Research on Ground Surface Roughness of Cemented Carbides with Various Grain Sizes Based on RBF Neural Network Prediction AITranslate
Abstract AITranslate
The BLOHM ORBIT 25 precision surface grinder was used to grind nano-grain cemented carbide GUo92,ultrafine-grain cemented carbide GU10UF and GU15UF,and submicron-grain cemented carbide GU20,to study the relationship between the grinding parameters,grain size,Co content and ground surface roughness of the material.The ternary regression analysis method was applied to explore the impact of grinding parameters on alloy surface roughness,and the RBF neural network was used to predict the surface roughness of the nano-grain cemented carbide,which was later analyzed.The results show that the speed of the worktable and the grinding depth are positively correlated with surface roughness while the linear speed of the grinding wheel is negatively correlated with surface roughness.The speed of the worktable is the most influential to surface roughness,while the linear speed of the grinding wheel is in the next place,and the grinding depth has the minimum impact.The smaller the grain size of the cemented carbide,the lower the surface roughness with the same grinding parameters.For cemented carbides with the same grain size but different Co content,the higher the Co content,the greater the surface roughness.RBF neural network could acurately predict the grinding surface roughness.
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Basic Information:
DOI：
Chinese Library Classification Number:TG135.5[一般工业技术—材料科学与工程]
Citation Information:
The BLOHM ORBIT 25 precision surface grinder was used to grind nano-grain cemented carbide GUo92,ultrafine-grain cemented carbide GU10UF and GU15UF,and submicron-grain cemented carbide GU20,to study the relationship between the grinding parameters,grain size,Co content and ground surface roughness of the material.The ternary regression analysis method was applied to explore the impact of grinding parameters on alloy surface roughness,and the RBF neural network was used to predict the surface roughness of the nano-grain cemented carbide,which was later analyzed.The results show that the speed of the worktable and the grinding depth are positively correlated with surface roughness while the linear speed of the grinding wheel is negatively correlated with surface roughness.The speed of the worktable is the most influential to surface roughness,while the linear speed of the grinding wheel is in the next place,and the grinding depth has the minimum impact.The smaller the grain size of the cemented carbide,the lower the surface roughness with the same grinding parameters.For cemented carbides with the same grain size but different Co content,the higher the Co content,the greater the surface roughness.RBF neural network could acurately predict the grinding surface roughness.
quote
| GB/T 7714-2015 | [1] ZHAN You-ji, ZHOU Jia-qi, XU Yong-chao, et al. Research on Ground Surface Roughness of Cemented Carbides with Various Grain Sizes Based on RBF Neural Network Prediction[J]. Rare Metals and Cemented Carbides, 2022(4): 87-93. |
| MLA | [1] ZHAN You-ji, et al., "Research on Ground Surface Roughness of Cemented Carbides with Various Grain Sizes Based on RBF Neural Network Prediction." Rare Metals and Cemented Carbides, no. 4, 2022, pp. 87-93. |
| APA | [1] ZHAN You-ji, ZHOU Jia-qi, XU Yong-chao, CHEN Jia-sheng, & SUN Jia-bao. (2022). Research on Ground Surface Roughness of Cemented Carbides with Various Grain Sizes Based on RBF Neural Network Prediction. Rare Metals and Cemented Carbides(4), 87-93. |
| IEEE | [1] ZHAN You-ji, ZHOU Jia-qi, XU Yong-chao, CHEN Jia-sheng, and SUN Jia-bao, "Research on Ground Surface Roughness of Cemented Carbides with Various Grain Sizes Based on RBF Neural Network Prediction," Rare Metals and Cemented Carbides, no. 4, pp. 87-93, 2022. keywords: {cemented carbide;grain size;Co content;surface roughness;RBF neural network prediction} |