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| 基于多层ANN的机器人等离子熔射智能化模型 | |
| 引用本文： | 夏卫生,张海鸥,王桂兰,杨云珍.基于多层ANN的机器人等离子熔射智能化模型[J].焊接学报,2009,30(7):41-44. |
| 作者姓名： | 夏卫生 张海鸥 王桂兰 杨云珍 |
| 作者单位： | 1. 华中科技大学材料成形与模具技术国家重点实验室,武汉,430074;数字制造装备与技术国家重点实验室,武汉,430074 2. 数字制造装备与技术国家重点实验室,武汉,430074 3. 华中科技大学材料成形与模具技术国家重点实验室,武汉,430074 |
| 基金项目： | 国家高技术研究发展计划(863计划)，国家自然科学基金，中国博士后科学基金 |
| 摘 要： | 分析了机器人等离子熔射过程的神经网络模型的实现方法,基于多层人工神经网络(antificial neural network,ANN)建立了等离子熔射过程的智能化模型.基于该模型,系统研究了等离子弧电流、熔射距离、机器人扫描间距和速度对主要涂层性能参数-残余应力和孔隙率的影响规律,并通过试验数据库的学习对涂层性能参数进行预测.结果表明,模型预测结果与试验结果有着很好的吻合,解决了工艺试验结果中仅有离散数据且难以全面反映等离子熔射工艺参数一涂层性能之间复杂非线性关系的难题. |
| 关 键 词： | 机器人等离子熔射 人工神经网络 智能化模型 残余应力 孔隙率 |
| 收稿时间： | 2008/7/21 0:00:00 |
Intelligent process modeling of robotic plasma spraying based on multi-layer artificial neural network |
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| XIA Weisheng,ZHANG Haiou,WANG Guilan and YANG Yunzhen.Intelligent process modeling of robotic plasma spraying based on multi-layer artificial neural network[J].Transactions of The China Welding Institution,2009,30(7):41-44. | |
| Authors: | XIA Weisheng ZHANG Haiou WANG Guilan and YANG Yunzhen |
| Affiliation: | State Key Laboratory of Material Processing and Die & Mould Technology, Huazhong Universiry of Technology, Wuhan 430074, China;State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science & Technology, Wuhan 430074, China,State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science & Technology, Wuhan 430074, China,State Key Laboratory of Material Processing and Die & Mould Technology, Huazhong Universiry of Technology, Wuhan 430074, China and State Key Laboratory of Material Processing and Die & Mould Technology, Huazhong Universiry of Technology, Wuhan 430074, China |
| Abstract: | The implementation of multi-layer artificial neural networks (ANNs) in robotic plasma spraying was discussed and an intelligent process model was constructed to fully describe the relationships between process parameters and coating properties. Influences of plasma arc current, spray distance, robot scanning space and scanning velocity on coating properties, i.e. residual stress and porosity, were systematically studied based on the present model. Prediction can be effectively carried out after the learning of the experimental database. Theoretical analysis shows the prediction results agree well with the experiments. It is favorable to fully investigate the complex and nonlinear relationships between processing parameters and coating properties as well as to overcome the limited information indicated by the discrete variable in the processing results. |
| Keywords: | robotic plasma spraying artificial neural network intelligent model residual stress porosity |
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