杨振凯,王海军,刘明,王晶晨.基于 BP 神经网络的 Fe 基合金粉末喷涂工艺参数优化[J].表面技术,2015,44(9):1-6.
YANG Zhen-kai,WANG Hai-jun,LIU Ming,WANG Jing-chen.Optimization of Spraying Process Parameters for Fe-based Alloy Based on BP Neural Network Model[J].Surface Technology,2015,44(9):1-6 |
| 基于 BP 神经网络的 Fe 基合金粉末喷涂工艺参数优化 |
| Optimization of Spraying Process Parameters for Fe-based Alloy Based on BP Neural Network Model |
| 投稿时间：2015-06-23 修订日期：2015-09-20 |
| DOI：10.16490/j.cnki.issn.1001-3660.2015.09.001 |
| 中文关键词: 超音速等离子喷涂 Fe 基合金粉 BP 神经网络 非线性拟合 输出预测 参数优化 |
| 英文关键词:supersonic plasma spray Fe base alloy powder BP neural network nonlinear fitting output prediction parameters optimization |
| 基金项目:国家自然科学基金面上项目(51175513) |
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| 中文摘要: |
| 目的 基于 BP 神经网络具有自学习、自训练和输出预测的功能,将其应用于热喷涂过程中的参数优化问题。 方法 依托高效能超音速等离子喷涂系统实验平台,以 Fe 基合金粉末为喷涂材料,将等离子喷涂中的主气流量、电功率和喷涂距离作为模型输入,涂层沉积速率和硬度作为模型输出,不断调整隐含层节点个数,最终建立 3-7-2 网络结构的 BP 神经网络以优化工艺参数。 利用优化出的工艺参数制备Fe 基合金涂层,测试其性能,并计算误差。 结果 神经网络优化出的最优喷涂工艺参数为:主气流量 96L/ min,电功率56 kW,喷涂距离95 mm。 采用该工艺参数制备涂层,涂层增厚实测平均值为360 μm,硬度为 672HV0. 3,而模型的预测值分别为 332 μm 和 611HV0. 3,与预测值的相对误差分别为 7. 8% 和 9. 1% 。结论 BP 神经网络对等离子喷涂参数优化问题的拟合精度比较高,误差在可以接受的范围之内。 将 BP神经网络运用于热喷涂工艺参数的优化具有科学性和可操作性。 |
| 英文摘要: |
| Objective BP neural network has the capability of self-learning, self training and output prediction, which could be a powerful tool to research the parameter optimization problem in thermal spraying process. Methods Relying on the high-efficiency supersonic plasma spray system (HEPJet) platform, using Fe-based alloy powder as the spraying material, the flow rate of main gas, spraying power and distance were set as the inputs of the model, while the coating deposition rate and hardness were set as model outputs. Through continuous adjustment of the number of hidden layer nodes, the BP neural network with a 3-7-2 network structure was eventually built to optimize the process parameters. The optimized parameters were then used to obtain the Fe-based alloy coating, test its performance and calculate the error. Results The optimized parameters according to the neural network optimized were: main gas flow 96 L / min, electric power 56 kW, spraying distance 95 mm. After the experiment, the coating hardness and deposition rate of coating were measured. Its average increment of coating thickness was 360 μm, and the average increment of coating hardnessis was 672HV0. 3, while the model predicted values were 332 μm and 611HV0. 3, respectively. Comparing with the predicted values, the errors were 7. 8% and 9. 1% , respectively. Conclusion According to the results of simulation and experiment, the accuracy of the BP neural network for the optimization of plasma spray parameters was relatively high, and the error was acceptable. It is scientific and reliable to use BP neural network to deal with the problems of thermal spraying parameters optimization |
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