钟厉,李国川,王立文.基于 BP 网络的不锈钢多元离子共渗优化设计[J].表面技术,2014,43(2):79-82.
ZHONG Li,LI Guo-chuan,WANG Li-wen.Multiple Ion Nitriding of Stainless Steel Optimization Design Based on BP Neural Network[J].Surface Technology,2014,43(2):79-82 |
| 基于 BP 网络的不锈钢多元离子共渗优化设计 |
| Multiple Ion Nitriding of Stainless Steel Optimization Design Based on BP Neural Network |
| 投稿时间：2013-11-08 修订日期：2013-12-18 |
| DOI： |
| 中文关键词: 多元离子渗氮 BP 神经网络 优化设计 |
| 英文关键词:multiple ion nitriding BP neural network optimization design |
| 基金项目:重庆市自然科学基金项目( CSTC, 2008BB6348) ;重庆市教委科学技术研究项目( KJ100424) |
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| 中文摘要: |
| 目的 通过神经网络优化不锈钢多元渗氮设计工艺,提高效率与质量。 方法 分析循环周期、乙醇+CS2 气体流量和氨气流量对奥氏体不锈钢 316 Ti 共渗后耐磨、耐腐蚀性能的影响,建立 BP 神经网络,通过预测,寻求不锈钢循环多元离子共渗处理工艺参数。 结果 对利用 BP 神经网络进行工艺优化设计的工艺参数进行实验验证,理论数值与试验数值的偏差在依5% 以内。 结论 新方法脱离传统的经验法与试验法,实现了循环多元离子共渗的优化设计。 |
| 英文摘要: |
| Objective To improve the efficiency and quality through the neural networks optimization of multiple ion nitriding process on stainless steel. Methods The influences of cycle, ethanol, CS2 and ammonia flow on the wear resistance and corrosion resistance of austenitic stainless steel 316Ti in nitriding were analyzed. A BP neural network was established, and parameters for the stainless steel circular multiple ion nitriding process were seeked through prediction. Results The process parameters error was optimized within 依5% by using BP neural network to validate the technical parameters of the process optimization. Conclusion Different from the traditional empirical method and test method, the new method realized the optimization design of multiple ion nitriding. |
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