Quadrics are the most common types of surfaces used in weldments. Extracting multiple welds formed by quadric surfaces from a single 3D point cloud is an essential and challenging step in robotic welding for complex weldments. Relevant studies mostly focus on weld extraction from weldments with a single type of quadric. A weld extraction method for weldments with general quadratic surfaces is required. (1) This paper proposes a quadric fitting method for all kinds of quadrics, efficiently solving the quadric models with linear equations. It can be observed from the test results that the fitting error of the method proposed in this paper grows at a rate of about 1/5 of that of the SVD method in the literature as the point cloud noise grows; and the method proposed in this paper improves the operating efficiency by about 40 %. (2) For noisy point clouds with multiple intersecting quadrics, a quadric segmentation method based on region growing is proposed. The proposed segmentation method reduces 30 % similar to 50 % of segmentation errors during the tests compared to the ICP registration approach in the literature. (3) A region growing method based on ETVPS (End Tangent Vector Projection Sorting) for weld extraction with the unorganized raw intersection points from the segmented quadrics is proposed. All the mentioned methods are verified with solid experiments with physical weldments. The proposed weld extraction method proves to be robust to noisy and defective point clouds.

The reliance on manual teaching in high-mix low-volume (HMLV) transmission tower base joints manufacturing leads to inefficiencies, inconsistent weld quality, and high labor costs. This study presents a robotic flexible welding path planning approach to automatically generate welding paths for customized fabrication. The proposed method begins with arbitrary positioning of the workpiece on the turntable, followed by point cloud acquisition and weld seam extraction. Based on the extracted seam information, a turntable rotation strategy is implemented to determine the optimal welding position for each seam relative to the robot. A path planning algorithm incorporating Cartesian space constraints during sampling is then used to generate feasible welding paths. Finally, the resulting welding paths are transmitted to the robot for execution. To validate this approach, a path planning work platform integrated with a modular communication mechanism was established. The experimental results show that, compared to other algorithms, the method reduces average search time by up to 26.8% and average path length by up to 18.7%.

Turntables play an essential role in various industrial areas due to their ability to improve the degree of freedom of workpieces or tools. In applications where positional accuracy of the objects is required, the accuracy of the system parameters of the turntable is critical for the applications to work properly. This paper proposes a new approach to calibrating the turntable system. First, we propose an optimized RANSAC (random sample consensus) algorithm to obtain high-accuracy reference planes from multiple frames of point clouds grabbed from the reference object placed on a turntable. Second, an singular value decomposition-based two-step method is proposed to calculate the rotation axis vector and the center position of the turntable based on the reference planes extracted by the modified RANSAC algorithm. Finally, several experiments are performed to verify the proposed methods. Compared with the existing methods, our approach gets the analytical results and tries to correct the rotation angles. The experimental results show that the proposed method considerably improves calibration accuracy and efficiency without complex devices.

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.

The influence of process parameters, including placement speed, laser power, tooling temperature, compaction force and tape tension, on the interlaminar shear strength of CF/PEEK components in-situ consolidated by laser- assisted automated fiber placement was systematically investigated. To examine both the individual and interactive effects of these parameters, two sets of orthogonal experiments were formulated and conducted, yielding a maximum ILSS of 70.3 MPa. Analysis of variance revealed that the interaction between laser power and placement speed had the most significant effect, followed by tooling temperature, compaction force and tape tension. Furthermore, the concept of linear energy density of consolidated segments (LEDCS) was introduced to characterize and quantify the relationship between laser power and placement speed. ILSS values exceeding 50 MPa were predicted within the LEDCS range of 1.58 J/mm to 3.75 J/mm. Finally, the failure modes of the samples were elucidated through scanning electron microscopy.

Gallium-based liquid metals (LMs), with the combination of liquid fluidity and metallic conductivity, are considered ideal conductive components for flexible electronics. However, huge surface tension and poor wettability seriously hinder the patterning of LMs and their wider applications. Herein, a recyclable liquid-metal-microgel (LMM) ink composed of LM droplets encapsulated into alginate microgel shells is proposed. During the mechanical stirring process, the released Ga3+ can cross-link with sodium alginate to form microgels covering the surface of LM droplets, which exhibits shear-thinning performance due to the formation and rupture of hydrogen bonds under different stress conditions, making the LMM ink possess excellent printability and superior adhesion to various substrates. Although patterns printed with the LMM ink are not initially conductive, they can be activated to recover conductivity by microstrain (<5%), pressing, and freezing. Additionally, the activated LMM circuit exhibits superior Joule heating behaviors and electrical performance in further investigation, including excellent conductivity, significant resistance response to strain with small hysteresis, great durability to nonplanar forces, and so forth. Furthermore, smart electronic clothes were fabricated and investigated by directly printing functional circuits on commercial clothes with the LMM ink, which integrate multiple functions, including tactile sensing, motion monitoring, human-computer interaction, and thermal management.

为了解决柔性夹具夹取异形零件过程中工件与夹具相对位置具有不确定性的难题,提出基于机器视觉的机器人装配位姿在线校正算法.通过图像预处理及零件表面特征提取,建立工件位姿向量.通过系统建模、误差分析及函数拟合,将工件位姿校正量分解为原始位姿差、旋转引入位姿差及残余位姿差三部分,将三部分位姿差进行线性组合作为零件位姿误差补偿量反馈给机器人,以引导机器人完成装配.为了验证算法的有效性,以涡旋式汽车空调压缩机动盘装配为例,设计机器人手眼装配系统进行实验.实验结果表明,该系统能够保证校正后的零件位姿与目标位姿角度偏差和x及y方向位置偏差分别小于0.6°和0.6 mm,平均装配时间小于20 s,实验过程中装配成功率达到99.67％.

Purpose In this work, the authors aim to provide a set of convenient methods for generating training data, and then develop a deep learning method based on point clouds to estimate the pose of target for robot grasping. Design/methodology/approach This work presents a deep learning method PointSimGrasp on point clouds for robot grasping. In PointSimGrasp, a point cloud emulator is introduced to generate training data and a pose estimation algorithm, which, based on deep learning, is designed. After trained with the emulation data set, the pose estimation algorithm could estimate the pose of target. Findings In experiment part, an experimental platform is built, which contains a six-axis industrial robot, a binocular structured-light sensor and a base platform with adjustable inclination. A data set that contains three subsets is set up on the experimental platform. After trained with the emulation data set, the PointSimGrasp is tested on the experimental data set, and an average translation error of about 2–3 mm and an average rotation error of about 2–5 degrees are obtained. Originality/value The contributions are as follows: first, a deep learning method on point clouds is proposed to estimate 6D pose of target; second, a convenient training method for pose estimation algorithm is presented and a point cloud emulator is introduced to generate training data; finally, an experimental platform is built, and the PointSimGrasp is tested on the platform.

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.

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.

With the development of 3D measurement technology, 3D vision sensors and object pose estimation methods have been developed for robotic loading and unloading. In this work, an end-to-end deep learning method on point clouds, PointNetRGPE, is proposed to estimating the grasping pose of SCARA robot. In PointNetRGPE model, the point cloud and class number are fused into a point-class vector, and several PointNet-like networks are used to estimate the robot grasping pose, containing 3D translation and 1D rotation. Considering that rotational symmetry is very common in man-made and industrial environments, a novel architecture is introduced into PointNetRGPE to solve the pose estimation problem with rotational symmetry in the z-axis direction. Additionally, an experimental platform is built containing an industrial robot and a binocular stereo vision system, and a dataset with three subsets is set up. Finally, the PointNetRGPE is tested on the dataset, and the success rates of three subsets are 98.89%, 98.89%, and 94.44% respectively.

To achieve active compliance in the robot deburring process, a compliance control method of deburring robots based on force impedance is proposed. An impedance model between the reference deburring trajectory and contact force is constructed, based on the force feedback. The force impedance is integrated into the position control loop of robots in the Cartesian coordinates. On-line adjustment of the robot displacement and velocity is realized using normal force and tangential force, respectively. The control system simulation is carried out in the Simulink environment using the S-function. Results show that this method guarantees the displacement tracking performance of the robot, and achieves the displacement compliance through the normal force, as well as the velocity compliance through the tangential force.

The vibration table in a combination environmental testing device suffers from temperature changes, which cause the dynamic characteristics of the vibration structure to vary. The mechanism of the thermal effect on the dynamic characteristics of an elastic structure is presented, and a modal analysis with thermal effects based on the finite-element method (FEM) is carried out. The results show that the natural frequencies for each order decrease as the temperature increases, while the mode shapes of the vibrator do not change with temperature. Although thermal stress may affect natural frequencies due to the additional initial stress element stiffness, this stress can be neglected in the modal analysis because it is negligible relative to the effect of the material property changes with temperature.

In order to reduce the movement resistance,tire wear and the anti-force acting on the lateral steering oil cylinder in the two-wheel steering process of the four-way forklift truck asymmetric steering mechanism,the two-wheel steering kinematics mathematical model was established.Firsdy,the optimization model of the two-wheel steering of asymmetric steering mechanism was established to approach the ideal Ackermann two-wheel steering.Then,the size and positional parameters of asymmetric steering mechanism,as well as the parameter K which is the left and right side effective area ratio of the lateral steering oil cylinder,were optimized by using Artificial Bee Colony(ABC) algorithm.The simulation result shows its feasibility,with 43.7％ improvement of the overall performance of the two-wheel steering of asymmetric steering mechanism,it has value to engineering application.

Aiming at the parts quality measurement process in the digital closed-loop manufacturing,a wireless terminal for on-machine measurement system of parts quality was designed.Firstly,framework of the on-machine measurement system was given,and functional requirements of the wireless terminal were presented afterwards.Then,hardware module selection and design,software module and worlflow of the wireless terminal were introduced,and the PC configuration software of WiFi network was designed.With a core embedded STM32 chip,the terminal was composed of RS485 communication module,WiFi and GRPS wireless network communication modules,etc.The terminal software was composed of the quality data collection,data format processing,wireless communications networks,field equipment control and GPIO interface module,etc.The results indicate that,as a critical component in the connection of field equipment and cloud server,this terminal can be used for the parts quality data acquisition,remote equipment monitoring,and parts quality control based on the cloud computing technology.

针对四向叉车电控式转向系统转向不稳定及过载保护能力差的缺陷,提出适用于四向叉车的液控式非对称转向机构.该机构既能满足四向叉车纵向-横向行驶之间的90°切换,又能有效规避切换过程中的“死点”现象.为了减小转向过程中四向叉车的行驶阻力、轮胎磨损以及横向转向油缸所受的侧偏力,建立非对称转向机构的转向运动数学模型;以接近Ackermann理想转向为优化目标,建立非对称转向机构的优化模型;采用量子行为粒子群优化(QPSO)算法优化非对称转向机构的尺寸以及定位参数.结果表明,优化后的非对称转向机构最小转弯半径减小12.7％,整体性能提高28％.

This paper presents geometric error compensation of CNC machine tools based on workpiece model modification and the numerical solution of equation set. Firstly, the nominal cutter position is introduced to the geometric error model of machine tool as the compensation goal according to the kinematics of three-axis machine tools. With the polynomials of basic error components, the polynomials of the geometric error model are established, which are the functions about movements of all axes and nominal NC code. The accurate compensation is represented as solution of corresponding simultaneous equations. The high-efficiency numerical calculation can obtain the compensated NC code. Secondly, the polynomial equations about points of workpiece are established based on the simple relationship between workpiece model and NC codes. The compensated points are calculated using numerical solution. STL is chose as the format of reconstructed workpiece model. In order to obtain the precise workpiece model with compensated points, one conversion approach from CAD model to STL model is proposed. The number of points calculated with isoparametric method can control the precision of STL. The reconstructed STL model is inputted to CAM software to generate processing file for machining. The input of the proposed compensation is CAD model of workpiece rather than NC codes and CL data. It makes the compensation convenient and suitable for different three-axis machine tools. Finally, the experiments are carried out on Carver800T CNC machine tool to testify the effectiveness of proposed geometric error compensation.

A precision tilting platform used for static calibration of accelerometers was presented.Linear motor was utilized and its linear displacement was converted to the angular displacement of the platform.Simplified mechanical model,as well as measurement and control system for the platform were introduced,and the factors that caused positioning error were analyzed.Static error is caused mainly by temperature variation and the gravity.Manufacturing and assembling error affects the positioning accuracy through theoretical transformation of the linear and angular displacement.Dynamic error is mainly influenced by run out bearing in which parallelism offset of the linear guide rail can be disregarded.Main factors which caused the system error were obtained through experiments,then the compensation method was presented.Results indicate that the dimensional error caused by assembling is the main source of the positioning error.After compensation in software,the absolute error is reduced to less than ±2.0″in the range of -3 600″to +3 600″.

根据四向叉车的转向需求,设计适用于四向叉车的非对称转向机构;为了同时提高非对称转向机构纵向和横向转向性能,分别建立非对称转向机构纵向四轮转向和横向两轮转向的转向运动数学模型,并且构建纵向四轮左、右转向非对称性约束;以接近Ackermann理想转向为优化目标,建立非对称转向机构双目标优化函数,采用改进的粒子群优化(PSO)算法求解非对称转向机构双目标优化的Pareto最优解.优化结果分析算例表明纵向和横向转向性能可分别提高32.1％和38.9％,为非对称转向机构优化设计提供有益的理论参考.