航空发动机涡轮叶片DR检测工艺参数优化

俞梦倩; 吴伟; 邬冠华; 夏志风; 傅伟成

doi:10.13224/j.cnki.jasp.20210731

航空发动机涡轮叶片DR检测工艺参数优化

doi: 10.13224/j.cnki.jasp.20210731

俞梦倩^1,,
吴伟^1,*, ,,
邬冠华¹,
夏志风²,
傅伟成¹

1.
南昌航空大学无损检测技术教育部重点实验室，南昌 330063
2.
中国航发沈阳黎明航空发动机有限责任公司，沈阳 110043

基金项目: 国家自然科学基金（62161030）

详细信息

作者简介:
俞梦倩（1997-），女，硕士，研究方向为智能图像处理。E-mail：10362109935@qq.com

通讯作者:
吴伟（1970-），男，教授、硕士生导师，硕士，研究方向为智能测试技术与射线检测、图像检测与智能识别等。E-mail：cwuwei@163.com

中图分类号: V232.4
计量
- 文章访问数: 158
- HTML浏览量: 38
- PDF量: 64
- 被引次数: 0
出版历程
- 收稿日期: 2021-12-27
- 网络出版日期: 2022-12-22

Optimization of DR detection process parameters for aero-engine turbine blades

1.
Key Laboratory of Non-destructive Testing Technology，Ministry of Education，Nanchang Hangkong University，Nanchang 330063，China
2.
Shenyang Liming Aeroengine Corporation Limited，Aero Engine Corporation of China，Shenyang 110043，China

摘要

摘要:
针对工业领域数字射线（DR）快速选定检测工艺参数获取高信噪比图像的需求，研究DR检测中多因素工艺参数不同组合对成像结果的影响。以航空发动机涡轮叶片同材料等效厚度试块为对象，采用二次回归正交旋转实验方法，建立检测图像信噪比与管电压、管电流、积分时间、不同等效厚度之间的二次回归方程模型，并检验单因素及各因素间交互作用对检测图像信噪比的显著性。利用人工刻槽航空发动机涡轮叶片结合回归方程模型，以检测图像信噪比为优化指标，在已知透照厚度情况下得到最佳工艺参数组合，比较检测图像信噪比的实际值与计算值。结果表明：在4组验证实验下实际信噪比值与计算值比较接近，误差范围在1.4%～5.5%，表明模型具有较高的可靠性。
- 数字射线检测 /
- 信噪比 /
- 航空发动机涡轮叶片 /
- 二次回归正交旋转组合设计 /
- 工艺参数
Abstract:
In view of the needs of digital radiography (DR) in the industrial field to quickly select the detection process parameters to obtain high signal-to-noise ratio images, the influences of different combinations of multi-factor process parameters on the imaging results in DR detection were studied. Taking the aero-engine turbine blade as the object, the quadratic regression orthogonal rotation test method was used to establish the quadratic regression equation model between the detection image signal-to-noise ratio and the tube voltage, tube current, integration time, and different equivalent thicknesses, and the single test was carried out. The significance of the factors and the interaction among the factors on the detection image signal-to-noise ratio were also discussed. Using the manual slotted aero-engine turbine blade combined with the regression equation model, the detection image signal-to-noise ratio was used as the optimization index, the optimal combination of process parameters was obtained under the condition of known translucency thickness, and the actual value and calculated value of the signal-to-noise ratio of the detected image were compared. The results showed that the actual signal-to-noise ratio was close to the calculated value under four sets of verification tests, and the error range was 1.4%—5.5%, indicating the high reliability of the model.
- digital ray detection /
- signal-to-noise ratio /
- aero-engine turbine blades /
- quadratic regression orthogonal rotation combination design /
- process parameters

HTML

图 1 DR透照布置实物图

Figure 1. Physical map of DR transillumination layout

下载: 全尺寸图片幻灯片

图 2 实验试块设计及实物图

Figure 2. Test block design and physical diagram

下载: 全尺寸图片幻灯片

图 3 各阶梯DR检测图像

Figure 3. DR detection images of each step

下载: 全尺寸图片幻灯片

图 4 航空发动机涡轮叶片刻槽示意图

Figure 4. Schematic diagram of notching grooves in aero-engine turbine blades

下载: 全尺寸图片幻灯片

图 5 回归模型实际工件验证

Figure 5. Regression model actual workpiece verification

下载: 全尺寸图片幻灯片

图 6 航空发动机涡轮叶片DR图像

Figure 6. DR image of aero-engine turbine blades

下载: 全尺寸图片幻灯片

表 1 DR系统技术指标

Table 1. Technical indicators of DR system

技术指标	参数及说明
像素尺寸	3524×4288
像素点间距/μm	100
AD位数/bit	16
探测器质量/kg	3.2
闪烁体材料	主要为Csl（Tl）
探测器材料	非晶硅
探测器工作温度/℃	10～35
探测器工作湿度/%	10～90
射线源焦点大小/μm	5
最大管电压/kV	225
最大管电流/μA	3000

下载: 导出CSV

表 2 工件各阶梯制作规格

Table 2. Production specifications of each step of the workpiece

参数	阶梯规格
参数	阶梯1	阶梯2	阶梯3	阶梯4	阶梯5
等效厚度/mm	2	5	8	11	14
丝径/mm	0.08	0.125	0.2	0.25	0.25

下载: 导出CSV

表 3 各阶梯预实验选择参数

Table 3. Selection parameters at each step of pre test

各阶梯预实验	管电压/kV	管电流/μA	积分时间/ms	阶梯厚度/mm	灰度均值G
第1块	120	500	1000	2	31125
第2块	130	800	200	5	30076
第3块	140	1400	2000	8	29854
第4块	150	1700	2500	11	33689
第5块	160	1700	3000	14	30689

下载: 导出CSV

表 4 DR参数水平编码表

Table 4. DR parameter level coding table

自然变量${{\textit{z}}_j}$	上星号臂水平$\gamma （2）$	上水平1	零水平0	下水平-1	下星号臂水平$－\gamma （ -2）$	变化间距${\varDelta _j}$
${x_1}$/kV	160	150	140	130	120	10
${x_2}$/μA	1700	1400	1100	800	500	300
${x_3}$/ms	3000	2500	2000	1500	1000	500
${x_4}$/mm	14	11	8	5	2	3

下载: 导出CSV

表 5 二次回归正交旋转实验设计表

Table 5. Design table of quadratic regression orthogonal rotation test

实验号	$v$	$i$	$t$	$h$	$vi$	$vt$	$vh$	$it$	$ih$	$th$	${v^2}$	${i^2}$	${t^2}$	${h^2}$	$R$
实验号	${{\textit{z}}_1}$	${{\textit{z}}_2}$	${{\textit{z}}_3}$	${{\textit{z}}_4}$	${{\textit{z}}_1}{{\textit{z}}_2}$	${{\textit{z}}_1}{{\textit{z}}_3}$	${{\textit{z}}_1}{{\textit{z}}_4}$	${{\textit{z}}_2}{{\textit{z}}_3}$	${{\textit{z}}_2}{{\textit{z}}_4}$	${{\textit{z}}_3}{{\textit{z}}_4}$	${\textit{z}}'_1$	${\textit{z}}'_2$	${\textit{z}}'_3$	${\textit{z}}'_4$	y
1	1	1	1	1	1	1	1	1	1	1	0.333	0.333	0.333	0.333	32.10
2	1	1	1	−1	1	1	−1	1	−1	−1	0.333	0.333	0.333	0.333	35.30
3	1	1	−1	1	1	−1	1	−1	1	−1	0.333	0.333	0.333	0.333	27.40
4	1	1	1	−1	1	−1	−1	−1	−1	1	0.333	0.333	0.333	0.333	35.30
5	1	−1	1	1	−1	1	1	−1	−1	1	0.333	0.333	0.333	0.333	27.29
6	1	−1	1	−1	−1	1	−1	−1	1	−1	0.333	0.333	0.333	0.333	30.20
7	1	−1	−1	1	−1	−1	1	1	−1	−1	0.333	0.333	0.333	0.333	25.71
8	1	−1	−1	−1	−1	−1	−1	1	1	1	0.333	0.333	0.333	0.333	28.60
9	−1	1	1	1	−1	−1	−1	1	1	1	0.333	0.333	0.333	0.333	27.70
10	−1	1	1	−1	−1	−1	1	1	−1	−1	0.333	0.333	0.333	0.333	30.57
11	−1	1	−1	1	−1	1	−1	−1	1	−1	0.333	0.333	0.333	0.333	23.28
12	−1	1	−1	−1	−1	1	1	−1	−1	1	0.333	0.333	0.333	0.333	26.14
13	−1	−1	1	1	1	−1	−1	−1	−1	1	0.333	0.333	0.333	0.333	24.54
14	−1	−1	1	−1	1	−1	1	−1	1	−1	0.333	0.333	0.333	0.333	27.50
15	−1	−1	−1	1	1	1	−1	1	−1	−1	0.333	0.333	0.333	0.333	23.60
16	−1	−1	−1	−1	1	1	1	1	1	1	0.333	0.333	0.333	0.333	26.04
17	2	0	0	0	0	0	0	0	0	0	3.333	−0.667	−0.667	−0.667	32.02
18	−2	0	0	0	0	0	0	0	0	0	3.333	−0.667	−0.667	−0.667	24.86
19	0	2	0	0	0	0	0	0	0	0	−0.667	3.333	−0.667	−0.667	30.60
20	0	−2	0	0	0	0	0	0	0	0	−0.667	3.333	−0.667	−0.667	25.40
21	0	0	2	0	0	0	0	0	0	0	−0.667	−0.667	3.333	−0.667	29.30
22	0	0	−2	0	0	0	0	0	0	0	−0.667	−0.667	3.333	−0.667	23.40
23	0	0	0	2	0	0	0	0	0	0	−0.667	−0.667	−0.667	3.333	25.40
24	0	0	0	−2	0	0	0	0	0	0	−0.667	−0.667	−0.667	3.333	31.70
25	0	0	0	0	0	0	0	0	0	0	−0.667	−0.667	−0.667	−0.667	33.82
26	0	0	0	0	0	0	0	0	0	0	−0.667	−0.667	−0.667	−0.667	33.78
27	0	0	0	0	0	0	0	0	0	0	−0.667	−0.667	−0.667	−0.667	33.74
28	0	0	0	0	0	0	0	0	0	0	−0.667	−0.667	−0.667	−0.667	33.65
29	0	0	0	0	0	0	0	0	0	0	−0.667	−0.667	−0.667	−0.667	33.69
30	0	0	0	0	0	0	0	0	0	0	−0.667	−0.667	−0.667	−0.667	33.60
31	0	0	0	0	0	0	0	0	0	0	−0.667	−0.667	−0.667	−0.667	33.50
32	0	0	0	0	0	0	0	0	0	0	−0.667	−0.667	−0.667	−0.667	33.57
33	0	0	0	0	0	0	0	0	0	0	−0.667	−0.667	−0.667	−0.667	33.59
34	0	0	0	0	0	0	0	0	0	0	−0.667	−0.667	−0.667	−0.667	33.57
35	0	0	0	0	0	0	0	0	0	0	−0.667	−0.667	−0.667	−0.667	33.75
36	0	0	0	0	0	0	0	0	0	0	−0.667	−0.667	−0.667	−0.667	33.59

下载: 导出CSV

表 6 以信噪比为评价指标的回归模型方差分析

Table 6. Variance analysis of regression model with signal-to-noise ratio as the evaluation index

变异来源	平方和	自由度	均方	F	P
${{\textit{z}}_1}$	73.780	1	73.780	4904.929	7.15×10⁻¹⁸^**
${{\textit{z}}_2}$	37.350	1	37.350	2483.046	5.87×10⁻¹⁶^**
${{\textit{z}}_3}$	53.103	1	53.103	3530.350	6.02×10⁻¹⁷^**
${{\textit{z}}_4}$	53.580	1	53.580	3562.065	5.68e×10⁻¹⁷^**
${{\textit{z}}_1}{{\textit{z}}_2}$	3.534	1	3.534	234.967	1.92×10⁻⁹^**
${{\textit{z}}_1}{{\textit{z}}_3}$	0.122	1	0.122	8.143	0.018^*
${{\textit{z}}_1}{{\textit{z}}_4}$	0.062	1	0.062	4.155	0.074
${{\textit{z}}_2}{{\textit{z}}_3}$	10.144	1	10.144	674.390	2.56×10⁻¹²^**
${{\textit{z}}_2}{{\textit{z}}_4}$	0.046	1	0.046	3.073	0.118
${{\textit{z}}_3}{{\textit{z}}_4}$	0.0245	1	0.024	1.597	0.250
${\textit{z}}'_1$	42.773	1	42.773	3549.306	2.44×10⁻¹⁶^**
${\textit{z}}'_2$	50.112	1	50.112	4179.574	8.76×10⁻¹⁷^**
${\textit{z}}'_3$	82.807	1	82.807	7001.594	3.38×10⁻¹⁸^**
${\textit{z}}'_4$	41.029	1	41.029	3399.783	3.19×10⁻¹⁶^**
模型	504.47	14	36.03	2395.50	1.44×10⁻¹⁸^**
剩余	0.3159	21	0.0150
失拟	0.2056	10	0.0206	2.05	0.1273
误差	0.1103	11	0.0100
总和	504.78	35
注：^表示$P < 0.05$，影响显著；^*表示$P < 0.01$，影响极显著。

下载: 导出CSV

表 7 航空发动机涡轮叶片刻槽设计尺寸

Table 7. Designed dimensions of grooves in aero-engine turbine blades

参数	编号
参数	1	2	3	4	5	6	7	8
公称尺寸/mm	10×0.2×1	10×0.2×1	5×0.2×1	10×0.2×1	4×0.2×1	4×0.2×1	4×0.2×1	4×0.2×1
等效厚度/mm	10.5	13	13	10.5	3.5	3.5	2.5	2.5

下载: 导出CSV

表 8 验证实验参数计算结果

Table 8. Verification of test parameter calculation results

实验参数水平	1、4号线槽	2、3号线槽	5、6号线槽	7、8号线槽
厚度换算水平	0.833	1.667	−1.5	−1.833
管电压水平约束范围	0～1	1～2	−2～−1	−2～−1.5
管电流水平约束范围	0～1	1～2	−2～−1	−2～−1.5
积分时间水平约束范围	0～1	1～2	−2～−1	−2～−1.5
最优管电压水平，实际值/kV	0.783, 148	1, 150	−1, 130	−1.5, 125
最优管电流水平，实际值/μA	0.702, 1311	1, 1400	−1, 800	−1.5, 650
最优积分时间水平，实际值/ms	0.563, 2282	1, 2500	−1, 1500	−1.5, 1250

下载: 导出CSV

参考文献(18)

[1]	CHEN L,LI B,ZHOU H,et al. Detection of three-dimensional parameter of defects for gas turbine blades based on two-dimensional digital radiographic projective imaging[J]. Journal of Nondestructive Evaluation,2019,38(4): 101-110. doi: 10.1007/s10921-019-0640-3
[2]	KUSK M W,JENSEN J M,GRAM E H,et al. Anode heel effect: does it impact image quality in digital radiography? a systematic literature review[J]. Radiography,2021,27(3): 976-981.
[3]	王琦,高党忠,马小军,等. 惯性约束聚变靶丸高精度X射线数字成像[J]. 光学精密工程,2020,28(2): 69-78. WANG Qi,GAO Dangzhong,MA Xiaojun,et al. High precision X-ray digital imaging of inertial confinement fusion target[J]. Optics and Precision Engineering,2020,28(2): 69-78. (in Chinese)
[4]	张军辉, 赖传理, 陈小明. X射线数字成像检测(DR)系统曝光曲线制作讨论[R]. 西安: 2017远东无损检测新技术论坛, 2017.
[5]	郭文明,陈宇亮. X射线图像灰度值与透照厚度的定量关系[J]. 无损检测,2016,38(2): 14-17. doi: 10.11973/wsjc201602004 GUO Wenming,CHENG Yuliang. Quantitative relationship between X-ray image gray value and transillumination thickness[J]. Nondestructive Testing,2016,38(2): 14-17. (in Chinese) doi: 10.11973/wsjc201602004
[6]	胡景东, 梁丽红, 刘雪梅, 等. 射线数字成像透照厚度与灰度模型研究[J]. 光学学报, 2021, 41(10): 222-227. HU Jingdong, LIANG Lihong, LIU Xuemei, et al. Study on transmission thickness and gray model of X-ray digital imaging[J]. Acta Optica Sinica, 2021, 41(10): 222-227. (in Chinese)
[7]	胡文刚, 陆云鹏, 郭世雄, 等. 基于DR数字射线成像技术的铝合金焊缝缺陷检测[J]. 焊接, 2021(2): 46-51, 64. HU Wengang, LU Yunpeng, GUO Shixiong, et al. Defect detection of aluminum alloy weld based on DR digital radiographic technology[J]. Weldingand Joining,2021(2): 46-51, 64. (in Chinese)
[8]	张佳祥. 钢制管道缺陷X射线数字成像检测技术研究[D]. 杭州: 浙江工业大学, 2017. ZHANG Jiaxiang, Research on X-ray digital imaging detection technology of steel pipeline defects[D]. Hangzhou: Zhejiang University of Technology, 2017. (in Chinese)
[9]	倪培君,王俊涛,闫敏,等. 数字射线检测技术理论研究进展[J]. 机械工程学报,2017,53(12): 13-18. doi: 10.3901/JME.2017.12.013 NI Peijun,WANG Juntao,YAN Min,et al. Theoretical research progress of digital radiographic testing technology[J]. Journal of Mechanical Engineering,2017,53(12): 13-18. (in Chinese) doi: 10.3901/JME.2017.12.013
[10]	郭增,李彦红. 基于回归正交试验的赤泥基固化剂改良粉质粘土水稳定性研究[J]. 数学的实践与认识,2019,49(5): 315-320. GUO Zeng,LI Yanhong. Study on the stability of silty clay water stabilized by red mud base curing agen based on regression orthogonal test[J]. Mathematics in Practice and Theory,2019,49(5): 315-320. (in Chinese)
[11]	魏慧荣,李晓东. 搅拌机颗粒导热系数的测定[J]. 兵器装备工程学报,2019,40(2): 179-183. doi: 10.11809/bqzbgcxb2019.02.036 WEI Huirong,LI Xiaodong. Determination of thermal conductivity of mixer particles[J]. Journal of Ordnance Equipment Engineering,2019,40(2): 179-183. (in Chinese) doi: 10.11809/bqzbgcxb2019.02.036
[12]	孙明社,郭小红,王梦恕,等. 基于二次正交试验的隧道非概率可靠度方法研究[J]. 土木工程学报,2017,50(增刊2): 105-111. SUN Mingshe,GUO Xiaohong,WANG Mengshu,et al. Study on non probabilistic reliability method of tunnel based on quadratic orthogonal test[J]. China Civil Engineering Journal,2017,50(Suppl.2): 105-111. (in Chinese)
[13]	葛宜元. 试验设计方法与Design-Expert软件应用[M]. 哈尔滨: 哈尔滨工业大学出版社, 2015.
[14]	郭芳,原霞,吉梦雯,等. 基于二次回归正交试验的连杆衬套成形质量分析[J]. 塑性工程学报,2018,25(5): 159-163. GUO Fang,YUAN Xia,JI Mengwen,et al. Forming quality analysis of connecting rod bushing based on quadratic regression orth-ogonal test[J]. Journal of Plasticity Engineering,2018,25(5): 159-163. (in Chinese)
[15]	刘艳,尤齐燊,朱红梅,等. 电极感应气雾化法制备新型高硬度马氏体铁基合金粉末[J]. 粉末冶金技术,2021,39(6): 537-544. LIU Yan,YOU Qishen,ZHU Hongmei,et al. Preparation of new high hardness martensite iron-based alloy powder by electrode induction gas atomization[J]. Powder Metallurgy Technology,2021,39(6): 537-544. (in Chinese)
[16]	马同玲,张扬军,王正,等. 基于正交试验的柔性联轴器设计灵敏度分析与优化研究[J]. 推进技术,2022,43(2): 237-245. MA Tonglin,ZHANG Yangjun,WANG Zheng,et al. Sensitivity analysis and optimization of flexible coupling design based on orthogonal test[J]. Journal of Propulsion Technology,2022,43(2): 237-245. (in Chinese)
[17]	MA X,RAO Q H. Property optimization of inorganic silicon aluminum polymer based on quadratic regression and orthogonal design[J]. Key Engineering Materials,2015,629/630: 510-517.
[18]	刘凯,王晓勇,袁孟春. 碳纤维缠绕发动机壳体数字射线DR成像检测图像质量控制[J]. 宇航材料工艺,2020,50(5): 76-82. LIU Kai,WANG Xiaoyong,YUAN Mengchun. Image quality control of digital ray DR imaging detection of carbon fiber wound engine shell[J]. Aerospace Materials and Technology,2020,50(5): 76-82. (in Chinese)