Assembly consistency improvement of straightness error of the linear axis based on the consistency degree and GA-MSVM-I-KM

Abstract

Fluctuation on the assembly quality of the linear axis of machine tools (LA-MT) at the same batch is urgent problem need to be solved in assembly of machine tools. In this paper, a new concept of assembly consistency degree was introduced for defining the fluctuation degree of assembly quality. Based on assembly consistency degree, a hybrid machine learning method, genetic algorithm optimized multi-class support vector machine and improved Kuhn–Munkres (GA-MSVM-I-KM) was proposed for improving assembly consistency of LA-MT. The assembly of linear axis of a three-axis vertical machining center was regarded as an example, and the assembly consistency influence factors on straightness error of Y-axis (SE-YA) were analyzed through the Kruskal–Wallis statistical method. The main factors affected on the assembly consistency of SE-YA turned out to be the machining errors of bed and the assembly team technical levels. Based on this, the assembly consistency improvement model was established. Then, the prediction model of SE-YA based on assembly experiment data and genetic algorithm optimized multi-class support vector machine (GA-MSVM) was constructed, and I-KM method was applied for improving assembly consistency of SE-YA. The results show that the GA-MSVM-I-KM method can effectively enhance the assembly consistency of SE-YA, and the assembly consistency degree is reduced from 0.19 to 0.08.