锻压技术

基于人工神经网络的运动机构可靠性灵敏度分析

英文标题：Reliability sensitivity analysis on motion mechanism based on artificial neural network

单位：西安航空学院

分类号：V19；TB114.3

出版年,卷(期):页码：2019,44(8):182-188

摘要：

针对运动机构在设计、制造、装配等过程中杆件尺寸及装配位置的不确定性对运动精度的影响，研究了一种基于神经网络的运动机构可靠性灵敏度的分析方法。该方法基于人工神经网络，通过较小的样本量构造出运动机构的功能函数，进而推导其可靠性计算公式，在此基础上结合全局灵敏度分析，推导出运动机构的可靠性灵敏度计算方法，求出随机变量对运动机构可靠性的贡献，即可靠性灵敏度一阶指标、二阶指标、高阶指标和总指标。最后，将研究方法应用于四连杆运动机构与某型航空冲压机床运动机构的分析，研究结果表明该方法可甄别出随机变量对运动机构可靠性的贡献情况，通过该结果可以有效地提高运动机构的可靠性和稳健性，具有较高地工程应用价值，也可为其他机构设计提供理论指导。

The influence of the uncertainty of linkage dimension and assembly position on the motion accuracy was considered during the design, manufacture and assembly processes of the motion mechanism, and a reliability sensitivity analysis method of motion mechanism based on the artificial neural network was studied. Based on the artificial neural network, the function of motion mechanism was constructed by small samples, and its reliability calculation formula was deduced. Then, combing with the global sensitivity analysis, the reliability sensitivity calculation method of the motion mechanism was derived, and the contribution of random variables to the reliability of motion mechanism was obtained, that is, the firstorder index, second-order index, high-order index and total index of the reliability sensitivity. Finally, the fourbar linkage motion mechanism and aeronautical stamping machine motion mechanism were analyzed by the proposed method. The results show that the researched method can identify the contribution of random variables to the reliability of motion mechanism, and the reliability and robustness of motion mechanism can be improved, which has not only higher engineering application value but also provides theoretical guidance for the other mechanism design.

基金项目：

陕西省教育厅科研计划项目 (18JK0410)；西安航空学院校级基金项目(2019KY1230)；西安航空学院校级基金项目(2015KY1215)

作者简介：唐成虎（1991-），男，硕士，助教 E-mail：chenghutang@126.com

参考文献：

[1] Dubourg V, Sudret B, Bourinet J M. Reliabilitybased design optimization using kriging surrogates and subset simulation[J]. Structural and Multidisciplinary Optimization, 2011, 44(5): 673-690.

[2] Du X, Sudjianto A. First order saddlepoint approximation for reliability analysis[J]. AIAA journal, 2004, 42(6): 1199-1207.

[3] Zhao Y G, Ono T. A general procedure for first/secondorder reliabilitymethod (FORM/SORM)[J]. Structural Safety, 1999, 21(2): 95-112.

[4] Au S K, Beck J L. Important sampling in high dimensions[J]. Structural Safety, 2003, 25(2): 139-163.

[5] 赵劲彪, 潘玉竹, 黎定仕, 等. 发射平台摆杆机构可靠性分析[J]. 现代机械, 2018(5): 76-79.

Zhao J B, Pan Y Z, Li D S, et al. Reliability analysis of launching platform swing rod mechanism[J]. Modern Machinery, 2018(5): 76-79.

[6] 徐琦, 罗路平, 夏力. 数控机床传动机构精度可靠性优化研究[J]. 机电工程, 2018, 35(10): 1030-1035，1047.

Xu Q, Luo L P, Xia L. Optimization of precision reliability for the transmission mechanism of NC machine [J]. Journal of Mechanical & Electrical Engineering, 2018, 35(10): 1030-1035，1047.

[7] 游令非, 张建国, 翟浩, 等.模糊-随机混合参数的机构运动可靠度计算方法[J].北京航空航天大学学报，2019，45（4）：714-721.

You L F, Zhang J G, Zhai H, et al. Compution method on motional reliability of mechanism under mixed parameters with fuzziness and randomness [J]. Journal of Beijing University of Aeronautics and Astronautics，2019，45（4）：714-721.

[8] 刘涛. 齿轮机构的运动精度可靠性分析[D]. 成都：西华大学, 2018.

Liu T. Reliability Analysis on Kinematics Accuracy of Gear Mechanism[D]. Chengdu: Xihua University, 2018.

[9] 聂飞飞, 周金宇, 曹清林. 高速经编机槽针机构运动精度可靠性分析计算[J]. 现代制造技术与装备, 2017，(9): 33-37.

Nie F F, Zhou J Y, Cao Q L. Analysis and calculation of kinematics accuracy reliability on needle mechanism of highspeed warp knitting machine[J]. Modern Manufacturing Technology and Equipment, 2017, (9):33-37.

[10]Lu Z, Song S, Yue Z, et al. Reliability sensitivity method by line sampling[J]. Structural Safety, 2008, 30(6): 517-532.

[11]Song S, Lu Z, Qiao H. Subset simulation for structural reliability sensitivity analysis[J]. Reliability Engineering & System Safety, 2009, 94(2): 658-665.

[12]吕震宙, 李璐祎, 宋述芳, 等. 不确定性结构系统的重要性分析理论与求解方法[M]. 北京：科学出版社, 2015.

Lyu Z Z, Li L Y, Song S F, et al. Importance Analysis Theory and Solution Method of Uncertain Structural Systems[M]. Beijing: Science Press, 2015.

[13]王文选, 高行山, 周长聪. 基于点估计的矩独立重要性测度分析方法[J]. 机械工程学报, 2017, 53(8): 16-24.

Wang W X, Gao H S, Zhou C C. Momentindependent importance measure analysis method based to pointestimate[J]. Journal of Mechanical Engineering, 2017, 53(8): 16-24.

[14]周长聪, 张峰, 王文选, 等. 随机激励下的随机结构全局灵敏度分析[J]. 高技术通讯, 2015, 25(10-11): 956-963.

Zhou C C, Zhang F, Wang W X, et al. Global sensitivity analysis for stochastic structures under random excitation [J]. Chinese High Technology Letters, 2015, 25(10-11): 956-963.

[15]刘付超, 魏鹏飞, 周长聪, 等. 含旋转铰间隙平面运动机构可靠性灵敏度分析[J]. 航空学报, 2018, 39(11): 222-230.

Liu F C, Wei P F, Zhou C C, et al. Timedependent reliability and sensitivity analysis for planar motion mechanism with revolution joint clearances[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(11): 222-230.

[16]王鑫辉，常琮尧，杜苏睿，等. 3PRR全柔顺并联机构的水平集多目标拓扑优化设计及灵敏度分析[J].锻压技术，2018,43(1):181-188.

Wang X H，Chang Z Y，Du S R，et al. Multiobjective topology optimization design and sensitivity analysis on 3PRR fully compliance parallel mechanism based on levelset method [J]. Forging & Stamping Technology，2018,43(1):181-188.

[17]Haykin S S. Neural Networks and Iearning Machines[M]. New York: Prentice Hall, 2009.

[18]林伟路，丁小凤，双远华. BP神经网络对斜轧穿孔轧制力的预测[J].锻压技术，2018,43(10):175-178.

Lin W L，Ding X F，Shuang Y H. Prediction on rolling force of oblique rolling piercing based on BP neural network [J].Forging & Stamping Technology，2018, 43(10):175-178.

[19]张涛，樊文欣，郭代峰，等. 基于BP神经网络的温挤压模具磨损量预测[J]. 锻压技术，2017，42（2）：178-182.

Zhang T，Fan W X，Guo D F，et al. Prediction on wear loss of warm extrusion die based on BP neural network [J]. Forging & Stamping Technology，2017，42（2）：178-182.

[20]唐成虎, 周长聪, 侯伟, 等. 考虑性能退化的飞机典型部件灵敏度分析[J]. 西安交通大学学报,2019，53(4)：158-166.

Tang C H, Zhou C C, Hou W, et al. A sensitivity analysis of typical aircraft components with performance degradation [J]. Journal of Xi′an Jiaotong University,2019, 53(4)：158-166.

[21]Sobol I M. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates[J]. Mathematics and Computers in Simulation, 2001, 55(1): 271-280.

服务与反馈：

【文章下载】【加入收藏】