3D Wear Area Reconstruction of Grinding Wheel by Frequency-Domain Fusion

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• 作者：Aibin Zhu ; Dayong He ; Jianwei Zhao…
• 关键词：Tool wear ; Depth from focus ; Shape from shading ; Frequency ; domain fusion ; Surface reconstruction
• 刊名：The International Journal of Advanced Manufacturing Technology
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：88
• 期：1-4
• 页码：1111-1117
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Industrial and Production Engineering; Media Management; Mechanical Engineering; Computer-Aided Engineering (CAD, CAE) and Design;
• 出版者：Springer London
• ISSN：1433-3015
• 卷排序：88

文摘

By using depth from focus (DFF) alone to obtain the 3D wear area reconstruction of grinding wheel, the depth values in the 3D wear area image are not continuous while by using shape from shading (SFS) alone, the 3D wear area cannot be fully obtained. A novel method that uses Fourier transform to fuse the methods of DFF and SFS is proposed in this paper. The proposed method can achieve depth continuity in tool wear images. After performing high-pass filtering and low-pass filtering for the 3D reconstructed images of DFF and SFS, respectively, in frequency domain, the 3D wear area reconstruction image of grinding wheel can then be obtained by Fourier inversion. Experimental results showed that the 3D wear area reconstruction image obtained by the proposed method can achieve better accuracy for the tool wear volume than using DFF or SFS alone. And the efficiency of the proposed method is proved to be better than microscopes.