## 7 Conclusions  
In this paper, we proposed an intelligent surface roughness estimation method. This novel technique consists steps of texture skew correction, image filtering, and image feature classification. An improved Sobel operator combined with Hough transform is used to correct image skew phenomena. 2D-DTCWT is employed to preserve features more effective for pattern recognition. The resulted images are used to train a ResNet based intelligent model. Major findings of this work can be summarized below:  
1. ResNet has proven to be an effective method for the surface roughness evaluation. Compared with traditional surface roughness measuring methods, the proposed method is a non-contact one without additional surface damages on the workpiece. Because of the engagement of ResNet in feature learning, this model does not rely on prior knowledge.
2. Surface milling experiments show that the proposed texture skew correction method is a feasible way to adjust image variabilities.
3. Effectiveness of the proposed novel method is verified by the surface roughness estimation on milled components. Results indicate that this method can distinguish different surface roughness classes with high precision.
4. Analysis of filters has demonstrated the function networks can be regarded as an automatic and intelligent realization of comparison specimen based manual surface roughness estimation. Despite similarities in principle the proposed method is also not sensitive to ambient light and does not rely on staffs' experience. Therefore, it can be served as a superior alternative scheme for this task.  
Limited by the measurement mean and the concrete manufacturing condition, only arithmetic mean roughness is investigated in this research. However, authors believe that the proposed method  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy** \\ \hline The proposed method & 95.14\% \\ (Texture skew correction + 2D-DTCWT + ResNet) & 74.62\% \\
2D-DTCWT + ResNet & 69.73\% \\ CNN with over 30 layers & 69.73\% \\ Texture skew correction + ResNet & 79.88\% \\ Texture skew correction + ResNet with 18 layers & 91.14\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparisons the results.  
is also capable for more roughness parameters and worth to do further research. In the future, it is worthwhile to investigate the surface roughness recognition for machining tasks of higher machining accuracy requirements based on acquisitions of huge datasets. Besides, investigations of surface roughness estimation applications on other concrete engineering products are also worthy further explorations.  
This work was financially supported by the Natural Science Foundation of China (Grant No. 51605403), The Ministry of Industry and Information Technology (MIIT) 2016 comprehensive and standardized trial and new model application of intelligent manufacturing (Grant No. Yu Luo Industrial Manufacturing [2016]07744), The Fundamental Research Funds for the Central Universities (Grant No. 20720160078), the Natural Science Foundation of Fujian Province, China (Grant No. 2016J01261) and the Natural Science Foundation of Guangdong Province, China (Grant No. 2015A030310010).  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiment; [PERSON] and [PERSON] performed the ResNet training experiment; [PERSON] and [PERSON] designed the figures; [PERSON] performed the skew algorithm; [PERSON] wrote the paper; [PERSON], [PERSON] and [PERSON] reviewed and edited the manuscript; all authors read and approved the manuscript.  
The authors declare no conflicts of interest.