EA4T车轴机器人磨抛工艺优化与轨迹规划研究

张峰; 冯中立; 徐锋; 张德明; 曾祥瑞; 马建伟; 张石磊

doi:10.13394/j.cnki.jgszz.2024.0187

EA4T车轴机器人磨抛工艺优化与轨迹规划研究

doi: 10.13394/j.cnki.jgszz.2024.0187

1.
中车青岛四方机车车辆股份有限公司, 山东青岛 266000
2.
大连理工大学机械工程学院, 高性能精密制造全国重点实验室, 辽宁大连 116024

详细信息

作者简介:
通信作者：张峰，1977年生，学士学位，正高级工程师，主要从事轨道车辆转向架工艺工作。E-mail: zhangfeng@cqsf.com

中图分类号: TG580.6
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出版历程
- 收稿日期: 2024-11-30
- 修回日期: 2025-01-18
- 录用日期: 2025-02-14
- 刊出日期: 2025-04-20

Research on process optimization and trajectory planning of EA4T axle robot grinding

1.
CRRC Qingdao Sifang Co., Ltd., Qingdao 266000, Shandong, China
2.
State Key Laboratory of High-Performance Precision Manufacturing, School of Mechanical Engineering,Dalian University of Technology, Dalian 116024, Liaoning, China

摘要

摘要: 为突破动车组EA4T车轴人工磨抛时作业强度大、加工质量不稳定等困境，采用工业机器人智能磨抛系统研究EA4T钢试件的磨抛工艺，并提出EA4T车轴机器人磨抛轨迹离线编程方法。首先对EA4T钢试件进行机器人磨抛正交试验；然后采用熵值法对试验结果进行多目标工艺优化，得出最优磨抛工艺参数组合；最后采用离线编程方法规划EA4T车轴轴肩部位的磨抛轨迹，并将生成的加工程序导入机器人示教器进行磨抛轨迹试验验证。研究表明，优化后的磨抛工艺参数组合为磨头目数400#（筛网孔径为0.038 mm）、磨抛力15 N、进给速度50 mm/s、主轴转速750 r/min。采用该参数组合磨抛后，EA4T钢试件的表面粗糙度R_a为0.338 μm、材料去除深度h为1.67 μm，均符合指标要求。采用最优磨抛工艺参数配合机器人磨抛轨迹规划方法，能够快速精准完成EA4T车轴轴肩部位的磨抛作业，具有一定的工程应用价值。
- EA4T车轴 /
- 机器人磨抛 /
- 工艺优化 /
- 磨抛轨迹 /
- 离线编程
Abstract: Objectives The EA4T axle is a critical load-bearing component of electric multiple unit (EMU) train bodies, directly influencing operational safety and reliability. As a high-end product with stringent technical requirements and complex manufacturing processes, the shoulder position of the EA4T axle is stressed repeatedly and there is stress concentration during service. Consequently, in the process of axle production, it is necessary to grind the axle shoulder to control its surface roughness and material removal depth. Current manual grinding methods for EA4T axle shoulder suffer from high labor intensity, inconsistent surface quality, and low efficiency. In order to effectively break through the current manual grinding dilemma of the EMU EA4T axle, the implementation of flexible grinding using an industrial robotic intelligent grinding system equipped with a constant-force control device presents a feasible solution to replace manual operations and achieve automated processing. Therefore, it is essential to carry out research on the grinding process of EA4T steel components, and explore the grinding process methods that meet the surface quality requirements of EA4T axle machining. Combined with the off-line programming method for EA4T axle robot grinding trajectory, the axis shoulder grinding trajectory is planned and the robot machining program is generated to realize high-quality and efficient automatic grinding of the EA4T axle by robot. Methods Firstly, an independently developed robotic intelligent constant-force grinding system serves as the experimental platform. EA4T steel specimens with dimensions of 150 mm × 63 mm × 9 mm are prepared as test pieces. Based on the quality control requirement that the surface roughness of the EA4T axle after grinding must not exceed 0.4 μm, and considering the actual situation of manual grinding process parameters, a Taguchi method-based orthogonal experiment with four factors and four levels is designed and implemented. In the experiment, a hand-held surface roughness measuring instrument is used to measure the surface roughness after grinding, and a precision analytical balance is used to measure the weight of the specimen before and after grinding to calculate the material removal depth. Thus, the surface roughness and the material removal depth of the specimen under different process parameters are obtained. Secondly, analysis of variance and significance testing are conducted to determine the significance level of the influence of each process parameter on the experimental results. The influence of the grit size of grinding tools, grinding force, feed speed, and spindle speed on the surface roughness and material removal depth is analyzed. Then, by calculating the entropy of each index to determine the weight coefficient, the surface roughness and material removal depth in the experimental results of each group are converted into comprehensive score values for evaluation. The optimal grinding process parameter combination with minimum surface roughness and material removal depth is obtained through comprehensive score range analysis. Finally, the off-line programming method is employed to establish a virtual model of the robotic intelligent grinding system within the robot off-line programming software. The 3D model of the EA4T axle is imported into the virtual environment. Based on the flexible grinding module at the end-effector, parameters including grinding head dimensions, end-effector tools, and trajectory configurations are defined. The robot machining system program SRC file is generated and subsequently transferred to the robot teach pendant. The grinding force, feed rate and spindle speed corresponding to the optimal grinding process parameters are entered into the control system. Physical grinding experiments are conducted on EA4T axle prototypes to validate the feasibility of the proposed grinding methodology. Results Through the grinding orthogonal experiments and physical verification experiments, the following results are obtained. (1) The order of influence of grinding process parameters on the surface roughness of EA4T steel is: abrasive grit size > spindle speed > feed rate > grinding force, with abrasive grit size exhibiting the most significant impact on surface roughness. The order of influence of process parameters on material removal depth is spindle speed > abrasive grit size > feed rate > grinding force, with spindle speed being the most influential. (2) With the goal of minimizing the comprehensive score of surface roughness and material removal depth, the optimized grinding parameter combination is selected by choosing the levels with the lowest mean values across all parameter groups. The selected parameters are brasive grit size 400#, grinding force 15 N, feed rate 50 mm/s, and spindle speed 750 r/min. Using this parameter combination, the post-grinding surface roughness reaches 0.338 μm, and the material removal depth is 1.67 μm, effectively improving surface quality while meeting specification requirements. (3) The off-line programming method is used to plan the grinding trajectory. The simulation and experiment trajectories of EA4T robot grinding completely coincide, realizing automatic grinding robot of the EA4T axle shoulder position without interference, singularities and with full reachability. Conclusions The paper conducts experimental research on process optimization and trajectory planning for robotic intelligent grinding of the EA4T axle. Through orthogonal experiments combined with the entropy weight method, the influence patterns of grinding processes on quality are revealed. The optimal process parameter combination for minimizing surface roughness and material removal depth is determined. The off-line programming method enables quick and accurate planning of a robot grinding trajectory that is non-interfering, non-singular and fully reachable. The proposed method improves grinding efficiency and surface quality, meets the requirements of grinding efficiency and surface quality of EA4T axle, and can be applied in actual production and processing, effectively breaking through the predicament of low efficiency and poor consistency of EA4T axle.
- EA4T axle /
- robot grinding /
- process optimization /
- grinding trajectory /
- off-line programming

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图 1 EA4T车轴与EA4T钢平板试件

Figure 1. EA4T axle and EA4T steel plate specimen

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图 2 机器人智能磨抛系统

Figure 2. Robot intelligent grinding and polishing system

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图 3 EA4T钢磨抛质量检测方法

Figure 3. Grinding and polishing quality test method of EA4T steel

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图 4 EA4T车轴机器人磨抛轨迹规划流程

Figure 4. Robot grinding and polishing trajectory planning process of EA4T axle

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图 5 EA4T车轴机器人磨抛轨迹仿真

Figure 5. Robot grinding and polishing trajectory simulation of EA4T axle

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图 6 EA4T车轴机器人磨抛轨迹试验

Figure 6. Robot grinding and polishing trajectory experiment of EA4T axle

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表 1 EA4T钢中各元素质量分数

Table 1. Mass fraction of each element in EA4T steel

元素	质量分数 w / %	元素	质量分数 w / %
Mn	0.50～0.80	C	0.22～0.29
Cr	0.90～1.20	Si	0.15～0.40
Mo	0.15～0.30	Cu	≤0.30
Ni	≤0.30	V	≤0.06
P	≤0.020	S	≤0.015
Fe	余量

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表 2 EA4T钢磨抛正交试验次序与参数

Table 2. Grinding and polishing orthogonal experiment sequences and parameters of EA4T steel

序号	A 磨头目数 G	B 磨抛力 F / N	C 进给速度 v / (mm·s⁻¹)	D 主轴转速 n / (r·min⁻¹)
1	120#	15	20	750
2	240#	15	30	1500
3	320#	15	40	2250
4	400#	15	50	3000
5	320#	20	30	750
6	400#	20	20	1500
7	120#	20	50	2250
8	240#	20	40	3000
9	400#	25	40	750
10	320#	25	50	1500
11	240#	25	20	2250
12	120#	25	30	3000
13	320#	30	50	750
14	120#	30	40	1500
15	400#	30	30	2250
16	320#	30	20	3000

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表 3 EA4T钢磨抛正交试验结果

Table 3. Orthogonal experiment results of EA4T steel grinding and polishing

序号	表面粗糙度 R_a / μm	材料去除深度 h / μm	误差列
1	0.931	5.67	1
2	0.709	4.67	2
3	0.532	2.33	3
4	0.598	3.00	4
5	0.673	3.67	4
6	0.313	2.67	3
7	0.725	10.67	2
8	0.627	6.33	1
9	0.561	2.00	2
10	0.753	3.33	1
11	0.376	10.67	4
12	0.978	15.33	3
13	0.695	3.67	3
14	0.659	10.00	4
15	0.309	2.67	1
16	0.363	6.00	2

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表 4 表面粗糙度方差分析

Table 4. Variance analysis results of surface roughness

影响因素	平方和	均方	F	显著性
A 磨头目数 G	0.086	0.029	5.456	0.099
B 磨抛力 F	0.294	0.098	18.760	0.019
C 进给速度 v	0.110	0.037	7.013	0.072
D 主轴转速 n	0.094	0.031	5.965	0.088

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表 5 材料去除深度方差分析

Table 5. Variance analysis results of material removal depth

影响因素	平方和	均方	F	显著性
A 磨头目数 G	30.888	10.296	2.756	0.214
B 磨抛力 F	143.258	47.753	12.781	0.032
C 进给速度 v	34.764	11.582	3.100	0.189
D 主轴转速 n	6.469	2.165	0.580	0.667

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表 6 试验综合评分

Table 6. Experiment overall score

序号	表面粗糙度 R_a / μm	材料去除深度 h / μm	综合评分 S
1	0.931	5.67	2.873
2	0.709	4.67	2.333
3	0.532	2.33	1.269
4	0.598	3.00	1.583
5	0.673	3.67	1.902
6	0.313	2.67	1.279
7	0.725	10.67	4.802
8	0.627	6.33	2.965
9	0.561	2.00	1.151
10	0.753	3.33	1.810
11	0.376	10.67	4.597
12	0.978	15.33	6.862
13	0.695	3.67	1.915
14	0.659	10.00	4.489
15	0.309	2.67	1.277
16	0.363	6.00	2.674

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表 7 综合评分极差分析

Table 7. Range analysis of overall score

影响因素	k₁	k₂	k₃	k₄	极差 R
A 磨头目数 G	4.757	2.740	1.914	1.323	3.434
B 磨抛力 F	2.015	2.737	3.605	2.589	1.590
C 进给速度 v	2.856	3.094	3.330	2.528	0.802
D 主轴转速 n	1.960	2.478	2.986	3.521	1.561

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