混合粒径磨料的磁粒研磨及光整加工试验

刘冰洋; 丁云龙; 邵文杰; 韩冰; 陈燕

doi:10.13394/j.cnki.jgszz.2024.0078

混合粒径磨料的磁粒研磨及光整加工试验

doi: 10.13394/j.cnki.jgszz.2024.0078

刘冰洋^{1, 2},
丁云龙^{1, 2,},
邵文杰^{1, 2},
韩冰^{1, 2},
陈燕^{1, 2}

1.
辽宁科技大学机械工程与自动化学院, 辽宁鞍山 114051
2.
辽宁省复杂工件表面特种加工重点实验室, 辽宁鞍山 114051

基金项目: 辽宁省教育厅科学研究经费项目（2019LNQN01）；辽宁省科技厅博士启动经费（2021-BS-241）。

详细信息

作者简介:
丁云龙，男，1988年生，博士、副教授、硕士生导师。主要研究方向：磁粒研磨光整加工。E-mail：dylustl@163.com

中图分类号: TQ73; TG58
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- 被引次数: 0
出版历程
- 收稿日期: 2024-04-25
- 修回日期: 2024-06-07
- 录用日期: 2024-06-24
- 刊出日期: 2025-06-30

Magnetic particle grinding and finishing test of mixed particle size abrasives

LIU Bingyang^{1, 2},
DING Yunlong^{1, 2
,},
SHAO Wenjie^{1, 2},
HAN Bing^{1, 2},
CHEN Yan^{1, 2}

1.
School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan 114051, Liaoning, China
2.
Liaoning Key Laboratory of Special Machining for Complex Workpiece Surface, Anshan 114051, Liaoning, China

摘要

摘要: 在磁粒研磨及光整加工试验中，相比于单一粒径磨料，采用混合粒径磨料能够提高磁性磨粒产生的磁粒刷的刚性和密度，进而提高加工效果。为探究混合粒径磨料磁粒研磨及光整加工的最佳工艺参数，基于响应曲面法，采用铁基氧化铝磁性磨料在SM4多功能机床上对SUS304不锈钢钢板工件的表面进行加工。以加工后工件的表面粗糙度R_a为响应值，对试验过程中的主轴转速、磨料质量比、磨料粒径比等主要试验参数进行优化和分析。结果表明：在主轴转速为511 r/min、磨料质量比为1.67、磨料粒径比为2.00的最佳参数组合下，工件的表面粗糙度R_a由0.244 μm的原始值降为0.036 μm的试验值，且R_a试验值与预测值0.038 μm相比，二者相对误差的绝对值为5.26%。采用混合粒径磨料的最佳参数组合进行光整加工，可有效去除工件表面的划痕，降低其表面粗糙度并提高其表面质量。
- 磁粒研磨 /
- 混合粒径磨料 /
- 响应曲面法 /
- 磨料粒径比 /
- 表面粗糙度
Abstract: Objectives Magnetic particle grinding finishing technology as an advanced processing technology can achieve high precision surface treatment. In order to simplify the test device, reduce the test cost and improve the processing effect, the single particle size abrasive is changed to mixed particle size abrasive without changing the test device, so as to improve the grinding effect of magnetic particles. Methods Finite element analysis software is used to simulate the magnetic field in the machining area, and the magnetic field data is imported into the discrete element simulation software through an API interface to obtain the magnetic field during the simulation process, in order to simulate the force situation of mixed particle size abrasive and single particle size abrasive during machining. Taking the spindle speed of the machine tool (A), the abrasive mass ratio (B) and the abrasive particle size ratio (C) as the research objects, the experimental parameters are analyzed and optimized using response surface methodology. To prevent abrasive splashing and reduce the grinding effect, the selected experimental parameters ranges are 400 to 600 r/min for A, 0.50 to 2.00 for B, and 1.5 to 2.5 for C. Using the surface roughness R_a of the workpiece as the response value, the Box Behnken method is used for response surface test design. Results The P-value of the variance analysis of the model experiment results is less than 0.000 1, indicating that the experimental model is highly significant. The mismatch term refers to the unexplained error in the model, with a P-value of 0.458 1, much greater than 0.050 0, indicating that the mismatch term is not significant and the regression equation fitted by the software is valid. At the same time, the multiple correlation coefficient R² is 0.996 2, and R²_Adj is 0.989 4 after verification, which is very close to 1.000 0, indicating a good fit of the model. Moreover, the surface roughness is affected by the spindle speed, abrasive mass ratio and abrasive particle size ratio to 98.94%. The results of the single factor experiment indicate that the order of influence on surface roughness R_a is the spindle speed, followed by the abrasive mass ratio and the abrasive particle size ratio. When the spindle speed is 500 r/min and the abrasive mass ratio is 1.25, the workpiece surface roughness R_a reaches the minimum value. In the case of a certain abrasive mass ratio, when the abrasive particle size ratio is 2.0 and the spindle speed is 500 r/min, the workpiece surface roughness R_a reaches its minimum value. Under the condition of constant spindle speed, when the abrasive mass ratio is 0.50 and the abrasive particle size ratio is 2.5, the workpiece surface roughness R_a reaches the maximum. However, when the abrasive particle size ratio is 2.0 and the appropriate mass ratio is 1.25, the surface roughness R_a of the workpiece can reach the minimum value. Aiming at the minimum surface roughness R_a of the workpiece, the response surface software is used to optimize the data, and the optimal process parameter combination for workpiece processing is obtained, that is, the spindle speed is 511 r/min, the abrasive mass ratio is 1.67, the abrasive particle size ratio is 1.9, and the predicted surface roughness R_a value after processing is 0.038 µm. When the workpiece is machined under the optimal process parameters, the surface roughness R_a of the workpiece decreases from the original value of 0.244 μm to the test value of 0.036 μm, and the absolute value of relative error between the two is 5.26%. Conclusions The experimental results show that the established model is effective, and the process parameters that affect the surface roughness R_a of the workpiece are in the order of spindle speed, followed by abrasive mass ratio and abrasive particle size ratio. Compared to single-abrasive magnetic particle grinding, the use of mixed abrasives can further reduce the surface roughness of the workpiece and improve its machining effect.
- magnetic particle grinding /
- mixed particle size abrasive /
- response surface method /
- abrasive particle size ratio /
- surface roughness

HTML全文

图 1 磨料在磁场中的受力图

Figure 1. Force diagram of abrasives in magnetic field

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图 2 仿真模型

Figure 2. Simulation model

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图 3 试验装置

Figure 3. Test setup

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图 4 实际值与预测值对比图

Figure 4. Comparison chart between actual and predicted values

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图 5 R_a预测值与外学生化残差分布图

Figure 5. Distribution diagram of R_a predicted values and external studentized residuals

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图 6 2种研磨参数对粗糙度的影响

Figure 6. Influence of two grinding parameters on roughness

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图 7 加工前后工件的表面粗糙度轮廓曲线

Figure 7. Surface roughness profile curves of workpiece before and after processing

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图 8 加工前后工件的表面形貌变化

Figure 8. Changes in surface morphology of workpieces before and after processing

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表 1 加工参数

Table 1. Processing parameters

参数	规格或取值
工件尺寸	150 mm × 120 mm × 10 mm
工件材料	SUS304不锈钢
加工间隙 d / mm	1.5
加工时间 t / min	15
进给速度 v_w / (m·min⁻¹)	3
磁极头	N35永磁体，ф 20 mm
磨料填充量 m / g	1.5

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表 2 Box-Behnken试验的因素、水平及编码

Table 2. Factors, levels and codes of Box-Behnken tests

水平及编码	因素
水平及编码	主轴转速 n / (r·min⁻¹) A	磨料质量比 R₁ B	磨料粒径比 R₂ C
−1	400	0.50	1.5
0	500	1.25	2.0
1	600	2.00	2.5

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表 3 响应面试验结果

Table 3. Experimental results of response surfaces

序号	A	B	C	表面粗糙度 R_a / μm
1	−1	−1	0	0.083
2	1	−1	0	0.077
3	−1	1	0	0.081
4	1	1	0	0.053
5	−1	0	−1	0.072
6	1	0	−1	0.068
7	−1	0	1	0.086
8	1	0	1	0.063
9	0	−1	−1	0.056
10	0	1	−1	0.044
11	0	−1	1	0.061
12	0	1	1	0.051
13	0	0	0	0.041
14	0	0	0	0.039
15	0	0	0	0.042

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表 4 方差分析结果

Table 4. Results of ANOVA

项目	平方和	自由度	均方差	F	P	显著性
模型	0.0036	9	0.0004	145.6200	<0.0001	非常显著
A	0.0005	1	0.0005	167.1100	<0.0001	非常显著
B	0.0003	1	0.0003	103.4700	0.0002	显著
C	0.0001	1	0.0001	19.8100	0.0067	显著
AB	0.0001	1	0.0001	43.4700	0.0012	显著
AC	0.0001	1	0.0001	32.4300	0.0023	显著
BC	1.000 × 10⁻⁶	1	1.000 × 10⁻⁶	0.3593	0.5750
A²	0.0025	1	0.0025	899.6400	<0.0001	非常显著
B²	0.0002	1	0.0002	61.1900	0.0005	显著
C²	0.0001	1	0.0001	40.7400	0.0014	显著
残差	0.0000	5	2.783 × 10⁻⁶
失拟项	9.250 × 10⁻⁶	3	3.083 × 10⁻⁶	1.32	0.4581	不显著
纯误差	4.667 × 10⁻⁶	2	2.333 × 10⁻⁶
总和	0.0037	14
	R²=0.996 2			校核后R_Adj²=0.989 4

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