使用CNN-SVR的汽车组合仪表组装质量预测方法

doi:10.3969/j.issn.1004-132X.2022.07.009

摘要/Abstract

摘要： 汽车组合仪表组装过程质检时间长、效率低，因此提出卷积神经网络与支持向量回归相结合的汽车组合仪表组装质量预测方法。结合仪表组装工艺，将卷积神经网络提取的生产数据特征作为支持向量回归的输入，对表征仪表质量的指针偏转角度做出预测。通过车间质检系统获取了仪表原始生产数据，对不同质检情况下的指针偏转角度进行了预测；结果表明所提方法预测误差较小，且具备较强的泛化能力，能够准确有效地预测汽车组合仪表的组装质量。

关键词: 质量预测, 汽车组合仪表, 卷积神经网络, 支持向量回归

Abstract: Due to the long quality inspection time during assembly and lower production efficiency of automotive instrument clusters, an assembly quality prediction method for automotive instrument clusters using CNN-SVR was proposed. Combined with the assembly processes of instrument products, the production data features were extracted through CNN, which were used as the inputs of SVR to predict the pointer deflection angle that characterizesd the quality of instruments. The original production data of the instruments were obtained through the quality inspection systems of the assembly workshops, and the pointer deflection angles under different quality inspection conditions were predicted. The results indicate that proposed method has smaller prediction errors and strong generalization ability, which may accurately and effectively predict the assembly quality of automobile instrument clusters.

Key words: quality prediction, automotive instrument cluster, convolutional neural network(CNN), support vector regression(SVR)

中图分类号:

U463.7

何彦, 肖圳, 李育锋, 吴鹏程, 刘德高, 杜江. 使用CNN-SVR的汽车组合仪表组装质量预测方法[J]. 中国机械工程, 2022, 33(07): 825-833.

HE Yan, XIAO Zhen, LI Yufeng, WU Pengcheng, LIU Degao, DU Jiang. An Assembly Quality Prediction Method for Automotive Instrument Clusters Using CNN-SVR[J]. China Mechanical Engineering, 2022, 33(07): 825-833.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.cmemo.org.cn/CN/10.3969/j.issn.1004-132X.2022.07.009

http://www.cmemo.org.cn/CN/Y2022/V33/I07/825

参考文献

［1］江平宇, 王岩, 王焕发, 等. 基于赋值型误差传递网络的多工序加工质量预测［J］. 机械工程学报, 2013, 49(6):160-170.
JIANG Pingyu, WANG Yan, WANG Huanfa, et al. Quality Prediction of Multistage Machining Processes Based on Assigned Error Propagation Network［J］. Journal of Mechanical Engineering, 2013, 49(6):160-170.
［2］MOHAMMADI P, WANG Z J. Machine Learning for Quality Prediction in Abrasion-resistant Material Manufacturing Process［C］∥2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). Vancouver, 2016:1-4.
［3］武颖, 姚丽亚, 熊辉, 等. 基于数字孪生技术的复杂产品装配过程质量管控方法［J］. 计算机集成制造系统, 2019, 25(6):1568-1575.
WU Ying, YAO Liya, XIONG Hui, et al. Quality Control Method of Complex Product Assembly Process Based on Digital Twin Technology［J］. Computer Integrated Manufacturing Systems, 2019, 25(6):1568-1575.
［4］KIRCHEN I, VOGEL-HEUSER B, HILDENBRAND P, et al. Data-driven Model Development for Quality Prediction in Forming Technology［C］∥IEEE 15th International Conference on Industrial Informatics (INDIN). Emden, 2017:775-780.
［5］刘银华, 孙芮, 吴欢. 基于车身尺寸数据流潜结构建模的装配质量预测控制［J］. 中国机械工程, 2019, 30(2):237-243.
LIU Yinhua, SUN Rui, WU Huan. Latent Structure Modeling and Predictive Quality Control Based on Multi-source Data Streams in the Auto Body Assembly Processes［J］. China Mechanical Engineering, 2019, 30(2):237-243.
［6］任明仑, 宋月丽. 大数据：数据驱动的过程质量控制与改进新视角［J］. 计算机集成制造系统, 2019, 25(11):2731-2742.
REN Minglun, SONG Yueli. Big Data:New Perspective of Process Quality Control and Improvement Driven by Data［J］. Computer Integrated Manufacturing Systems, 2019, 25(11):2731-2742.
［7］GONZALEZ-VAL C, PALLAS A, PANADEIRO V, et al. A Convolutional Approach to Quality Monitoring for Laser Manufacturing［J］. Journal of Intelligent Manufacturing, 2020, 31(3):789-795.
［8］ZHU Jun, CHEN Nan, PENG Weiwen. Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network［J］. IEEE Transactions on Industrial Electronics, 2018, 66(4):3208-3216.
［9］SUN Chuanzhi, LI Chengtian, LIU Yongmeng, et al. Prediction Method of Concentricity and Perpendicularity of Aero Engine Multistage Rotors Based on PSO-BP Neural Network［J］. IEEE Access, 2019, 7:132271-132278.
［10］WAN Xiaodong, WANG Yuanxun, ZHAO Dawei, et al. Weld Quality Monitoring Research in Small Scale Resistance Spot Welding by Dynamic Resistance and Neural Network［J］. Measurement, 2017:120-127.
［11］杨洲, 景博, 张劼, 等. 自动驾驶仪PHM系统健康评估方法研究［J］. 仪器仪表学报, 2012, 33(8):1765-1772.
YANG Zhou, JING Bo, ZHANG Jie, et al. Research on Health Assessment Method of Autopilot Prognostics and Health Management System［J］. Chinese Journal of Scientific Instrument, 2012, 33(8):1765-1772.
［12］ZHENG Maokuan, MING Xinguo, ZHANG Xianyu, et al. Map Reduce Based Parallel Bayesian Network for Manufacturing Quality Control［J］. Chinese Journal of Mechanical Engineering, 2017, 30(5):1216-1226.
［13］朱大业, 丁晓红, 王神龙, 等. 基于支持向量机模型的复杂非线性系统试验不确定度评定方法［J］. 机械工程学报, 2018, 54(8):177-184.
ZHU Daye, DING Xiaohong, WANG Shenlong, et al. Uncertainty Evaluation Method of Complex Nonlinear System Test Based on Support Vector Machine Model［J］. Journal of Mechanical Engineering, 2018, 54(8):177-184.
［14］叶永伟, 陆俊杰, 钱志勤, 等. 基于LS-SVM的机械式温度仪表误差预测研究［J］. 仪器仪表学报, 2016, 37(1):57-66.
YE Yongwei, LU Junjie, QIAN Zhiqin, et al. Study on the Temperature Error Prediction of Mechanical Temperature Instrument Based on LS-SVM［J］. Chinese Journal of Scientific Instrument, 2016, 37(1):57-66.
［15］SONG Lijun, HUANG Wenkang, HAN Xu, et al. Real-time Composition Monitoring Using Support Vector Regression of Laser-induced Plasma for Laser Additive Manufacturing［J］. IEEE Transactions on Industrial Electronics, 2017, 64(1):633-642.
［16］ZHANG Li, ZHOU Weida, CHANG Pei-chann, et al. Iterated Time Series Prediction with Multiple Support Vector Regression Models［J］. Neurocomputing, 2013, 99(1):411-422.
［17］李兵, 韩睿, 何怡刚, 等. 改进随机森林算法在电机轴承故障诊断中的应用［J］. 中国电机工程学报, 2020, 40(4):1310-1319.
LI Bing, HAN Rui, HE Yigang, et al. Applications of the Improved Random Forest Algorithm in Fault Diagnosis of Motor Bearings［J］. Proceedings of the CSEE, 2020, 40(4):1310-1319.
［18］SUI Xiaoyue, LV Zhimin. Prediction of the Mechanical Properties of Hot Rolling Products by Using Attribute Reduction ELM［J］. The International Journal of Advanced Manufacturing Technology, 2016, 85(5/8):1395-1403.
［19］张妍, 王村松, 陆宁云, 等. 基于退化特征相似性的航空发动机寿命预测［J］. 系统工程与电子技术, 2019, 41(6):1414-1421.
ZHANG Yan, WANG Cunsong, LU Ningyun, et al. Remaining Useful Life Prediction for Aero-engine Based on the Similarity of Degradation Characteristics［J］. Systems Engineering and Electronics, 2019, 41(6):1414-1421.
［20］ZHU Zhiyu, PENG Gaoliang, CHEN Yuanhang, et al. A Convolutional Neural Network Based on a Capsule Network with Strong Generalization for Bearing Fault Diagnosis［J］. Neurocomputing, 2019, 323:62-75.
［21］王志学, 刘献礼, 李茂月, 等. 切削加工颤振智能监控技术［J］. 机械工程学报, 2020, 56(24):1-23.
WANG Zhixue, LIU Xianli, LI Maoyue, et al. Intelligent Monitoring and Control Technology of Cutting Chatter［J］. Journal of Mechanical Engineering, 2020, 56(24):1-23.
［22］张冀, 王俊宏, 尉迟明, 等.基于计算机视觉的汽车仪表指针检测方法［J］. 计算机工程与科学, 2013, 35(3):134-139.
ZHANG Ji, WANG Junhong, YUCHI Ming, et al. Novel Automobile Meter Pointer Detection Algorithm Based on Computer Vision［J］. Computer Engineering & Science, 2013, 35(3):134-139.
［23］LI Jialin, LI Xueyi, HE D. A Directed Acyclic Graph Network Combined with CNN and LSTM for Remaining Useful Life Prediction［J］. IEEE Access, 2019, 7：75464-75475.
［24］石琴, 仇多洋, 吴冰, 等. 基于粒子群优化支持向量机算法的行驶工况识别及应用［J］. 中国机械工程, 2018, 29(15):1875-1883.
SHI Qin, QIU Duoyang, WU Bing, et al. DCR and Applications Based on PSO-SVM Algorithm［J］. China Mechanical Engineering, 2018, 29(15):1875-1883.
［25］亓欣波, 李长鹏, 李阳, 等. 基于机器学习的电子束选区熔化成形件密度预测［J］. 机械工程学报, 2019, 55(15):48-55.
QI Xinbo, LI Changpeng, LI Yang, et al. Machinelearning Algorithms on Density Prediction of Electron Beam Selective Melted Parts［J］. Journal of Mechanical Engineering, 2019, 55(15):48-55.
［26］徐文骥, 魏泽飞, 孙晶, 等. 轴承滚子电化学机械光整加工表面质量预测与加工参数选择［J］. 中国机械工程, 2012, 23(5):525-530.
XU Wenji, WEI Zefei, SUN Jing, et al. Surface Quality Prediction and Processing Parameters Determination on Electrochemical Mechanical Finishing of Bearing Roller［J］. China Mechanical Engineering, 2012, 23(5):525-530.
［27］谭峰, 李成南, 萧红, 等. 基于LSTM循环神经网络的数控机床热误差预测方法［J］. 仪器仪表学报, 2020, 41(9):79-87.
TAN Feng, LI Chengnan, XIAO Hong, et al. A Thermal Error Prediction Method for CNC Machine Tool Based on LSTM Recurrent Neural Network［J］. Chinese Journal of Scientific Instrument, 2020, 41(9):79-87.

[1]	阴艳超, 施成娟, 邹朝普, 刘孝保. 基于深度时间卷积神经网络与迁移学习的流程制造工艺过程质量时序关联预测[J]. 中国机械工程, 2023, 34(14): 1659-1671.
[2]	贺青川, 刘慧, 潘骏, 陈文华. 基于电机运行状态的电静压伺服机构内泄漏检测方法[J]. 中国机械工程, 2023, 34(12): 1407-1414.
[3]	曾俊玮, 季元进, 任利惠, 葛方顺, 孙泽良, 黄章行. 融合一维卷积神经网络和双向门控循环单元的APM车辆轮胎径向载荷识别方法[J]. 中国机械工程, 2023, 34(03): 359-368.
[4]	许哲东, 侯公羽, 杨丽, 黄小军. 基于支持向量回归积和改进粒子群算法的特定区间盾构机作业参数选取[J]. 中国机械工程, 2022, 33(24): 3007-3014.
[5]	唐东林, 杨洲, 程衡, 刘铭璇, 周立, 丁超. 浅层卷积神经网络融合Transformer的金属缺陷图像识别方法[J]. 中国机械工程, 2022, 33(19): 2298-2305，2316.
[6]	郭伟, 邢晓松. 基于改进卷积生成对抗网络的少样本轴承智能诊断方法[J]. 中国机械工程, 2022, 33(19): 2347-2355.
[7]	鲍宏, 杨靖, 柯庆镝, 李红真, 么永政, . 基于支持向量回归的熔丝制造3D打印能效优化模型[J]. 中国机械工程, 2022, 33(18): 2215-2226.
[8]	刘会永, 张松, 李剑峰, 栾晓娜, . 采用改进CNN-BiLSTM模型的刀具磨损状态监测[J]. 中国机械工程, 2022, 33(16): 1940-1947，1956.
[9]	李聪波, 龙云, 崔佳斌, 赵希坤, 赵德. 基于多源异构数据的数控铣削表面粗糙度预测方法[J]. 中国机械工程, 2022, 33(03): 318-328.
[10]	郭家昕, 程军圣, 杨宇, . 改进多线性主成分分析网络及其在滚动轴承故障诊断中的应用[J]. 中国机械工程, 2022, 33(02): 187-193,201.
[11]	李敏波, 董伟伟. 面向不平衡数据集的汽车零部件质量预测[J]. 中国机械工程, 2022, 33(01): 88-96.
[12]	揭震国, 王细洋, 龚廷恺. 基于深度学习与子域适配的齿轮故障诊断[J]. 中国机械工程, 2021, 32(22): 2716-2723.
[13]	严建文, 钟小虎, 范煜, 郭三敏, . 基于整群抽样和支持向量回归模型的高功率半导体激光器剩余使用寿命预测[J]. 中国机械工程, 2021, 32(13): 1523-1529.
[14]	刘念聪, 吴圣红, 谢京良, 杨程文, 刘保林, 蒋浩, 陈云. 车削奥氏体不锈钢时冷却参数对刀具振动和表面粗糙度的影响[J]. 中国机械工程, 2021, 32(11): 1321-1329.
[15]	何强, 唐向红, 李传江, 陆见光, 陈家兑. 负载不平衡下小样本数据的轴承故障诊断[J]. 中国机械工程, 2021, 32(10): 1164-1171,1180.