喷丸处理后6061铝合金工件表面粗糙度的模拟计算及预测

doi:10.13229/j.cnki.jdxbgxb20171272

摘要/Abstract

摘要：

以6061铝合金工件为对象，研究了喷丸对工件表面粗糙度的影响。首先，建立了喷丸有限元仿真模型，分别模拟计算了单丸粒喷丸和多丸粒喷丸后铝合金工件的表面形貌。对应进行了单丸粒喷丸和多丸粒喷丸试验，采用激光共聚焦显微镜获取了喷丸后实际工件的表面形貌，以验证所建立的喷丸有限元仿真模型的有效性。在此基础上，对铝合金工件开展了9组喷丸的正交模拟试验，通过对喷丸表面进行离散计算得到三维粗糙度参数 S _q，研究获得工件初始粗糙度、丸粒尺寸、丸粒速度和喷丸覆盖率对工件表面粗糙度的影响规律。采用BP神经网络模型对正交试验结果进行处理，获得铝合金工件喷丸处理后表面粗糙度的预测模型。将预测的经喷丸处理后的工件表面粗糙度与模拟试验的粗糙度数据进行对比可知，两者的平均误差仅为3.46%，预测精度能够满足实际喷丸需要；相应构建了喷丸处理后铝合金工件的表面粗糙度控制图，这对实际喷丸过程中较准确控制工件的表面粗糙度有重要价值。

关键词: 金属材料, 喷丸处理, 6061铝合金, 表面粗糙度, 神经网络, 预测模型

Abstract:

The purpose of this study is to analyze the effect of shot peening on the surface finish of the workpiece, in which the AA6061 workpiece is taken as example. Firstly, a FEM model of shot peening is established, based on which the surface morphology of single shot peening and multiple shot peening are simulated. Corresponding experiments of single shot peening and multiple shot peening are carried out and the practical surface morphology of the workpiece after the shot peening is observed by the Laser Scanning Confocal Microscope, by which the validity of the established FEM model of shot peening is verified. Then, 9 groups of orthogonal simulations are executed to study the influence of the initial roughness of target material, shot size, shot speed and shot peening coverage on the final surface roughness of the workpiece described by the 3D roughness parameter S _q. The prediction model of the surface roughness of the workpiece after the shot peening is established by means of the BP neural network model. Based on this prediction model the predicted surface roughness and the simulated surface roughness are compared and the mean error is 3.46%. This prediction accuracy can meet the requirement of the shot peening production. Furthermore, the controlling map of AA6061 workpiece after shot peening is constructed, which is important to control the final surface finish in the practical shot peening processing. The methodology presented in this paper can be extended to the application of other metallic material besides aluminum alloy, which would be helpful for the simulation, prediction and controlling the surface roughness of the workpiece after the shot peening processing.

Key words: metal material, shot peening, 6061 aluminum alloy, surface roughness, neural network model, prediction model

中图分类号:

TG146.2

徐戊矫,刘承尚,鲁鑫垚. 喷丸处理后6061铝合金工件表面粗糙度的模拟计算及预测[J]. 吉林大学学报(工学版), 2019, 49(4): 1280-1287.

Wu⁃jiao XU,Cheng⁃shang LIU,Xin⁃yao LU. Simulation and prediction of surface roughness of 6061 aluminum alloy workpiece after shot peening[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1280-1287.

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试验编号	A	B	C	D	E
1	0	0.2	50	100	4.8402
2	0	0.6	75	200	11.7275
3	0	1.0	100	300	19.6275
4	3.2	0.2	75	300	3.7858
5	3.2	0.6	100	100	31.8285
6	3.2	1.0	50	200	13.7609
7	6.4	0.2	100	200	6.9918
8	6.4	0.6	50	300	7.7602
9	6.4	1.0	75	100	38.2005