Abstract

Investigating the effect of the uncertainty in the parameters on time-varying chatter stability is critical to refrain from the time-varying regenerative chatter in turning. In this study, the influence of tool wear on the cutting force coefficient (CFC) in turning processing is taken into consideration, and CFC variation during cutting time is described by the Gamma process. The model of the time-varying chatter stability in turning is established based on the motional and mechanical properties of the cutting process in turning. Time-varying reliability (TVR) is estimated based on the uncertain and time-varying characteristics of the turning process parameters. The method of moment-independent time-varying global sensitivity analysis (TV-GSA) based on the cumulative failure probability (CFP) is proposed to measure the effect of parameters on the CFP of the chatter stability in turning. Furthermore, for reducing the computational cost of moment-independent TV-GSA based on CFP, the active learning Kriging model is established to replace the nonlinear and implicit limit state function of the chatter stability in turning. The dynamic model of the turning chatter is validated by an illustrative example. And the results of the proposed method are compared with the results of the Monte Carlo simulation to verify the effectiveness of the proposed method.

摘要

研究参数的不确定性对时变颤振稳定性的影响对于避免转弯时的时变再生颤振至关重要。在这项研究中，考虑了刀具磨损对车削加工中切削力系数（CFC）的影响，并通过伽玛过程描述了切削时间中CFC的变化。基于车削切削过程的运动和力学特性，建立了车削时变颤振稳定性模型。时变可靠性（TVR）是根据车削过程参数的不确定性和时变特性来估计的。提出了基于累积失效概率(CFP)的矩无关时变全局灵敏度分析(TV-GSA)方法来衡量参数对转动颤振稳定性CFP的影响。此外，为了降低基于 CFP 的矩无关 TV-GSA 的计算成本，建立了主动学习 Kriging 模型，以替代转动中颤振稳定性的非线性和隐式极限状态函数。转动颤振的动态模型通过说明性示例进行验证。并将所提方法的结果与蒙特卡罗模拟结果进行对比，验证所提方法的有效性。建立主动学习Kriging模型来替代转动中颤振稳定性的非线性和隐式极限状态函数。转动颤振的动态模型通过说明性示例进行验证。并将所提方法的结果与蒙特卡罗模拟结果进行对比，验证所提方法的有效性。建立主动学习Kriging模型来替代转动中颤振稳定性的非线性和隐式极限状态函数。转动颤振的动态模型通过说明性示例进行验证。并将所提方法的结果与蒙特卡罗模拟结果进行对比，验证所提方法的有效性。