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

被引:0
作者
Yang Hui
Xuesong Mei
Gedong Jiang
Fei Zhao
Pengcheng Shen
机构
[1] Xi’an Jiaotong University,State Key Laboratory for Manufacturing Systems Engineering
[2] Shaanxi Key Laboratory of Intelligent Robots,School of Mechanical Engineering
[3] Xi’an Jiaotong University,undefined
来源
Journal of Intelligent Manufacturing | 2020年 / 31卷
关键词
Assembly consistency improvement; Straightness error; Linear axis; Batch assembly; Consistency degree; GA-MSVM-I-KM;
D O I
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中图分类号
学科分类号
摘要
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.
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页码:1429 / 1441
页数:12
相关论文
共 114 条
  • [1] Altintas Y(2011)Machine tool feed drives CIRP Annals 60 779-796
  • [2] Verl A(2002)SMOTE: synthetic minority over-sampling technique Journal of Artificial Intelligence Research 16 321-357
  • [3] Brecher C(2016)A comprehensive error analysis method for the geometric error of multi-axis machine tool International Journal of Machine Tools and Manufacture 106 56-66
  • [4] Chawla NV(2018)Machining accuracy reliability analysis of multi-axis machine tool based on Monte Carlo simulation Journal of Intelligent Manufacturing 29 191-209
  • [5] Bowyer KW(2018)Geometric error modeling and sensitivity analysis of single-axis assembly in three-axis vertical machine center based on jacobian-torsor model ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering 4 031004-369
  • [6] Hall LO(2016)A new solution to the measurement process planning for machine tool assembly based on Kalman filter Precision Engineering 43 356-10
  • [7] Chen J(2016)Computer-aided sleep staging using complete ensemble empirical mode decomposition with adaptive noise and bootstrap aggregating Biomedical Signal Processing and Control 24 1-5797
  • [8] Lin S(2017)Simulation and analysis for accuracy predication and adjustment for machine tool assembly process Advances in Mechanical Engineering 9 1687814017734475-112
  • [9] Zhou X(2016)A variance change point estimation method based on intelligent ensemble model for quality fluctuation analysis International Journal of Production Research 54 5783-1946
  • [10] Cheng Q(2018)Modeling and elastic deformation compensation of flexural feed drive system International Journal of Machine Tools and Manufacture 132 96-13467