Co-optimization of magnetic abrasive finishing behaviors of zirconium tube surfaces with Fe-6.5 wt% Si/SiC abrasives using BP neural network and response surface methodology

被引：3

作者：

Zhao, Xudong ^{[1
]}

Zhang, Xinjian ^{[1
]}

Cheng, Bo ^{[1
]}

Li, Wensheng ^{[1
,2
]}

Seniuts, Uladzimir ^{[3
]}

Viktor, Zhornik ^{[3
]}

机构：

[1] Lanzhou Univ Technol, Sch Mat Sci & Engn, State Key Lab Adv Proc & Recycling Nonferrous Met, Lanzhou 730050, Peoples R China

[2] Northwest Normal Univ, Coll Phys & Elect Engn, Lanzhou 730070, Gansu, Peoples R China

[3] Natl Acad Sci Belarus, Joint Inst Mech Engn, Minsk 220072, BELARUS

来源：

MATERIALS TODAY COMMUNICATIONS | 2024年 / 38卷

关键词：

Magnetic abrasive finishing; Surface roughness; BP Neural Network; Response surface method; Parameter optimization; FABRICATION;

D O I：

10.1016/j.mtcomm.2023.107901

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

A novel abrasive of Fe-6.5 wt%Si/SiC composite was prepared for magnetic abrasive finishing (MAF) to achieve fine machining of zirconium alloy tubes. The optimized parameters of magnetic pole speed R= 16.0 r center dot min(-1), tube speed Q= 1220.5 r center dot min(-1), abrasive quality P = 119.3 g, and machining gap N = 2.30 mm were obtained by response surface analysis. The BP neural network model was used to predict the optimal machining effect of MAF. It showed that the predicted results of BP neural network model are quite close to the actual test results, with an error of 7.526%. Finally, the roughness of zirconium alloy tube was reduced from 0.207 mu m to 0.113 mu m, resulting in a 45.41% improvement rate. Fe-6.5 wt% Si/SiC composite abrasive was an efficient magnetic abrasive to improve the surface quality of zirconium alloy parts.

引用

页数：10

共 34 条

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