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.

RGB-D sensors are gradually introduced into the robotic system to help the machine understand its surroundings. Among the point cloud processing methods, instance segmentation of point cloud is extremely important since the quality of segmentation will affect the performance of subsequent algorithms. In this paper, the 3D reconstruction process of RGB-D sensor is analyzed, and a framework PointSeg is proposed to handle the instance segmentation of point cloud captured by RGB-D sensor. The PointSeg realizes point cloud instance segmentation by applying the deep learning method YOLACT++ to instance segment the color image first and then matching the instance information with the point cloud. In addition, an experimental platform that is equipped with a Kinect v2 is built, and a dataset is set up and then the PointSeg is tested on the dataset. The result shows that the PointSeg achieves point cloud instance segmentation according to the instance information extracted from color images, which not only has good real-time performance but also has better instance segmentation accuracy compared with the method of conducting instance segmentation directly on point cloud data, and the introduction of data augmentation in the training phase can achieve good training effect even on a small training dataset.

Vision-based weld seam extraction poses a significant challenge for weldments with complex spatial structures in automated welding. Existing research primarily focuses on identifying weld seams from weldments with given positions and postures, while practical weld path planning requires multiple weld seams identified within arbitrarily placed weldments. This paper proposes a methodology that identifies weld seams from arbitrarily placed spatial planar weldments in a single run. First, by introducing a turntable calibrated with respect to a 3D camera, we perform 3D reconstruction on an arbitrarily placed spatial planar weldment. Second, an improved RANSAC algorithm based on Euclidean clustering is proposed to carry out plane segmentation, focusing on segmentation accuracy. Finally, we present a novel weld seam extraction algorithm leveraging the half-edge data structure to efficiently compute weld seams from the segmented planes. The experiments conducted in this study demonstrate that the average segmentation errors (as an indirect indicator of weld seam extraction error) are reduced by 90.3% to 99.8% over conventional segmentation methods, and the standard deviations are reduced by 64.8% to 97.0%.

Electric spindles are a critical component of numerically controlled machine tools that directly affect machining precision and efficiency. The accurate identification of the modal parameters of an electric spindle is essential for optimizing design, enhancing dynamic performance, and facilitating fault diagnosis. This study proposes a covariance-driven stochastic subspace identification (SSI-cov) method integrated with a simulated annealing (SA) strategy and fuzzy C-means (FCM) clustering algorithm to achieve the automated identification of modal parameters for electric spindles. Using both finite element simulations and experimental tests conducted at 22 °C, the first five natural frequencies of the electric spindle under free, constrained, and dynamic conditions were extracted. The experimental results demonstrated experiment errors of 0.17% to 0.33%, 1.05% to 3.27%, and 1.29% to 3.31% for the free, constrained, and dynamic states, respectively. Compared to the traditional SSI-cov method, the proposed SA-FCM method improved accuracy by 12.05% to 27.32% in the free state, 17.45% to 47.83% in the constrained state, and 25.45% to 49.12% in the dynamic state. The frequency identification errors were reduced to a range of 2.25 Hz to 20.81 Hz, significantly decreasing errors in higher-order modes and demonstrating the robustness of the algorithm. The proposed method required no manual intervention, and it could be utilized to accurately analyze the modal parameters of electric spindles under free, constrained, and dynamic conditions, providing a precise and reliable solution for the modal analysis of electric spindles in various dynamic states.