Ling Wang
Sichuan University
7 Papers
23 Citations
Ling Wang is an academic researcher from Sichuan University. The author has contributed to research in topics: Deep learning & Reduction (mathematics). The author has an hindex of 3, co-authored 5 publications.
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Papers
FFCNN: A Deep Neural Network for Surface Defect Detection of Magnetic Tile
TL;DR: Deep learning technique is embedded into the system for automatic defect identification and experimental results demonstrated that the developed system is effective and efficient for magnetic tile surface defect detection.
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Crack detection in magnetic tile images using nonsubsampled shearlet transform and envelope gray level gradient
TL;DR: Experimental results show that this method achieves an accuracy rate of 95.5% in detecting cracks longer than 0.9 mm with an average runtime of 0.576 s, and outperforms traditional methods in terms of accuracy and robustness.
25
Study on the spindle axial thermal error of a five-axis machining center considering the thermal bending effect
TL;DR: In this paper , a novel modeling approach to study the spindle axial thermal error of a five-axis machining center that considers the thermal bending effect was proposed, and the thermal deformation coefficients of the column were effectively identified by using both the axial and radial thermal error data.
23
Modeling, identification, and measurement of geometric errors for a rotary axis of a machine tool using a new R-test
TL;DR: This paper makes a detailed analysis and summarizes four identification algorithms for geometric errors based on the above theory and develops a new R-test device with the addition of positioning datums and a sliding table.
12
Obtaining more appropriate temperature sensor locations for thermal error modeling: reduction, classification, and selection
TL;DR: A three-step sensor selection strategy based on the detrended cross-correlation coefficient is proposed to obtain a stable and robust set of thermal key points and has higher accuracy and stronger robustness, which is effective for sensor selection of thermal error modeling.
3