摘要
目的优化安全阀关闭件研磨工艺参数,提高安全阀密封面研磨质量。方法采用Al2O3砂纸为磨具,通过正交试验研究了磨粒细度、研磨时间、研磨转速、研磨压力对阀座和阀瓣表面粗糙度的影响规律。采用粗糙度测量仪对阀座和阀瓣的表面粗糙度进行检测,初步获得了较好的研磨工艺参数。采用MATLAB中BP神经网络解决非线性映射逼近问题,建立表面粗糙度预测模型,分析安全阀研磨工艺实验得来的16组真实样本数据,并对不同工艺参数下的粗糙度进行预测。结果通过正交试验可以初步获得较好的研磨工艺参数,分别是:磨粒细度1500目、研磨压力100 N、研磨转速100 r/min、研磨时间10 min。进一步设计更全面的正交试验,验证粗糙度模型的预测结果,得到最好的研磨方案是:砂纸细度1500目、研磨压力120 N、研磨转速80 r/min、研磨时间12 min。结论粗糙度预测模型能够很好地预测表面粗糙度,并得到最佳工艺参数,表面粗糙度可以降低到0.074μm,有效地提高了研磨质量。
The work aims to optimize process parameters of safety valve closure member and improve grinding quality of safety valve sealing surface. With Al2O3 sandpaper as abrasive, law of influence of abrasive grain fineness, grinding time, grinding speed and grinding pressure on surface roughness of valve seat and valve flap was studied by performing orthogonal test. The surface roughness of valve seat and valve flap was measured with roughness tester, and better grinding process parameters were obtained preliminarily. Nonlinear mapping approximation was solved with BP neural network in MATLAB. A surface roughness prediction model was established, and 16 sets of real sample data from grinding process experiment of safety valve were analyzed, and roughness under different process parameters was predicted. The optimal process parameters： abrasive grain fineness of 1500 mesh, grinding pressure of 100 N, grinding speed of 100 r/min, grinding time of 10 min, were obtained preliminarily by performing orthogonal test. In order to further design more comprehensive orthogonal test and validate prediction results of the roughness model, the best grinding scheme obtained was： sandpaper fineness of 1500 mesh, grinding pressure of 120 N, grinding speed of 80 r/min, and grinding time of 12 min. The roughness prediction model can be used to predict surface roughness favorably and obtain the optimal process parameters which may reduce surface roughness to 0.074 μm and effective improve grinding quality.
出处
《表面技术》
EI
CAS
CSCD
北大核心
2018年第1期242-248,共7页
Surface Technology
关键词
安全阀关闭件
研磨
BP神经网络
表面粗糙度
预测
safety valve closure members
grinding
BP neural network
surface roughness
prediction
