谢楠,周俊锋,郑蓓蓉.考虑能耗的多传感器融合加工表面粗糙度预测方法[J].表面技术,2018,47(9):240-249.
XIE Nan,ZHOU Jun-feng,ZHENG Bei-rong.An Approach for Surface Roughness Prediction in Machining Based on Mul-ti-sensor Fusion Considering Energy Consumption[J].Surface Technology,2018,47(9):240-249
考虑能耗的多传感器融合加工表面粗糙度预测方法
An Approach for Surface Roughness Prediction in Machining Based on Mul-ti-sensor Fusion Considering Energy Consumption
投稿时间:2018-05-30  修订日期:2018-09-20
DOI:10.16490/j.cnki.issn.1001-3660.2018.09.032
中文关键词:  能耗  多传感器融合  表面粗糙度  预测方法  核主成分分析  支持向量机
英文关键词:energy consumption  multi-sensor fusion  surface roughness  prediction approach  KPCA  SVM
基金项目:国家自然科学基金项目(71471139);国家工信部智能制造标准化项目
作者单位
谢楠 1.同济大学 a.中德工程学院,上海 201804 
周俊锋 1.同济大学 b.机械与能源工程学院,上海 201804 
郑蓓蓉 2.温州大学 机电工程学院,浙江 温州 325000 
AuthorInstitution
XIE Nan 1.a.Sino-German College of Applied Sciences, Shanghai 201804, China 
ZHOU Jun-feng 1.b.School of Mechanical Engineering, Tongji University, Shanghai 201804, China 
ZHENG Bei-rong 2.College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325000, China 
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中文摘要:
      目的 提出一种考虑能耗的多传感器融合加工表面粗糙度预测方法,精确预测零件表面粗糙度。方法 首先采集车削过程中的功率和振动信号,测量加工表面粗糙度值,利用集成经验模态分解(Ensemble empirical mode decomposition,EEMD)和小波包分析提取振动信号的时域与频域特征,联合功率信号的时域特征、能耗特征与切削参数,构造联合多特征向量。然后采用核主成分分析(Kernel principal component analysis,KPCA)对联合多特征向量进行融合降维处理生成融合特征。最后将融合特征作为基于支持向量机(Support vector machine,SVM)的表面粗糙度预测模型的输入特征,并使用遗传算法(Genetic algorithm,GA)对SVM模型相关核参数进行优化以提高预测精度。结果 预测得到的表面粗糙度平均相对误差为4.91%,最大误差为0.111 µm,预测时间为9.24 s。与单传感器预测方法及多传感器联合特征预测方法相比,多传感器融合预测方法具有最高的准确率且预测速度快。结论 多传感器采集的信息更全面、准确,保证了预测的准确性,对特征进行融合可进一步提高预测精度。
英文摘要:
      In order to predict surface roughness of the workpiece accurately, an approach for surface roughness prediction in machining based on multi-sensor fusion considering energy consumption is proposed. The power signal and the vibration signal of the turning process were collected firstly. Surface roughness was measured. The time domain and frequency domain characteristics of the vibration signal were extracted by using the EEMD and wavelet packet analysis. And the vibration features with the time domain feature of power signal, energy features and cutting parameters constructed the joint multi-eigenvectors. Then the KPCA was used to fuse the joint multi-eigenvectors to generate the fusion feature. Finally, the fusion feature was taken as the input characteristic of the SVM-based surface roughness prediction model. In addition,GA was used to optimize the relevant kernel parameters of SVM model to improve the prediction accuracy. For the prediction results obtained by multi-sensor fusion, the mean relative error was 4.91%, the maximum error was 0.111 µm and the prediction time was 9.24 seconds. The experimental results showed the proposed method had the highest prediction accuracy compared with the joint feature and single sensor. A comparative study with multi-sensor joint feature prediction method and signal-sensor feature prediction method shows the information collected by multi-sensor is more comprehensive and accurate, which ensures prediction accuracy and the prediction accuracy can be further improved by fusing the features.
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