| 铝合金P-MIG焊稳定性近似熵神经网络预测 |
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| 引用本文： | 聂晶,石玗,黄健康,樊丁. 铝合金P-MIG焊稳定性近似熵神经网络预测[J]. 焊接学报, 2010, 31(8): 77-80 |
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| 作者姓名： | 聂晶 石玗 黄健康 樊丁 |
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| 作者单位： | 兰州理工大学甘肃省有色金属新材料重点实验室,兰州,730050;兰州理工大学有色金属合金及加工教育部重点实验室,兰州,730050 |
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| 基金项目： | 国家自然科学基金,甘肃省教育厅基金,兰州理工大学优秀青年教师培养计划资助项目 |
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| 摘 要： | 通过对不同焊接过程的电压信号进行近似熵计算与分析,得到了能够反映焊接过程稳定性的不同工艺参数与近似熵值的匹配模型和样本数据.在此基础上提出了运用广义回归神经网络(GRNN)对电弧电压信号近似熵进行预测的方法进而对铝合金脉冲MIG焊过程稳定性进行了识别.介绍了铝合金脉冲MIG焊过程稳定性近似熵广义回归神经网络预测模型的结构和算法,并对样本数据进行了预测试验.结果表明,该神经网络电弧电压近似熵预测的平均误差为9.08%,准确率为90.92%,满足用来评价铝合金脉冲MIG焊接过程稳定性的精度要求.
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| 关 键 词： | 脉冲熔化极氩弧焊 电压信号 近似熵 稳定性 广义神经网络 模式识别 |
| 收稿时间： | 2008-12-01 |
Approximate entropy GRNN forecast for aluminum alloy pulsed MIG welding stability |
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| NIE Jing,SHI Yu,HUANG Jiankang and FAN Ding. Approximate entropy GRNN forecast for aluminum alloy pulsed MIG welding stability[J]. Transactions of The China Welding Institution, 2010, 31(8): 77-80 |
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| Authors: | NIE Jing SHI Yu HUANG Jiankang FAN Ding |
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| Affiliation: | Key Laboratory of Non-ferrous Metal Alloys and Processing, The Ministry of Education, Lanzhou University of Technology, Lanzhou 730050, China,State Key Laboratory of Gansu Advanced Non-ferrous Metal Materials, Lanzhou University of Technology, Lanzhou 730050, China,Key Laboratory of Non-ferrous Metal Alloys and Processing, The Ministry of Education, Lanzhou University of Technology, Lanzhou 730050, China and State Key Laboratory of Gansu Advanced Non-ferrous Metal Materials, Lanzhou University of Technology, Lanzhou 730050, China |
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| Abstract: | The model and sample data that able to reflect the stability of the different welding process parameters were obtained through analyzing welding voltage signals in aluminum alloy pulsed MIG welding by approximate entropy.On this basis,a method that predicts the approximate entropy of the voltage signals by generalized regression neural network(GRNN) was proposed.The structure and algorithm of the GRNN prediction model were introduced and the prediction experiments on ample data were done.The results show that the average error of the predictive value is 9.08%,the accuracy rate of it is 90.92%,and the results meet the forecast accuracy of aluminum alloy pulsed MIG welding process stability. |
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| Keywords: | pulse MIG voltage signal approximate entropy stability generalized regression neural network Pattern Recognition |
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