基于灰色关联分析的GH2132线材高精度切削参数优化

目的 通过无心车床车削去除GH2132线材的表面缺陷，分析无心车床加工参数对线材表面粗糙度、尺寸误差和表面显微硬度的响应关系，并建立GH2132线材表面灰色关联度多目标优化模型，确定可行工艺参数域。方法 采用响应曲面中心复合设计，测量车削后GH2132线材的表面粗糙度、尺寸误差和表面显微硬度;利用响应曲面法(Response Surface Method，RSM)分别建立表面粗糙度、尺寸误差和表面显微硬度的单目标预测模型，确定单目标优化最优工艺参数组;基于灰色关联分析(Grey Correlation Analysis，GRA)理论，以表面粗糙度、尺寸误差和表面显微硬度为优化指标进行降维处理，构建车削工艺参数与灰色关联度的二阶回归预测模型;绘制车削工艺参数与灰色关联度值的等值线图，确定可行工艺参数域。结果 对建立的表面粗糙度、尺寸误差和表面显微硬度的单目标预测模型进行方差分析，显著度均小于0.000 1。得到了最小表面粗糙度工艺参数组，切削速度n=373.919 r/min，进给速度vf =0.475 m/min。得到了最小尺寸误差工艺参数组，n=375.636 r/min，vf =0.596 m/min。得到了最大表面显微硬度工艺参数组，n=337 r/min，vf = 0.903 m/min。对于灰色关联度多目标预测模型，误差范围为0.13%~9.4%，确定的可行工艺参数域对应的最小灰色关联度值为0.544 37。结论 基于灰色关联分析的多目标预测模型的准确度较高，主轴转速n对多目标的响应程度大于进给速度vf。通过确定可行工艺参数域，为GH2132线材去除表面缺陷提供工程参考。

Optimization of High-precision Cutting Parameters of GH2132 Wire Based on Grey Relational Analysis

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.