基于MPSO-BP对5A06铝合金薄壁件变形预测
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摘要
为预测不同铣削参数下的5A06铝合金薄壁件的加工变形,文章基于BP神经网络和粒子群算法提出了一种新的方法,该方法对粒子群算法中的惯性权重和学习因子进行动态调整并提出了新的惯性权值自适应策略,之后对相关参数进行优化形成改进粒子群优化算法,最后用改进后的粒子群算法优化BP神经网络并将优化后的BP神经网络用于5A06铝合金薄壁件加工变形预测。仿真实验结果表明:MPSO-BP相对于PSO-BP和BP有较小的预测误差,现场加工实验结果进一步说明了MPSO-BP具有良好的预测精度。
In order to predict the machining deformation of 5 A06 aluminum alloy thin-walled parts under different milling parameters, the paper proposes a new method based on BP neural network and particle swarm optimization algorithm, which dynamically adjusts the inertia weight and learning factor in particle swarm optimization,what is more, a new inertia weight adaptive strategy is proposed. Then, the relevant parameters are optimized to form an improved particle swarm optimization algorithm. In the last, the improved particle swarm optimization algorithm is used to optimize BP neural network and the optimized BP neural network is used for 5 A06 aluminum alloy thin-wall piece machining deformation prediction. The simulation results show that MPSO-BP has less prediction error than PSO-BP and BP and the results of field processing experiments further demonstrate that MPSO-BP has good prediction accuracy.
In order to predict the machining deformation of 5 A06 aluminum alloy thin-walled parts under different milling parameters, the paper proposes a new method based on BP neural network and particle swarm optimization algorithm, which dynamically adjusts the inertia weight and learning factor in particle swarm optimization,what is more, a new inertia weight adaptive strategy is proposed. Then, the relevant parameters are optimized to form an improved particle swarm optimization algorithm. In the last, the improved particle swarm optimization algorithm is used to optimize BP neural network and the optimized BP neural network is used for 5 A06 aluminum alloy thin-wall piece machining deformation prediction. The simulation results show that MPSO-BP has less prediction error than PSO-BP and BP and the results of field processing experiments further demonstrate that MPSO-BP has good prediction accuracy.
引文
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[2] JUN Du,ZHENG Ying,WEI Xin,et al.An improved fused deposition modeling process for forming large size thin-walled parts[J].Journal of Materials Processing Tech.,2016,234:332-341.
[3] Sevcan U,Merve P,Mehmet S U.The Discrimination of raw and UHT milk samples contaminated with penicillin G and ampicillin using image processing neural network and biocrystallization methods[J].Journal of Food Composition and Analysis,2013,32(1):12-19 .
[4] ZHENG Hao,SHI Li Feng.Current Status and Future Potential of Neural Networks Used for Medical Image Processing[J].Journal of Multimedia,2011,6(3):244-251.
[5] 郭战杰,王晓莉.基于图像处理和模糊神经网络理论的船舶分类研究[J].舰船科学技术,2017,39(18):52-54.
[6] 吴诗辉,刘晓东,邵悦,等.一种基于神经网络的仿真优化方法[J].系统仿真学报,2018,30(1):36-44.
[7] Qiang Li Peng,Hua Wu.Electric Arc Furnace Based on Fuzzy Neural Network Control System and MATLAB Simulation[J].Applied Mechanics and Materials,2011,1151(48):491-495.
[8]ZHI Shang.Application of artificial intelligence CFD based on neural network in vapor water two-phase flow[J].Engineering Applications of Artificial Intelligence,2005,18(6):663-671.
[9]Koushall K,Gour S,Mitra T.Advanced Applications of Neural Networks and Artificial Intelligence:A Review[J].International Journal of Information Technology and Computer Science(IJITCS),2012,4(6):57-68.
[10] 秦国华,张运建,叶海潮.基于神经网络的薄壁件加工变形预测方法[J].兵工学报,2013,34(7):840-845.
[11] 唐东红 ,孙厚芳,王洪艳.用BP神经网络预测数控铣削变形[J].制造技术与机床,2007(8):48-50.
[12] Zhang Zhi Yong,Li Yan,He Xiao Yang.A Particle Swarm Optimization Algorithm for Neural Networks in Recognition of Maize Leaf Diseases[J].Sensors &Transducers,2014,166(3):181-189.
[13] Mohsen A,Lloyd H C.Improved Particle Swarm Optimization–Based Artificial Neural Network for Rainfall-Runoff Modeling[J].Journal of Hydrologic Engineering,2014,19(7):132-139.
[14] Meissner M,Schmuker,Schneider Gt.Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training.[J].BMC Bioinformatics,2006,125(7):15-19.
[15] 陆亚男,南敬昌,高明明.改进并行粒子群算法优化RBF神经网络建模[J].计算机工程与应用,2017,53(14):45-50.
[16] Dian P,Rini S,Mariyam S,et al.Particle Swarm Optimization:Technique,System and Challenges[J].International Journal of Computer Applications,2011,14(1):19-23.
[17] YU Liu,ZHE Wen Shi,MING Wei.A Survey on Particle Swarm Optimization Algorithms for Multimodal Function Optimization[J].Journal of Software,2011,6(12):244-252.
[18] GUO Fang Fang,HU Yi Bing,XIU Long Ting,et al.A Hierarcchical P2P Model and a Data Fusion Method for Network Security Situation Awareness System[J/OL].Wuhan University Journal of Natural Sciences,2016(2):126-132.
[19] Anjaney J,Bimlesh K.Particle Swarm Optimization Neural Network for Flow Prediction in Vegetative Channel[J].Journal of Intelligent Systems,2013,22(4):487-501.
[20] Cagdas H,Aladag U,Yolcu E E.A New Multiplicative Seasonal Neural Network Model Based on Particle Swarm Optimization[J].Neural Processing Letters,2013,37(3):251-262.
[21] LIU Jian,LI Xi,JI Hong,et al.Multi-user selection based on particle swarm optimization and power control scheme in the uplink CoMP-MU-MIMO system[J].The Journal of China Universities of Posts and Telecommunications,2013,20(5):79-84.
[22] 张必兰.改进的粒子群优化算法及应用研究[D].重庆:重庆大学,2007.
[23] HAI Ying.Research on Network GroupEvent Recognition Based on Neural Network[J].Open Journal of Social Sciences,2016,4(12):86-92.
[24] 赵晶.量子行为粒子群优化算法及其应用中的若干问题研究[D].无锡:江南大学,2013.