磨料水射流铣削钛合金深度与表面粗糙度研究

杜航; 熊杰; 陈炜; 李登; 巫世晶

doi:10.13433/j.cnki.1003-8728.20220045

磨料水射流铣削钛合金深度与表面粗糙度研究

doi: 10.13433/j.cnki.1003-8728.20220045

武汉大学水射流理论与新技术湖北省重点实验室, 武汉 430072

基金项目:

国家自然科学基金项目 51805188

详细信息

作者简介:
杜航(1996-), 硕士研究生, 研究方向为水射流理论与新技术, dh0802@whu.edu.cn

通讯作者:
巫世晶, 教授, 博士生导师, wsj@whu.edu.cn

中图分类号: V261.91
计量
- 文章访问数: 111
- HTML全文浏览量: 40
- PDF下载量: 25
- 被引次数: 0
出版历程
- 收稿日期: 2021-06-21
- 刊出日期: 2023-07-25

Investigation of Depth and Surface Roughness in Abrasive Water Jet Milling of Titanium Alloy

Hubei Key Laboratory of Waterjet Theory and New Technology, Wuhan University, Wuhan 430072, China

摘要

摘要: 磨料水射流铣削技术柔性大、工艺参数复杂，其加工性能难以有效控制。针对这一问题，本文首先通过响应曲面法研究了磨料水射流铣削钛合金时典型工艺参数对铣削深度和表面粗糙度的影响，并采用传统回归方式建立了经验预测模型；其次在结合磨粒磨损理论、高斯轮廓模型和表面成形分析的基础上，进一步建立了铣削深度和表面粗糙度的半经验预测模型；然后利用实验数据进行了参数标定；最后通过实验验证和对比了两种模型。结果表明，两种预测模型的平均误差均小于15%，相比经验模型，半经验模型既可以解释参数影响和铣削机理，又可以保证预测的准确率和稳定性，对于控制铣削深度和表面质量具有重要价值。
- 磨料水射流铣削 /
- 钛合金 /
- 预测模型 /
- 磨粒磨损理论 /
- 高斯轮廓
Abstract: Abrasive water jet milling technology has large flexibility and complex processing parameters, so its machining performance is difficult to effectively control. To solve this problem, firstly, the influence of the typical process parameters on the milling depth and surface roughness in abrasive water jet milling of titanium alloy is investigated with the response surface methodology (RSM). Then an empirical model is established with the traditional regression method. Secondly, a semi empirical model for milling depth and surface roughness is established in terms of the abrasive wear theory, Gaussian profile model and surface forming analysis. Thirdly, the coefficients are calibrated by using the experimental data. Eventually the two models are verified and compared with the experiments. The results show that the average error of the two prediction models is below 15%. Comparing with the empirical model, the semi empirical model can not only explain the influence of the parameters and milling mechanism, but also ensure the accuracy and stability of prediction, which is of great value for controlling the milling depth and surface quality.
- abrasive water jet milling /
- titanium alloy /
- prediction model /
- abrasive wear theory /
- gaussian profile

HTML全文

图 1 磨料水射流Zig-Zag铣削路径

Figure 1. Zig-Zag milling path with abrasive water jet

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图 2 横向进给对磨痕廓形的影响

Figure 2. The influence of transverse feeding on the profile shape of the grinding mark

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图 3 不同经验模型的残差正态概率分布

Figure 3. Normal probability distributions of the residuals for different empirical models

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图 4 不同经验模型的残差与预测响应间的关系

Figure 4. The relationship between residuals and predicted responses for different empirical models

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图 5 单个工艺参数对不同铣削特性响应的影响

Figure 5. The influence of individual process parameters on the responses of different milling characteristics

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图 6 单道磨痕的几何轮廓解析

Figure 6. Geometrical profile analysis of a single grinding mark

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图 7 不同方向的一维轮廓粗糙度形成原理

Figure 7. Formation principle of one-dimensional profile roughness in different directions

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图 8 铣削性能实验值与预测模型值的对比

Figure 8. Comparison of experimental values and predicted model values for milling performance

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表 1 工件和磨料的材料性能

Table 1. Material properties of the workpiece and abrasive

材料	σ_f/MPa	σ_b/MPa	ρ/(g·cm^-3)	莫氏硬度	粒度/目
Ti₆Al₄V	≥860	≥895	4.51	-	-
石榴石	-	-	3.4~4.3	7.5	80

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表 2 响应曲面实验设计中的因素及水平

Table 2. Factors and levels in the response surface experimental design

工艺因素	水平1	水平2	水平3
工艺因素	-1	0	1
A: 射流压力P/MPa	220	260	300
B: 磨料流量m_a/(g·min^-1)	200	480	760
C: 靶距D/mm	6	14	22
D: 射流角度α/(°)	30	60	90
E: 进给速度u/(mm·s^-1)	30	50	70
F: 横向进给量S/mm	0.4	0.6	0.8

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表 3 验证实验中的因素及水平

Table 3. Factors and levels in validation experiments

工艺因素	水平	水平	水平	水平	水平	水平
射流压力P	-1, 0, 1	0	0	0	0	0
磨料流量	0	-1, 0, 1	0	0	0	0
靶距D	0	3	-1, 0, 1	0	0	0
射流角度α	1	1	1	-1, 0, 1	1	1
进给速度u	0	0	0	0	-1, 0, 1	0
横向进给量S	-1	-1	-1	-1	-1	-1, 0, 1

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表 4 模型误差统计

Table 4. Model error statistics %

铣削特性	模型类型	平均误差	最大误差	误差标准差
铣削深度	经验模型	9.57	34.92	7.71
铣削深度	半经验模型	9.38	24.57	6.62
X向粗糙度	经验模型	10.87	31.16	8.75
X向粗糙度	半经验模型	13.81	28.41	7.75
Y向粗糙度	经验模型	14.58	36.81	9.12
Y向粗糙度	半经验模型	13.54	30.53	10.09

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