Noncontact 3D measurement method on hole-structure precision inspection

Hao-tian Shi , Di Wu , Cheng-kai Pang , Hai-yan Huang , Xuan Zhang , Yan Xu , Xiu-liang Chen , Guang Wu

Optoelectronics Letters ›› 2021, Vol. 17 ›› Issue (4) : 231 -235.

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Optoelectronics Letters ›› 2021, Vol. 17 ›› Issue (4) : 231 -235. DOI: 10.1007/s11801-021-0084-8
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Noncontact 3D measurement method on hole-structure precision inspection

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Abstract

In order to implement 3D point cloud scanning of small hole structure, which could not be contacted or damaged, we propose a noncontact 3D measuring method. The system contains a laser triangulation displacement sensor, a Michelson interferometer system and a coordinate measuring machine, with the advantages of non-invasive scanning, fast measurement speed and high precision. Focusing on reconstructing 3D point cloud data, random sample consensus is used to separate surface data and hole data respectively from the raw dataset. Least square optimization determines the function of the cylinder, as well as hole diameter and inclined angle between the hole and the surface. In the experiment scanning a round hole, the estimated result has diameter error and angle error within 30 µm and 0.2°, respectively. Results manifest the effectiveness and feasibility of this system and express practicality in manufacturing industry.

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Hao-tian Shi, Di Wu, Cheng-kai Pang, Hai-yan Huang, Xuan Zhang, Yan Xu, Xiu-liang Chen, Guang Wu. Noncontact 3D measurement method on hole-structure precision inspection. Optoelectronics Letters, 2021, 17(4): 231-235 DOI:10.1007/s11801-021-0084-8

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References

[1]

WuQ-Y, ZhangB-C, LinH-X, ZengX-J. Chinese Optics Letter, 2016, 14: 010010

[2]

BianX-Y, CuiJ-N, LuY-S. Applied Sciences, 2019, 9: 242

[3]

UsmanZ, MonfaredR P, LohseN, JacksonMR. International Journal of Metrology and Quality Engineering, 2016, 7: 2

[4]

MarimuthuS, AntarM, DunleaveyJ. Precision Engineering, 2019, 55: 339

[5]

PeinerE, DoeringL. IEEE Sensors Journal, 2012, 13: 2

[6]

BiC, FangJ-G, LiK, GuoZ-J. Chinese Journal of Aeronautics, 2017, 30: 4

[7]

GaoX-X, ZhaoB, WangQ-W. Review of Scientific Instruments, 2019, 90: 9

[8]

SuZ-Q, HeQ, XieZ. Review of Scientific Instruments, 2016, 87: 3

[9]

KaoC-C, ShihA J. Measurement Science and Technology, 2007, 18: 11

[10]

YuL, BiQ-Z, JiY-L, FanY-F, HuangN-D, WangY-H. Precision Engineering, 2019, 58: 35

[11]

Fischler Martin A. and Robert C. Bolles, The IEEE International Conference on Computer Vision (ICCV), 4332 (2019).

[12]

DonadelloS, MottaM, GokhanA, PrevitaliB. Optics and Laser in Engineering, 2019, 112: 136

[13]

ZhangX-B, FanF-M, GheisariM, SrivastavaG. IEEE Access, 2019, 7: 64837

[14]

AntonioG, MauricioJ, CoralR. Sensors, 2019, 19: 8

[15]

SchlarpJ, CsencsicsE, SchitterG. IEEE Transactions on Instrumentation and Measurement, 2019, 69: 6

[16]

WangK-W, ZhangX, HaoQ-S, WangY, ShenY. Neurocomputing, 2019, 332: 7

[17]

MuhammadR, LiangZ-M, HeddamS, Zounemat-KermaniM, KisiO, LiB. Journal of Hydrology, 2020, 586: 124371

[18]

NoviniA. Optics, Illumination, and Image Sensing for Machine Vision, 1987, 728: 84

[19]

SangeethaD, DeepaP. Journal of Real-Time Image Processing, 2019, 16: 957

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