| 引用本文: | 杨周,刘盼学,王昊,张义民.应用BP神经网络分析电主轴频率可靠性灵敏度[J].哈尔滨工业大学学报,2017,49(1):30.DOI:10.11918/j.issn.0367-6234.2017.01.004 |
| YANG Zhou,LIU Panxue,WANG Hao,ZHANG Yimin.Frequency reliability-based sensitivity analysis of motorized spindle by BP neural networks[J].Journal of Harbin Institute of Technology,2017,49(1):30.DOI:10.11918/j.issn.0367-6234.2017.01.004 |
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| 摘要: |
| 为研究结构参数变化对电主轴抗共振性能的影响，采用ANSYS有限元分析软件，建立某电主轴的参数化模型，并进行模态分析；将ISIGHT软件集成ANSYS，筛选出电主轴重要的几何参数和材料参数作为设计变量，并使用优化的拉丁方抽样方法随机抽取足够的样本；利用BP神经网络拟合出电主轴低阶固有频率与各随机变量的函数关系，建立电主轴频率的可靠性极限状态方程；采用改进的一次二阶矩方法得到电主轴的频率可靠度和可靠性灵敏度，并以Monte-Carlo方法对计算结果进行验证. 结果表明：电主轴的密度、弹性模量及总长度的均值和标准差对电主轴频率可靠度影响显著；利用BP神经网络构建的频率可靠性功能函数较合理；改进一次二阶矩方法能够比较准确、有效地分析频率可靠性灵敏度，且比Monte-Carlo方法效率更高.
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| 关键词: 电主轴 抗共振 模态分析 ISIGHT BP神经网络 可靠性 |
| DOI：10.11918/j.issn.0367-6234.2017.01.004 |
| 分类号:TH133.2 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(3,0, U1234208)；“高档数控机床与基础制造装备”重大专项(2013ZX04011011)；教育部新教师基金(20110042120020)；中央高校基本科研业务费专项资金(N150304006)；机械系统与振动国家重点实验室开放课题(MSV201402)；辽宁省高等学校优秀人才支持计划(LJQ2014030) |
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| Frequency reliability-based sensitivity analysis of motorized spindle by BP neural networks |
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YANG Zhou,LIU Panxue,WANG Hao,ZHANG Yimin
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(School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China)
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| Abstract: |
| To study the anti-resonance of motorized spindle influenced by variations of structural parameters, the structure of one motorized spindle is parameterized firstly using ANSYS and then the modal analysis is carried out. With the ISIGHT platform integrated into ANSYS, several significant geometric and material parameters of the motorized spindle system are selected out as design variables to obtain sufficient samples by Optimal Latin Hypercube method. To fit the function between the low-order natural frequency and the random variables, the BP neural networks are constructed and the reliability limit state equation of the motorized spindle frequency is obtained. Subsequently, the frequency reliability and sensitivity of the motorized spindle, calculated with updating first order second moment method (AFOSM), are verified by Monte-Carlo method. The results show that the density, elastic modulus and the total length of the motorized spindle significantly affect the frequency reliability, in terms of the mean and standard deviation. Meanwhile, the reliability limit state equation constructed by the BP neural networks is relatively rational. The AFOSM to analyze the frequency reliability-based sensitivity is comparatively precise and with higher efficiency than Monte-Carlo method.
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| Key words: motorized spindle anti-resonance modal analysis ISIGHT BP neural networks reliability |
