文章摘要
徐承亮,曹志勇,王大军,胡吉全.基于SGPLVM-LSSVM算法的U形折弯件模型参数优化研究[J].机床与液压,2018,46(20):29-32.
.Research on Parameter Optimization of U shaped Bending Parts Model Based on SGPLVM-LSSVM Algorithm[J].Machine Tool & Hydraulics,2018,46(20):29-32
基于SGPLVM-LSSVM算法的U形折弯件模型参数优化研究
Research on Parameter Optimization of U shaped Bending Parts Model Based on SGPLVM-LSSVM Algorithm
  
DOI:10.3969/j.issn.1001-3881.2018.20.007
中文关键词: U形折弯件  支持向量机模型  监督学习-高斯过程隐变量模型
英文关键词: U shaped bending parts  Least square support vector machine  Supervised gaussian process latent variable model
基金项目:国家自然基金面上项目(51675201);材料成形国家重点实验室开放基金资助项目(P2018-006)
作者单位E-mail
徐承亮 广州科技贸易职业学院信息工程学院 281552074@qq.com 
曹志勇   
王大军   
胡吉全   
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中文摘要:
      影响高强度U形折弯件回弹的因素众多,比如工件尺寸、力学性能和负载条件等,使得高强度折弯件的弯曲回弹难以控制。把回弹角α和最小弯曲回弹半径R作为双目标函数,首先利用监督学习-高斯过程隐变量模型(SGPLVM)进行变量筛选和降维,构建U形折弯件的最小二乘支持向量机模型(LSSVM);再把SGPLVM-LSSVM实验结果分别与SVM、 FEM、实际零件进行比较,验证了此算法模型的可行性。
英文摘要:
      There are many factors influencing springback of high strength U shaped bending parts, such as workpiece size, mechanical properties and load conditions, which make bending springback of high strength bending parts be difficult to control. The minimum bending radius R and the springback angle α were taken as two objective functions. Firstly, supervised gaussian process latent variable model (SGPLVM) was used for variable selection and dimensionality reduction, the least squares support vector machine (LSSVM) model for U shaped bending part was constructed. The prediction results of SGPLVM-LSSVM were compared with SVM, FEM prediction results and actual engineering parts to verify the feasibility of the proposed model.
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