发动机曲轴多工序装配的质量预测模型研究
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摘要
针对发动机曲轴回转力矩检测中较大的误差波动性影响装配质量的问题,构建了基于粒子群参数优化(Particle Swarm Optimization,PSO)的最小二乘支持向量机(Least Squares Support Vector Machines,LS-SVM)的发动机曲轴装配质量预测模型。综合考虑了装配质量的不确定性和装配工序相对确定的特征,选取了轴向间隙、同轴度、间隙配合、弯曲度等主要因素作为输入特性,曲轴回转力矩作为输出特性。根据采集整理后的质量数据进行训练学习,利用粒子群算法对最小二乘支持向量机中的参数进行优化,预测曲轴回转力矩。以曲轴回转力矩检测为例,对比分析了神经网络模型,结果表明了该模型的实用性与有效性。
The large error volatility in the torque measurement of engine crankshaft will affect the assembly quality. Therefore an assembly quality prediction model for engine crankshaft based on particle swarm optimization(PSO) of least squares support vector machines(LS-SVM) was constructed. Considering the uncertainty in assembly quality and the relative certainty in assembly process, the paper selected the axial clearance, alignment, clearance fit and deflection as inputs, and chose crank torque as the output. With the sorted data for training and learning from the field, the paper used the particle swarm optimization algorithm of least squares support vector machine for optimization. Then the trained model was applied to predict the corresponding crankshaft torsional moment. In the end, the engine crankshaft torque calculated by using the neural network model was compared and analyzed and the results show the applicability and validity of the proposed model.
The large error volatility in the torque measurement of engine crankshaft will affect the assembly quality. Therefore an assembly quality prediction model for engine crankshaft based on particle swarm optimization(PSO) of least squares support vector machines(LS-SVM) was constructed. Considering the uncertainty in assembly quality and the relative certainty in assembly process, the paper selected the axial clearance, alignment, clearance fit and deflection as inputs, and chose crank torque as the output. With the sorted data for training and learning from the field, the paper used the particle swarm optimization algorithm of least squares support vector machine for optimization. Then the trained model was applied to predict the corresponding crankshaft torsional moment. In the end, the engine crankshaft torque calculated by using the neural network model was compared and analyzed and the results show the applicability and validity of the proposed model.
引文
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[2]尹超,郭晨,赵旭.微车后桥关键工序生产异常损失评估及预警方法[J].计算机集成制造系统,2014,20(10):2533-2541.Yin Chao,Guo Chen,Zhao Xu.Evaluation and Early Warning Method of Abnormal Production Loss for Minicar Rear Axle Key Process[J].Computer Integrated Manufacturing Systems,2014,20(10):2533-2541.(in Chinese)
[3]梁忠权.复杂机电产品关键装配工序物料质量损失预测方法及支持系统[D].重庆:重庆大学,2013.Liang Zhongquan.Quality Loss Forecasting Method and Its Support System for Key Assembly Process Materials Quality Loss for Complex Electromechanical Products[D].Chongqing:Chongqing University,2013.(in Chinese)
[4]尹超,甘德文,梁忠权,等.复杂机电产品关键装配工序物料质量损失评估及预警方法[J].计算机集成制造系统,2014,20(6):1433-1442.Yin Chao,Gan Dewen,Liang Zhongquan,et al.Evaluation and Early Warning Method of Key Assembly Process Materials Quality Loss for Complex Electromechanical Products[J].Computer Integrated Manufacturing Systems,2014,20(6):1433-1442.(in Chinese)
[5]SCHNELLE K D,MAH R S H.Product Quality Management Using a Real-Time Expert System[J].ISIJ International,1994,34(10):815-821.
[6]ZHOU S M.Combining Dynamic Neural Networks and Image Sequences in a Dynamic Model for Complex Industrial Production Processes[J].Expert Systems with Applications,1999,16(1):13-19.
[7]LU N,GAO F.Stage-Based Online Quality Control for Batch Processes[J].Industrial&Engineering Chemistry Research,2006,45(7):2272-2280.
[8]WIDODO A,YANG B S.Support Vector Machine in Machine Condition Monitoring and Fault Diagnosis[J].Mechanical Systems and Signal Processing,2007,21(6):2560-2574.
[9]WU Q,LAW R.The Complex Fuzzy System Forecasting Model Based on Fuzzy SVM with Triangular Fuzzy Number Input and Output[J].Expert Systems with Applications,2011,38(10):12085-12093.
[10]LI J,FREIHEIT T,HU S J,et al.A Quality Prediction Framework for Multistage Machining Processes Driven by an Engineering Model and Variation Propagation Model[J].Journal of Manufacturing Science and Engineering,2007,129(6):1088-1100.
[11]ZHAO C,WANG F,MAO Z,et al.Quality Prediction Based on Phase-Specific Average Trajectory for Batch Processes[J].AICh E Journal,2008,54(3):693-705.
[12]WANG D.Robust Data-Driven Modeling Approach for Real-Time Final Product Quality Prediction in Batch Process Operation[J].IEEE Transactions on Industrial Informatics,2011,7(2):371-377.
[13]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.
[14]王波,唐晓青.机械产品装配过程质量控制决策研究[J].中国机械工程,2010,21(2):164-168,174.Wang Bo,Tang Xiaoqing.Decision-Making of Quality Control for Mechanical Assembly Activities[J].China Mechanical Engineering,2010,21(2):164-168,174.(in Chinese)
[15]VAPNIK V N.The Nature of Statistical Learning Theory[J].Neural Networks IEEE Transactions on,1995,10(5):988-999.
[16]SUYKENS J A K,VANDEWALLE J.Recurrent Least Squares Support Vector Machines[J].Circuits&Systems I Fundamental Theory&Applications IEEE Transactions on,2000,47(7):1109-1114.
[17]Jiang Annan,Liang Bing,Zhang Jiao.Forecasting inSitu Gas Content Using Geological Factors Based on Particle Swarm Optimization and Least Square Support Vector Machine[J].Journal of Liaoning Technical University(Natural Science),2009,28(3):363-366.
[18]Sun Lin,Yang Shiyuan.Prediction for Gas Emission Quantity of the Working Face Based on LS-SVM[J].Journal of China Coal Society,2008,33(12):1377-1380.
[19]KU C C,LEE K Y.System Identification and Control Using Diagonal Recurrent Neural Networks[C]//American Control Conference,Chicago,USA,1992:545-549.
[20]Zhu Hanxin,RAGHUVEER M R.Influence of Representation Model and Voltage Harmonics on Metal Oxide Surge Arrester Diagnostics[J].IEEE Transactions on Power Delivery,2001,21(4):599-603.