Measuring the motion of a robot accurately is an important and integral part of evaluating the dynamic and static performance of the robot. The performance index of a robot, such as kinematic accuracy, bearing capacity, deformation, vibration, stability, and structural mode can all be calculated according to the motion displacement of the robot. Therefore, improving the robot motion measurement method, promoting the measurement accuracy, and enriching the measurement content have received considerable scholarly attention worldwide. In this paper, an approach based on binocular vision was proposed to measure the 3D spatial motion of a robot. In the process of reconstructing robot movement, a mathematical model that can facilitate the solving process and improve the accuracy of results was derived to build 3D coordinates information. A novel coordinate transformation method that is based on the singular value decomposition was drawn up to realize the transformation from camera coordinates to robot coordinates. Several experiments were carried out on the self-built three-degree-of-freedom rectangular coordinate robot platform. The marker was designed specially and glued to the end of robot, and a new train of thought was adopted to extract the marker’s feature point. The vision-based measurement results were compared with the actual coordinate value. Results of experiments demonstrated that the proposed method can successfully reconstruct the 3D spatial motion of a robot more exactly, which can meet the requirements of high-precision motion control, motion performance evaluation, and operation state evaluation.
Abstract. The roundness error is the main geometric characteristic parameter of shaft and hole parts. Evaluation accuracy is an important indicator of the quality inspection technology. Existing roundness error evaluation methods are insufficient in terms of the calculation amount, convergence speed, and calculation accuracy. This study proposes a novel roundness error evaluation method based on improved bee colony algorithm to evaluate the roundness error of shaft and hole parts. Population initialization and search mechanism were considered for the optimal design to improve the convergence precision of the algorithm. The population was initialized in the local search domain defined by the contour data. The roughness error was obtained by the convergence solution of the circle center calculated iteratively by the step-decreasing method. The roundness error was also evaluated by taking the same set of image domain data as an example to verify the feasibility of the proposed method. The algorithm exhibited higher accuracy than that traditional methods and thus can be widely used to evaluate the roundness error of shaft and hole parts.
To enhance the friction and wear properties of 40Cr steel’s surface, CoCrFeMnNi high-entropy alloy (HEA) coatings with various Ti contents were prepared using laser cladding. X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS) were used to characterize the phase composition, microstructure, and chemical composition of the samples. The findings demonstrated that the CoCrFeMnNiTix HEA coatings formed a single FCC phase. Fe2Ti, Ni3Ti, and Co2Ti intermetallic compounds were discovered in the coatings when the molar ratio of Ti content was greater than 0.5. The EDS findings indicated that Cr and Co/Ni/Ti were primarily enriched in the dendrite and interdendrite, respectively. Ti addition can effectively enhance the coating’s mechanical properties. The hardness test findings showed that when the molar ratio of Ti was 0.75, the coating’s microhardness was 511 HV0.5, which was 1.9 times the hardness of the 40Cr (256 HV0.5) substrate and 1.46 times the hardness of the CrCrFeMnNi HEA coating (348 HV0.5). The friction and wear findings demonstrated that the addition of Ti can substantially reduce the coating’s friction coefficient and wear rate. The coating’s wear resistance was the best when the molar ratio of Ti was 0.75, the friction coefficient was 0.296, and the wear amount was 0.001 g. SEM and 3D morphology test results demonstrated that the coating’s wear mechanism changed from adhesive wear and abrasive wear to fatigue wear and abrasive wear with the increase in Ti content.
Due to its unique single-phase multivariate alloy characteristics and good low-temperature mechanical properties, CoCrFeNiMn high entropy alloy (HEA) has attracted the interest of many researchers in recent years. In this paper, to improve the wear resistance of Q235 alloy steel surface, CoCrFeNiMnSnx HEA coatings were prepared on the surface of Q235 steel via laser cladding. X-ray diffractometry, optical microscopy, scanning electron microscopy (SEM), and energy dispersive spectrometry were used to determine the microstructure and chemical composition. The research findings revealed that the CoCrFeNiMn HEA coatings were formed from a single FCC phase. As the Sn content in the coating increased, a new MnNi2Sn phase formed. Microhardness and friction and wear results showed that when the mole content of Sn was 0.2, the hardness of the CoCrFeNiMn HEA coating was increased by approximately 45%, the friction coefficient decreased by 0.168, and the wear loss decreased by 16.6%. Three-dimensional noncontact morphology and SEM results revealed that the wear mechanisms of CoCrFeNiMn HEA coatings were abrasive wear, delamination wear and a small amount of oxidative wear under dry friction conditions, whereas the friction mechanisms of CoCrFeNiMnSn0.2 HEA coatings were primarily abrasive wear and oxidative wear.
In the defect diagnosis of the gear-shaft-bearing system with compound defects, the generated vibration signals are complicated. In addition, the information acquired by a single sensor is easily affected by uncertain factors, and low diagnostic accuracy is caused when traditional defect diagnosis methods are used, which cannot meet the high-precision diagnosis requirements. Therefore, a method is developed to identify the defect types and defect degrees of the gear-shaft-bearing system efficiently. In this method, the vibration signals are collected using multiple sensors, the dual-tree complex wavelet and the optimal weighting factor (OWF) methods are used for the data layer fusion, and the preprocessing is realized through wavelet transform and FFT. A learning model based on two-stream CNN composed of 1D-CNN and 2D-CNN is established, and the obtained wavelet time-frequency map and FFT spectrum are used as the input. Then, the trained features from the output of the connected layer are classified by the SVM. Compared with the OWF-1DCNN and OWF-2DCNN models, the time consumption of the OWF-TSCNN model is increased by 14.5%–26.6%, and the convergence speed of the network is decreased. However, its accuracy reaches 100% and 99.83% in the training set and test set, and the loss entropy and over-fitting rate are also greatly reduced. The feature extraction ability and generalization ability of the OWF-TSCNN model are increased, reaching 100% diagnosis accuracy on different defect types and defect degrees, which is more suitable for defect diagnosis of the gear-shaft-bearing system.
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