The rotation frame is a key component of the amusement equipment. The natural frequency and buckling strength of the rotation frame have a direct impact on the stability of the structure. Finite element software ANSYS Workbench was used to establish a finite element model of the rotation frame. The natural frequency that had the most significant influence on the rotation frame was obtained through the modal analysis and harmonic response analysis. The buckling eigenvalue when the structure was unstable was obtained through buckling stability analysis. Then 140 test points were obtained by using the experimental design. In order to explore the sensitivity of various design variables to the natural frequency and buckling strength of the rotation frame, a BP(back propagation) neural network mathematical model was established through MATLAB software to fit test points. Combining the use of Isight and MATLAB, a descriptive Monte Carlo sampling method was used to numerically simulate the neural network model. The research results showed that the most dangerous modes that had a significant influence on the stability of rotation frame were the 10th to 12th modes. The result of parameter sensitivity analysis indicated that the parameters which had a mainly affect on the rotation frame dangerous modal frequencies were the limb wall thickness, limb section length and limb section width, in addition, the structure parameters which had the most significant influence on the structural buckling stability were six dimensional parameters of the slewing shank, the web, and the circular plate. The conclusion shows that the reasonable improvement of a part of design parameters that effect on the stability of the rotation frame will effectively improve the structural stability and design efficiency. This will provide a certain reference for the subsequent design improvement and optimization of the structure.
TANG Lin, XU Zhi-pei, HE Tian-long, AO Wei-chuan. Sensitivity analysis of rotation frame stability based on BP neural network. Chinese Journal of Engineering Design, 2018, 25(5): 576-582.
王新刚,张义民,王宝艳.机械零部件的动态可靠性灵敏度分析[J].机械工程学报,2010,46(10):188-193. WANG Xin-gang, ZHANG Yi-min, WANG Bao-yan. Dynamic reliability sensitivity analysis of mechanical components[J]. Journal of Mechanical Engineering, 2010, 46(10):188-193.
[[2]]
张峰,杨旭锋,刘永寿,等.飞机起落架缓冲器参数可靠性灵敏度分析[J].振动工程学报,2015,28(1):67-72. ZHANG Feng, YANG Xu-feng, LIU Yong-shou, et al. Reliability parameter sensitivity analysis for aircraft landing gear shock absorber[J]. Journal of Vibration Engineering, 2015, 28(1):67-72.
[[3]]
张春来.具有柔性支撑的起重机臂架结构稳定分析方法研究[D].哈尔滨:哈尔滨工业大学机电工程学院,2017:49-53. ZHANG Chun-lai. Research on stability analysis method of crane jib structure with flexible support[D]. Harbin:Harbin Institute of Technology, School of Mechatronics Engineering, 2017:49-53.
[[4]]
李欣,裴朋超.桁架臂的稳定性灵敏度分析与优化[J].建设机械技术与管理,2017(3):81-85. LI-Xin, PEI Peng-chao. Analysis and optimization of stability sensitivity for the lattice jib[J]. Construction Machinery Technology & Management, 2017(3):81-85.
[[5]]
申士林.类椭圆截面伸缩臂接触非线性分析及结构轻量化技术研究[D].成都:西南交通大学机械工程学院,2013:31-38. SHEN Shi-lin. Nonlinearcontact analysis of similar-oval jib and research of structural lightweight technique[D]. Chengdu:Southwest Jiaotong University, School of Mechanical Engineering, 2013:31-38.
[[6]]
胡波,许志沛,吴永明,等.基于神经网络的桑巴塔回转架参数灵敏度分析[J].机械设计与制造,2017(S1):149-152. HU-Bo, XU Zhi-pei, WU Yong-ming, et al. Parameter sensitivity analysis of rotary frame of the Samba tower based on artificial neural network[J]. Machinery Design & Manufacture, 2017(S1):149-152.
[[7]]
李文涛.大型游乐设备——"桑巴塔"的回转架力学特性研究[D].成都:西南交通大学机械工程学院,2016:11-13. LI Wen-tao. The research of mechanical properties of Samba tower rotary frame-large amusement equipment[D]. Chengdu:Southwest Jiaotong University, School of Mechanical Engineering, 2016:11-13.
[[8]]
李永奎,韩美玲.基于SolidWorks-Motion大型回转游乐设备动力学仿真分析[J].机械设计与制造,2016(3):51-53. LI Yong-kui, HAN Mei-ling. Based on SolidWork-Motion large rotary amusement equipment dynamic simulation analysis[J]. Machinery Design & Manufacture,2016(3):51-53.
[[9]]
吴迪,武岳,杨庆山,等.大跨屋盖结构风振响应参数灵敏度分析[J].工程力学,2015,32(2):171-177. WU Di, WU Yue, YANG Qing-shan, et al. Parameter sensitivity analysis of wind-induced response of long-span proofs[J]. Engineering Mechanics, 2015, 32(2):171-177.
[[10]]
李惠林,周兵兵,刘倩,等.载重货车驱动桥壳谐响应分析[J].机械设计与制造,2013(9):101-106. LI Hui-lin, ZHOU Bing-bing, LIU Qian, et al. Harmonic response analysis for heavy-duty lorry drive axle housing[J]. Machinery Design & Manufacture, 2013(9):101-106.
[[11]]
苏少普,陈先民,董登科.含凸台连接整体加筋壁板结构的稳定性分析[J].机械设计,2015,32(8):57-61. SU Shao-pu, CHEN Xian-min, DONG Deng-ke. Buckling analysis of integral stiffened panels with filleted junction[J]. Journal of Machine Design, 2015, 32(8):57-61.
[[12]]
邓乾旺,文文.基于拉丁超立方抽样的薄板装配误差分析[J].中国机械工程,2012,23(8):947-951. DENG Qian-wang, WEN Wen. Sheet metal assembly deviation analysis based on Latin hypercube sampling[J].China Mechanical Engineering, 2012, 23(8):947-951.
[[13]]
喻曦.基于人工神经网络的舰船灵敏度分析[J].舰船科学技术,2015,37(3):147-150. YU Xi. Parametric sensitivity analysis of ships based on neural network[J]. Ship Science and Technology, 2015, 37(3):147-150.
[[14]]
何琴淑,杨玉明,肖世富.参数概率灵敏度分析的神经网络方法及其应用[J].计算力学学报,2011,28(S1):29-32. HE Qin-shu, YANG Yu-ming, XIAO Shi-fu. Neural network method in parametric probabilistic sensitivity analysis and its application[J]. Chinese Journal of Computational Mechanics, 2011, 28(S1):29-32.
[[15]]
张雷.基于神经网络技术的结构可靠性分析与优化设计[D].长春:吉林大学机械工程学院,2004:19-22. ZHANG Lei. Neural network-based structure reliability and optimization design[D]. Changchun:Jilin University, School of Mechanical Engineering, 2004:19-22.
[[16]]
南东雷,贾志新,李威.三参数威布尔分布的蒙特卡洛点估计方法[J].机械设计与制造,2017(1):142-144. NAN Dong-lei, JIA Zhi-xin, LI Wei. Monte Carlo based parametric point estimation for three parameter Weibull distribution[J]. Machinery Design & Manufacture, 2017(1):142-144.
[[17]]
AHAMMED M, MELCHERS R E. Gradient and parameter sensitivity estimation for systems evaluated using Monte Carlo analysis[J]. Reliability Engineering and System Safety, 2006, 91(5):594-601.