| 基于神经网络和粒子群算法的激光熔覆工艺优化 |
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| 引用本文： | 倪立斌,刘继常,伍耀庭,鄢锉. 基于神经网络和粒子群算法的激光熔覆工艺优化[J]. 中国激光, 2011, 0(2) |
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| 作者姓名： | 倪立斌 刘继常 伍耀庭 鄢锉 |
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| 作者单位： | 湖南大学汽车车身先进设计制造国家重点实验室;中南大学粉末冶金国家重点实验室;广汽长丰汽车股份有限公司永州分公司; |
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| 基金项目： | 国家科技支撑计划(2007BAF29B01); 教育部新世纪优秀人才支持计划(NCET-08-0183);教育部长江学者与创新团队发展计划(531105050037)资助课题; 中国博士后科学基金项目(20060390879); 湖南大学汽车车身先进设计制造国家重点实验室自主课题(60870005) |
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| 摘 要： | 采用反向传播(BP)神经网络和粒子群算法相结合的方法对激光熔覆过程的工艺参数进行优化。运用BP神经网络建立熔覆带特征(熔覆带高度、熔覆带宽度)与熔覆工艺参数之间的预测模型,利用训练样本对所建立的网络进行训练,形成输入与输出之间的高度映射关系,在此基础上,使用粒子群优化算法对工艺参数进行寻优。试验结果表明,使用该方法优化得到的工艺参数进行加工获得的结果与预期结果有较小的误差,有利于获得预期的熔覆质量。
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| 关 键 词： | 激光技术 激光熔覆 反向传播神经网络 粒子群算法 工艺优化 |
Optimization of Laser Cladding Process Variables Based on Neural Network and Particle Swarm Optimization Algorithms |
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| Ni Libin Liu Jichang, Wu Yaoting Yan Cuo State Key Laboratory of Advanced Design , Manufacturing for Vehicle Body,Hunan University,Changsha,Hunan ,China State Key Laboratory for Powder Metallurgy,Central South University,Hunan ,China GAC Changfeng Motor Co.Ltd Yongzhou Branch,Yongzhou,Hunan ,China. Optimization of Laser Cladding Process Variables Based on Neural Network and Particle Swarm Optimization Algorithms[J]. Chinese Journal of Lasers, 2011, 0(2) |
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| Authors: | Ni Libin Liu Jichang Wu Yaoting Yan Cuo State Key Laboratory of Advanced Design Manufacturing for Vehicle Body Hunan University Changsha Hunan China State Key Laboratory for Powder Metallurgy Central South University Hunan China GAC Changfeng Motor Co.Ltd Yongzhou Branch Yongzhou Hunan China |
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| Affiliation: | Ni Libin1 Liu Jichang1,2 Wu Yaoting3 Yan Cuo1 1State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body,Hunan University,Changsha,Hunan 410082,China 2State Key Laboratory for Powder Metallurgy,Central South University,Hunan 410083,China 3GAC Changfeng Motor Co.Ltd Yongzhou Branch,Yongzhou,Hunan 425001,China |
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| Abstract: | Combination of back propagation(BP)neural network and particle swarm optimization(PSO) algorithms is used to optimize process variables during the laser cladding.BP neural network model is developed to express the relationship between the clad process variables and the clad parameters(the width,height of clad bead),and the samples obtained in experiments are used to train network model to form the perfect map relation between input and output.Then,PSO algorithm is used to grabble the suitable values of the ... |
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| Keywords: | laser technique laser cladding back propagation neural network particle swarm optimization process variables optimization |
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