碳钢焊条熔敷金属力学性能遗传神经网络预测

摘要为了更好的反映焊条原材料成分与其熔敷金属力学性能之间的映射关系，本论文对E4303和E5015碳钢焊条进行配方设计和堆焊试验，测定其熔敷金属的抗拉强度、屈服强度、延伸率、冲击功4项力学性能指标。分别采用RBF、BP、GA-BP三种神经网络方法建立由焊条原材料成分预测焊条力学性能的神经网络模型。用这三种神经网络模型对训练样本以外的试验数据进行预测。结果表明用GA-BP结合的计算方法所建立的神经网络模型预测焊条力学性能的平均绝对误差最小。E4303的屈服强度的平均相对误差达到9.83%、抗拉强度的平均相对误差达到13.85%、延伸率的平均相对误差达到13.47%、冲击功的平均相对误差达到9.41%；E5015的屈服强度平均的相对误差达到8.69%、抗拉强度的平均相对误差达到6.41%、延伸率的平均相对误差达到11.25%、冲击功平均的相对误差达到8.35%。7068
关键词碳钢焊条神经网络力学性能预测遗传算法
毕业设计说明书（论文）外文摘要
Title Prediction Of The Deposited Metal Mechanical Properties of Carbon Steel Electrode Based on Genetic Neural Network
Abstract
To acquire a prediction model reflecting the relationship between primary materials formula and the deposited metal mechanical properties of electrodes，formula design and resurfacing welding experiments are made on E4303and E5015 carbon steel electrode．Mechanical properties indexes of deposited metal including tensile strength，yield strength，elongation percentage，impacting energy ．Using the methods of RBF、BP、GA-BP neural network，three models for predicting electrode mechanical properties directly from primary material components are built respectively．The models are used to predict the experiment data except training samples. Results show that the prediction average relative errors of the modle for predicting electrode mechanical properties directly from primary material components base on GA-BP is the least．And the prediction average relative errors of tensile strength of E4303 is9.83%，the yield strength is 13.85%，the elongation percentage is 13.47%，the impacting energy is 9.41%; the prediction average relative errors of tensile strength of E5015 is 8.69%，the yield strength is 6.41%，the elongation percentage is 11.25%，the impacting energy is 8.35%.
Keywords carbon steel electrodes neural network mechanical properties prediction genetic algorithm
目   录

1. 引言   1
1．1 焊条成分预测力学性能的研究现状   1
1. 2 遗传算法、神经网络在预测应用的研究现状   1
1. 3 本课题研究内容   2
2. 碳钢焊条配方与力学性能分析   4
2. 1 酸性焊条配方与力学性能分析   4
2. 2 碱性焊条配方与力学性能分析   5
3. 碳钢焊条熔敷金属力学性能预测   7
3. 1 输入输出数据的确定   7
3. 2 试验数据归一化   10
3. 3 碳钢焊条熔敷金属力学性能预测神经网络模型   11
3.3.1 RBF预测模型   11
3.3.1.1 RBF预测模型结构建立   11
3.3.1.2 RBF网络训练   14
3.3.1.3 RBF网络预测   16
3.3.2   碳钢焊条熔敷金属力学性能BP预测模型   19
3.3.2.1 BP预测模型结构建立   19
3.3.2.2 BP网络训练   22
3.3.2.3 BP网络预测   25
3.3.3   遗传算法优化碳钢焊条熔敷金属力学性能BP预测模型   28
3.3.3.1遗传算法优化BP预测模型结构建立   28 碳钢焊条熔敷金属力学性能遗传神经网络预测:http://www.youerw.com/cailiao/lunwen_4828.html