity for selecting the highly related assembly parameters with assembly quality of linear axis, and the proposed data-driven modeling approach is effective for assembly quality evaluation of linear axis.