| SHI Li-chen,LIU Ya-xiong,SHI Wei-chun,LU Zhu-qing,DOU Wei-tao.Optimization of High-precision Cutting Parameters of GH2132 Wire Based on Grey Relational Analysis[J],51(11):373-384 |
| Optimization of High-precision Cutting Parameters of GH2132 Wire Based on Grey Relational Analysis |
| |
| View Full Text View/Add Comment Download reader |
| DOI：10.16490/j.cnki.issn.1001-3660.2022.11.035 |
| KeyWord:surface defects multi-objective optimization centerless lathe grey relational degree feasible process parameter domain |
| Author | Institution |
| SHI Li-chen |
Xi'an University of Architecture and Technology, Xi'an , China |
| LIU Ya-xiong |
Xi'an University of Architecture and Technology, Xi'an , China |
| SHI Wei-chun |
Xi'an University of Architecture and Technology, Xi'an , China |
| LU Zhu-qing |
Western Superconducting Technologies Co., Ltd., Xi'an , China |
| DOU Wei-tao |
Xi'an Aeronautical Polytechnic Institute, Xi'an , China |
|
| Hits: |
| Download times: |
| Abstract: |
| It is an advanced surface improvement technology, which can improve surface properties such as surface roughness, dimensional error, surface microhardness while removing surface defects by turning GH2132 wire. Since GH2132 wire is a raw material for aviation fasteners with a diameter of less than 10 mm and a length of more than 60 m and the processability is poor, the process parameters are the source of improving the surface performance. The work aims to remove surface defects of GH2132 wire by centerless lathe turning, analyze the response relationship between machining parameters of centerless lathe and surface roughness, dimensional error and surface microhardness of wire, and establish a multi-objective optimization model of grey correlation degree of GH2132 wire surface to determine the feasible process parameter domain. Herein, a response surface center composite design was taken to measure the surface roughness, dimensional error, and surface microhardness of the GH2132 wire after turning. The single-objective prediction models of surface roughness, dimensional error, and surface microhardness were established respectively based on the response surface method (RSM), to determine the optimal set of process parameters for single-objective optimization. Then, surface roughness, dimensional error, and surface microhardness were used as optimization indicators to reduce dimensionality and furthermore construct second-order regression prediction model of turning process parameters and grey correlation degree based on Grey Correlation Analysis (GRA) theory. The contour map of turning process parameters and grey correlation degree value was drawn to determine the feasible process parameter domain. The XF-WXC centerless lathe was used in the experiment. The test material was GH2132 wire with a diameter of 8 mm, which was not heated. The length of the test-piece was 1300 mm, and the test process parameters were processed after the trial cutting of 300 mm. The 300 mm interval for trial cutting of each group of process parameters was set as the non-measurement interval. YG8 cemented carbide tools were used in the test. The spindle speeds during cutting were 337, 350, 380, 410 and 422 r/min, respectively. The feed rates were 0.196, 0.3, 0.55, 0.8 and 0.903 m/min, respectively. Then, the surface roughness of the processed specimen was detected with a roughness meter (TR2000). The surface microhardness of the processed specimen was also measured with a hardness tester (Time5310). A digital display micrometer was used to measure the diameter of the processed specimen. The significance was less than 0.000 1 for the established single-objective prediction models of surface roughness, dimensional error, and surface microhardness which were analyzed by variance analysis. Minimum surface roughness process parameter group was:cutting speed n=373.919 r/min and feed speed vf =0.475 m/min. The minimum size error process parameter group was:n=375.636 r/min and vf = 0.596 m/min. Maximum surface microhardness process parameter group was:n=337 r/min and vf = 0.903 m/min. For the grey correlation degree multi-objective prediction model, the error range was between 0.13% and 9.4%, and the minimum grey correlation degree value corresponding to the determined feasible process parameter domain was 0.544 37. The accuracy of the multi-objective prediction model based on grey relational analysis is higher, and the response degree of the spindle speed n to the multi-target is greater than the feed speed vf..Through the determined feasible process parameter domain, it provides engineering reference for removing surface defects of GH2132 wire. |
| Close |
|
|
|