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Appl. Sci. 2018, 8(3), 381; https://doi.org/10.3390/app8030381

Noncontact Surface Roughness Estimation Using 2D Complex Wavelet Enhanced ResNet for Intelligent Evaluation of Milled Metal Surface Quality

1
School of Aerospace Engineering, Xiamen University, Xiamen 361005, China
2
Shenzhen Research Institute of Xiamen University, Shenzhen 518000, China
3
Cross-strait Tsinghua Research Institute, Beijing 100084, China
4
School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China
*
Author to whom correspondence should be addressed.
Received: 2 February 2018 / Revised: 25 February 2018 / Accepted: 1 March 2018 / Published: 6 March 2018
(This article belongs to the Section Mechanical Engineering)
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Abstract

Machined surfaces are rough from a microscopic perspective no matter how finely they are finished. Surface roughness is an important factor to consider during production quality control. Using modern techniques, surface roughness measurements are beneficial for improving machining quality. With optical imaging of machined surfaces as input, a convolutional neural network (CNN) can be utilized as an effective way to characterize hierarchical features without prior knowledge. In this paper, a novel method based on CNN is proposed for making intelligent surface roughness identifications. The technical scheme incorporates there elements: texture skew correction, image filtering, and intelligent neural network learning. Firstly, a texture skew correction algorithm, based on an improved Sobel operator and Hough transform, is applied such that surface texture directions can be adjusted. Secondly, two-dimensional (2D) dual tree complex wavelet transform (DTCWT) is employed to retrieve surface topology information, which is more effective for feature classifications. In addition, residual network (ResNet) is utilized to ensure automatic recognition of the filtered texture features. The proposed method has verified its feasibility as well as its effectiveness in actual surface roughness estimation experiments using the material of spheroidal graphite cast iron 500-7 in an agricultural machinery manufacturing company. Testing results demonstrate the proposed method has achieved high-precision surface roughness estimation. View Full-Text
Keywords: surface roughness estimation; texture skew correction; dual tree complex wavelet transform (DTCWT); residual network (ResNet); Hough transform (HT) surface roughness estimation; texture skew correction; dual tree complex wavelet transform (DTCWT); residual network (ResNet); Hough transform (HT)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Sun, W.; Yao, B.; Chen, B.; He, Y.; Cao, X.; Zhou, T.; Liu, H. Noncontact Surface Roughness Estimation Using 2D Complex Wavelet Enhanced ResNet for Intelligent Evaluation of Milled Metal Surface Quality. Appl. Sci. 2018, 8, 381.

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