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| 基于BP神经网络算法的Ti-6Al-4V激光NiAl-VC合金化的工艺研究 | |
| 引用本文： | 蔡定葆,张群莉,Mykola Anyakin,Ruslan Zhuk,任博,姚建华.基于BP神经网络算法的Ti-6Al-4V激光NiAl-VC合金化的工艺研究[J].应用激光,2013,33(1):18-23. |
| 作者姓名： | 蔡定葆 张群莉 Mykola Anyakin Ruslan Zhuk 任博 姚建华 |
| 作者单位： | 蔡定葆:特种装备制造与先进加工技术教育部/浙江省重点实验室(浙江工业大学), 浙江 杭州 310014浙江工业大学激光加工技术工程研究中心, 浙江 杭州 310014 张群莉:特种装备制造与先进加工技术教育部/浙江省重点实验室(浙江工业大学), 浙江 杭州 310014浙江工业大学激光加工技术工程研究中心, 浙江 杭州 310014 Mykola Anyakin:乌克兰国立科技大学激光技术研究所, 基辅 03056 Ruslan Zhuk:乌克兰国立科技大学激光技术研究所, 基辅 03056 任博:特种装备制造与先进加工技术教育部/浙江省重点实验室(浙江工业大学), 浙江 杭州 310014浙江工业大学激光加工技术工程研究中心, 浙江 杭州 310014 姚建华:特种装备制造与先进加工技术教育部/浙江省重点实验室(浙江工业大学), 浙江 杭州 310014浙江工业大学激光加工技术工程研究中心, 浙江 杭州 310014 |
| 摘 要： | 为了进一步提高Ti-6Al-4V的性能,以满足其在工程中更广泛的运用,研究了在Ti-6Al-4V激光NiAl-VC合金化的工艺。以改变激光功率、激光扫描速度和粉末质量含量比例进行了工艺实验,采用BP神经网络(BP-NN)算法,建立了合金化层性能与工艺参数之间的关系模型,并通过验证实验表明预测效果良好,具有可行性。采用BP-NN算法进行了模拟实验,分析了不同工艺参数条件对合金化层深度、宽度、平均硬度、最高硬度的影响规律。本研究对Ti-6Al-4V激光NiAl-VC合金化的实践应用具有指导意义和参考价值。 |
| 关 键 词： | Ti-6Al-4V BP神经网络 激光合金化 工艺 |
| 收稿时间： | 2012/11/9 |
Study of Laser Alloying Process on Ti-6Al-4V Surface Using NiAl-VC Powder Based on BP Neural Network Algorithm |
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| Cai Dingbao,Zhang Qunli,Mykola Anyakin,Ruslan Zhuk,Ren Bo,Yao Jianhua.Study of Laser Alloying Process on Ti-6Al-4V Surface Using NiAl-VC Powder Based on BP Neural Network Algorithm[J].Applied Laser,2013,33(1):18-23. | |
| Authors: | Cai Dingbao Zhang Qunli Mykola Anyakin Ruslan Zhuk Ren Bo Yao Jianhua |
| Affiliation: | 1,2 (1 Key Laboratory of E&M(Zhejiang University of Technology),Ministry of Education & Zhejiang Province,Hangzhou, Zhejiang 310014,China;2 Research Center of Laser Processing Technology and Engineering,Zhejiang University of Technology, Hangzhou,Zhejiang 310014,China;3 Laser Technology Research Institute,National Technical University of Ukraine "Kiev Polytechnic Institute",Kiev 03056,Ukraine) |
| Abstract: | In order to further improve the performance of Ti-6Al-4V, this paper makes it meet the more widely used in engineering, then trying to study the laser alloying process on Ti-6Al-4V surface using NiAl-VC powder. Experiments with the change of laser power and scanning speed and the mass ratio of powder content were preceded. The relation model between alloyed layer performances and process parameters was established by using BP neural network (BP-NN) algorithm. Experimental verification shows that, the prediction effect is nice and relatively feasible. The influence of different process parameters on the properties, such as depth, width, average hardness and maximum hardness of the alloyed layer, was analyzed by the simulation based on BP neural network algorithm. This research embodies guiding significance and reference value to the application of the laser alloying process on Ti-6Al-4V surface in the industrial manufacturing. |
| Keywords: | Ti-6Al-4V BP neural network laser alloying process |
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