计算机集成制造系统 ›› 2020, Vol. 26 ›› Issue (9): 2344-2354.DOI: 10.13196/j.cims.2020.09.004

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直纹面侧铣加工精度可靠性分析

潘柏松,丁炜,项涌涌,罗路平,俞铭杰   

  1. 浙江工业大学特种装备制造与先进加工技术教育部重点实验室
  • 出版日期:2020-09-30 发布日期:2020-09-30
  • 基金资助:
    国家自然科学基金资助项目(51475425);浙江省自然科学基金资助项目(LQ18E050014)。

Reliability analysis for machining accuracy of side milling of ruled surface

  • Online:2020-09-30 Published:2020-09-30
  • Supported by:
    Project supported by the National Natural Science Foundation,China(No.51475425),and the Natural Science Foundation of Zhejiang Province,China(No.LQ18E050014).

摘要: 针对直纹面侧铣加工中存在动态不确定性因素导致其精度可靠度计算效率低、可靠度预测困难等问题,提出了基于加工切触点高斯过程(GP)模型的精度可靠性分析方法。考虑机床驱动误差与刀具参数不确定性,将切触点坐标变换值应用高斯随机过程表征,并基于LU400型BC轴机床拓扑结构建立精度可靠度模型;通过K-L变换分解切触点随机过程,求解获得精度可靠度GP代理模型;以模型精度指标迭代更新计算模型,并基于代理模型采用蒙特卡洛法获得可靠度值。结果表明,所提方法具有较高的求解效率和精度,为直纹面侧铣加工精度可靠度预测提供了有效的方法。

关键词: 直纹面侧铣加工, 精度可靠度, 代理模型, 高斯随机过程

Abstract: Aiming at the problems of low efficiency of accuracy reliability calculation and difficulty of reliability prediction caused by dynamic uncertainties in side milling of the ruled surface,a method of precision reliability analysis based on Gauss Process(GP)model of processing contact points was proposed.Considering the driving errors of machine tools and the uncertainty of tool parameters,the transformation values of contact coordinates were characterized with Gauss stochastic process,and an accuracy reliability model was established based on the topological structure of LU400 BC-axis machine tool.By decomposing the contact stochastic process with K-L transform,the GP agent model of accuracy reliability was obtained.The calculation model was updated iteratively with the model accuracy index,and the reliability value was obtained by Monte Carlo method based on the agent model.The results showed that the method had high efficiency and accuracy,and provided an effective method for predicting the reliability of the processing accuracy of the side milling of ruled surface.

Key words: side milling of ruled surface, precision reliability, agent model, Gaussian random process

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