# Visual Detection of Surface Defects Based on Self-Feature Comparison in Robot 3-D Printing
## 4 Conclusions and Future Work  
The defects in the 3-D printing process may affect the surface quality and accuracy and cause a certain waste of filaments, power, and time. This paper develops a visual detection system of surface defects based on self-feature comparison in robot 3-D printing, which can distinguish the defects and their own characteristics. Three main achievements have been made in this work.  
1. A visual detection system of surface defects based on self-feature comparison has been designed, where both path planning in 3-D printing and self-feature extraction are from discrete point clouds after model slicing.
2. A self-feature extraction method is introduced. The visual points of the printed part can be selected according to the current coordinates and camera direction. Then the feature points judged by the three-point feature judgment method are projected onto the theoretical pictures to achieve the self-feature pictures.
3. This paper presents an evaluation of contour similarity based on empirical formula based on the contour parameters detected by image processing technology.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**SOCP** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline a & 83.9\% & \(-163.7\%\) & \(-151.2\%\) & \(-74.0\%\) & \(-290.7\%\) & \(-283.8\%\) \\ b & \(-189.5\%\) & \(86.8\%\) & \(-164.9\%\) & \(-114.1\%\) & \(-120.5\%\) & \(-285.7\%\) \\ c & \(-175.5\%\) & \(-160.8\%\) & \(71.1\%\) & \(-28.4\%\) & \(-96.7\%\) & \(-59.2\%\) \\ \hline \end{tabular}
\end{table}
Table 3: The similarity of the contours parameters in Figure 7.  
The method we proposed in this work can detect defects that are generated in the process of FDM and distinguish self-features of the objects and real defects, reducing the model's missing rate. Note that in theory, the minimum size of defects the method can detect is 8.5 pixels by 8.5 pixels and as for the real physical size, it needs to be converted by the camera parameters and detection distance.  
In future work, the processing time of the proposed algorithm should be further shortened so that it could satisfy the need for real-time online detection. And to improve the robustness of the detection system, materials of printing objects, experimental environments, and equipment should be taken into consideration.  
Investigation, W.D. and W.S.; writing--original draft preparation, W.D.; visualization, W.D. and W.S.; project administration, H.S.; J.F. and Y.X. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Nature Science Foundation of China (No. 51975518), the Science Fund for Creative Research Groups of National Natural Science Foundation of China (No. 51821093), Key Research and Development Plan of Zhejiang Province (No. 2018C01073) and the Fundamental Research Funds for the Central Universities (No. 2019 QNA4004). The founding sponsors had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  
Thanks for partial technical support by [PERSON] in our research group.  
The authors declare no conflict of interest.