基于机器学习的0Cr17Ni4Cu4Nb不锈钢流变应力预测研究

赵礼栋; 张又铭; 张继林; 窦建明; 姚家宝

doi:10.7513/j.issn.1004-7638.2023.04.028

基于机器学习的0Cr17Ni4Cu4Nb不锈钢流变应力预测研究

doi: 10.7513/j.issn.1004-7638.2023.04.028

1.
兰州工业学院计算机与人工智能学院, 甘肃兰州 730050
2.
大连交通大学机车车辆工程学院, 辽宁大连 116000
3.
兰州工业学院机电工程学院, 甘肃兰州 730050
4.
兰州交通大学机电工程学院, 甘肃兰州 730070

基金项目: 甘肃省青年科技基金计划项目（21JR7RA351）；甘肃省自然科学基金（20JR5RA376）；甘肃省重点人才项目（甘组通字 [2022]77 号）；国家级大学生创新创业训练计划项目（202211807007）；兰州交通大学甘肃省重点实验室开放课题（2022051）。

详细信息

作者简介:
赵礼栋，1977年出生，男，硕士，副教授，主要从事计算机软件编程、硬件调试以及模型建立研究，E-mail：40241324@qq.com

通讯作者:
窦建明，1985年出生，男，博士，副教授，主要从事智能制造、切削过程状态监测与故障诊断研究，E-mail：lgy_116@163.com

中图分类号: TG142.7,TP301.6
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- 被引次数: 0
出版历程
- 收稿日期: 2022-11-16
- 刊出日期: 2023-08-30

Research on prediction accuracy of the flow stress of 0Cr17Ni4Cu4Nb stainless steel based on machine learning

1.
School of Computer and Artificial Intelligence, Lanzhou Institute of Technology, Lanzhou 730050, Gansu, China
2.
School of Locomotive and Vehicle Engineering, Dalian Jiaotong University, Dalian 116000, Liaoning, China
3.
School of Electrical and Mechanical Engineering, Lanzhou Institute of Technology, Lanzhou 730050, Gansu, China
4.
College of Electrical and Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China

摘要

摘要: 以0Cr17Ni4Cu4Nb不锈钢为例，提出一种基于粒子群优化BP神经网络预测流变应力的新模型。以常温下的准静态（0.001 s⁻¹）压缩试验数据、四种温度（25、350、500、300 ℃）和六种应变率（750、1500、2 000、2600、3500、4500 s⁻¹）的冲击试验数据为基础，构建了 0Cr17Ni4Cu4Nb不锈钢流变应力的随机森林预测模型、粒子群优化随机森林预测模型、Back Propagation（BP）神经网络预测模型以及粒子群优化BP神经网络预测模型，采用统计学的决定系数（R²）、平均绝对误差（MAE）、均方差（MSE）和均方误差平方根（RMSE）四个指标分析评价上述四种模型，得出四种模型预测的综合性能依次是粒子群优化BP神经网络模型、BP神经网络模型、粒子群优化随机森林模型、随机森林模型。粒子群优化BP神经网络模型决定系数R²=0.9997、平均绝对误差MAE=1.5773、均方差MSE=5.5053和均方误差平方根RMSE=2.3463，该模型能够很好预测 0Cr17Ni4Cu4Nb不锈钢流变应力。
- 0Cr17Ni4Cu4Nb不锈钢 /
- 流变应力 /
- 预测模型 /
- 机器学习 /
- 粒子群优化BP神经网络
Abstract: A new model for predicting rheological stresses based on particle swarm optimization BP neural network is proposed for 0Cr17Ni4Cu4Nb stainless steel as an example. Based on the quasi-static (0.001 s⁻¹) compression testing data at room temperature and the impact testing data at four temperatures (25, 350, 500 and 300 ℃) and six strain rates (750, 1500, 2000, 2600, 3500 and 4500 s⁻¹), a random forest prediction model for rheological stress of 0Cr17Ni4Cu4Nb stainless steel, a Particle Swarm Optimized Random Forest prediction model, a Back Propagation (BP) neural network, and a Particle Swarm Optimized BP neural network are constructed. Four indicators including the statistical coefficient of determination (R²), mean absolute error (MAE), mean square error (MSE) and root mean square error (RMSE) are used to analyze and evaluate the four models mentioned above. The comprehensive performance of the prediction models is in sequence of particle swarm optimization BP neural network model, BP neural network model, particle swarm optimization random forest model, and the random forest model. The coefficient of determination R²=0.9997, mean absolute error MAE=1.5773, mean squared error MSE=5.5053 and root mean squared error RMSE=2.3463 are determined for the particle swarm optimized BP neural network model, which can predict the rheological stress of 0Cr17Ni4Cu4Nb stainless steel very well.
- 0Cr17Ni4Cu4Nb stainless steel /
- rheological stress /
- prediction model /
- machine learning /
- particle swarm optimization BP neural network

HTML全文

图 1 随机森林结构

Figure 1. Random forest structure

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图 2 BP神经网络结构

Figure 2. BP neural network structure

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图 3 基于PSO-RF流程

Figure 3. Flow chart based on PSO-RF

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图 4 基于PSO-BP神经网络流程

Figure 4. Flow chart based on PSO-RF neural network

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图 5 不同模型真应力预测和试验值之间的相关性

Figure 5. Correlation between predicted and experimental values of true stress by different models

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表 1 不锈钢0Cr17Ni4Cu4Nb的化学成分

Table 1. Chemical composition of 0Cr17Ni4Cu4Nb stainless steel %

C	Si	Cr	Ni	Mn	P	S	Cu	Nb	Fe
0.05	0.80	16.25	3.60	0.82	0.03	0.02	3.83	0.28	Bal.

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表 2 随机森林预测能力评价指标

Table 2. Random forest predictive capability evaluation index

ntree	R²	MAE	MSE	ESE
5	0.9477	24.4079	1069.2639	32.3405
10	0.9500	23.9846	1022.4408	31.2505
15	0.9526	23.4958	969.3096	30.9611
20	0.9645	20.9364	725.6117	26.8710
25	0.9629	21.1896	759.2660	27.4315
30	0.9676	19.5587	661.3547	25.6921
35	0.9672	20.1650	671.3218	25.9012
40	0.9664	20.2319	687.3605	26.1867
45	0.9684	19.7709	645.1419	25.3661
50	0.9667	20.0062	680.3112	26.0351
55	0.9666	20.1374	681.8699	26.1012
60	0.9647	20.6059	721.0314	26.8389

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表 3 粒子群优化随机森林预测能力评价指标

Table 3. Particle swarm optimization random forest prediction capability evaluation index

ntree	R²	MAE	MSE	ESE
16	0.9672	20.0205	670.9482	25.8998

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表 4 不同神经元个数训练下的评价指标

Table 4. Evaluation metrics under different training numbers of neurons

神经元数量	R²	MAE	MSE	ESE
3	0.9777	16.5449	455.9399	21.3527
5	0.9802	15.4572	404.0375	20.1007
7	0.9951	7.4794	100.2042	10.0102
9	0.9968	6.0398	65.4811	8.092
11	0.9982	4.4221	36.8969	6.0743
13	0.9978	4.7259	45.1895	6.7223

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表 5 不同隐含层个数训练下的评价指标

Table 5. Evaluation metrics under different training numbers of implied layers

隐含层层数	R²	MAE	MSE	ESE
1	0.9982	4.4194	36.9513	6.0788
2	0.9993	2.5569	14.4780	3.8050
3	0.9996	2.0898	8.9598	2.9933
4	0.9995	2.1464	10.3096	3.2109
5	0.9995	2.0498	9.7388	3.1207
6	0.9997	1.5773	5.5429	2.3543

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表 6 粒子群优化BP神经网络训练下的评价指标

Table 6. Evaluation metrics under particle swarm optimization BP neural network training

隐含层结构	R²	MAE	MSE	ESE
12	0.9969	5.8513	63.677	7.9798
12×12	0.9995	2.1863	10.9691	3.312
12×12×8	0.9997	1.4770	5.5053	2.3463
9×11×12×12	0.9997	1.6695	6.6039	2.5698
12×11×10×12×11	0.9996	1.7680	7.3263	2.7067
12×12×12×12×12×12	0.9997	1.5030	5.6700	2.3812

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