Generative Model-Driven Sampling Strategy for the High-Efficiency Measurement of Complex Surfaces on Coordinate Measuring Machines

被引:11
作者
Ren, Jieji [1 ]
Ren, Mingjun [1 ]
Sun, Lijian [2 ]
Zhu, Limin [1 ]
Jiang, Xiangqian [3 ]
机构
[1] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200201, Peoples R China
[2] Zhejiang Lab, AI Res Inst, Hangzhou 311121, Peoples R China
[3] Univ Huddersfield, Sch Comp & Engn, Future Metrol Hub, Engn & Phys Sci Res Council EPSRC, Huddersfield HD1 3DH, W Yorkshire, England
关键词
Machining error; measurement; surface metrology; GAUSSIAN-PROCESSES; CMM INSPECTION; ON-MACHINE; CURVES; RECONSTRUCTION; METROLOGY; FREEFORM;
D O I
10.1109/TIM.2021.3082322
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Coordinate measuring machines are widely used in the precision measurement of manufacturing workpieces. However, the nature of a point-by- point probing characteristic limits their efficiency in the measurement of complex parts, which normally requires dense sampling points for fully evaluating machining errors with high fidelity. To address this problem, this article proposes a generative model-driven sampling strategy to reduce the number of sampling points while preserving the measurement accuracy. Specifically, the reconstruction of surface errors with sparse sampling is transformed as a point cloud super-resolution task, which constructs a generative model to estimate accurate dense results from sparse sampled data. A multiscale neural network architecture is designed to achieve reconstruction, and the fractional Brownian motion is applied to simulate machining errors and synthesize a large-scale dataset for model training. The generalized neural model can then utilize sparse measurements to reconstruct global machining errors, which dramatically reduces the sampling time and increases measurement efficiency. Both computer simulations and actual measurements are carried out to verify the effectiveness of the proposed method.
引用
收藏
页数:11
相关论文
共 59 条
[1]   A Deep Journey into Super-resolution: A Survey [J].
Anwar, Saeed ;
Khan, Salman ;
Barnes, Nick .
ACM COMPUTING SURVEYS, 2020, 53 (03)
[2]   Adaptive inspection in coordinate metrology based on kriging models [J].
Ascione, Rocco ;
Moroni, Giovanni ;
Petro, Stefano ;
Romano, Daniele .
PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY, 2013, 37 (01) :44-60
[3]  
Aubele, 1988, U.S. Patent, Patent No. [4 769 763, 4769763]
[4]  
Biagini F, 2008, PROBAB APPL SER, P1
[5]  
CHO MW, 1995, INT J PROD RES, V33, P427, DOI 10.1080/00207549508930158
[6]  
Dierckx P., 1995, Curve and Surface Fitting with Splines
[7]  
Dinh HQ, 2001, EIGHTH IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION, VOL II, PROCEEDINGS, P606, DOI 10.1109/ICCV.2001.937682
[8]   Accelerating the Super-Resolution Convolutional Neural Network [J].
Dong, Chao ;
Loy, Chen Change ;
Tang, Xiaoou .
COMPUTER VISION - ECCV 2016, PT II, 2016, 9906 :391-407
[9]   Image Super-Resolution Using Deep Convolutional Networks [J].
Dong, Chao ;
Loy, Chen Change ;
He, Kaiming ;
Tang, Xiaoou .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2016, 38 (02) :295-307
[10]  
Dong Y., 2019, J AEROSPACE POWER, V34, P175