Fused deposit modeling (FDM) additive manufacturing technology is widely applied in recent years. However, there are many defects that may affect the surface quality, accuracy, or even cause the collapse of the parts. This paper presents a solution to the problem of detecting defects on the outer surface in the additive process of FDM. A multi-view and all-round vision detection method is introduced where the detection field of view is changing with the vector of the outer surface during the printing process on the six degrees of freedom robot FDM printer. After the image is preprocessed, this paper can identify the defects effectively according to its laminate structure, and introduces a mathematical matrix to represent the defects which will be classified into three typical types according to the geometry shape and area distribution. This research only focuses on the feasibility of the defects detection method. To create the object of high quality, more research is needed to account for the process parameters which significantly cause the defects. This work will help to detect the defects online, monitor the printing quality of the outer surface, reduce the waste of printed filaments, etc. This study is among the first to present a multi-view and all-round vision detection method to detect defects on the outer surface in the additive process of FDM; proposes a means of identifying defects according to its laminate structure; and introduces a mathematical matrix to represent the defects which may be used in quality assessment.

Fused Deposition Modeling (FDM) additive manufacturing technology is widely applied in recent years. However, there are many defects that may affect the surface quality, accuracy, or even cause the collapse of the parts in the printing process. In the existing defect detection technology, the characteristics of parts themselves may be misjudged as defects. This paper presents a solution to the problem of distinguishing the defects and their own characteristics in robot 3-D printing. A self-feature extraction method of shape defect detection of 3D printing products is introduced. Discrete point cloud after model slicing is used both for path planning in 3D printing and self-feature extraction at the same time. In 3-D printing, it can generate G-code and control the shooting direction of the camera. Once the current coordinates have been received, the self-feature extraction begins, whose key steps are keeping a visual point cloud of the printed part and projecting the feature points to the picture under the equal mapping condition. After image processing technology, the contours of pictured projected and picture captured will be detected. At last, the final defects can be identified after evaluation of contour similarity based on empirical formula. This work will help to detect the defects online, improve the detection accuracy, and reduce the false detection rate without being affected by its own characteristics.