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Weld bead measurement and weld quality inspection are important parts in industrial welding. In this paper, a structured light vision sensor is developed to achieve on-line weld bead measurement and weld quality inspection. Firstly, a structured light vision sensor with a narrow-band optical filter is developed to reduce welding noises such as arc lights and splashes. Secondly, the weld bead type identification algorithm including image pre-processing, baseline extraction, and weld bead classification is proposed to classify filling weld bead and capping weld bead. Thirdly, feature extraction algorithms of filling weld bead and capping weld bead are presented to obtain corresponding feature points. Combining the image coordinates of feature points with structured light vision model, the weld bead size could be obtained and the weld quality could be evaluated. Finally, many weld bead measurement and weld quality inspection experiments are conducted. Experimental results demonstrate that the developed structured light vision sensor and proposed methods could achieve satisfactory performance for weld quality inspection.

In this paper, an automatic recognition system of welding seam type based on support vector machine (SVM) method is presented. The hardware of the proposed system consists of an industry robot with six degrees of freedom, a vision sensor, and a computer. The system has two parts including input feature vector computation and model building. In the input feature vector computation part, the depth values of a series of points of the welding joint are taken as feature vector, which are determined by four steps including main line extraction of the laser stripe, normalization of the laser stripe, selection of the left and right edge points of the welding joint, and normalization of feature vectors. In the model building part, SVM-based modeling method is used to achieve welding seam type recognition. At first, RBF kernel function is employed for classification of welding seam types. Then, the parameters of RBF are determined by a grid search method using cross-validation. After the optimal parameters of RBF being determined, the SVM model is built, and it could be used to predict welding seam type. Finally, a series of welding seam type recognition experiments are implemented. Experimental results show that the proposed system can achieve welding seam type recognition accurately and the computation cost can be reduced compared with previous methods.

With the rapid growth of high-voltage transmission lines, the number of power transmission line equipments is correspondingly increasing. Power insulator is the basic component which plays the key role in the stable operation of power system. As a common defect of power insulators, missing-cap issue will affect the structural strength and durability of different power insulators. Therefore, the condition monitoring of power insulators is a daily but priority power line inspection task. Faced with the weak image features of small insulator defects in the aerial images, the conventional handcrafted features could not extract effectively powerful image features. Meanwhile, the small-scale insulator defects will bring a certain effect to the model training of deep learning. Therefore, the high-efficiency and accurate defect inspection still present a challenging task against complex backgrounds. To address the above issues, aimed at the missing-cap defects of power insulators, a novel defect identification algorithm from aerial images is proposed by taking advantage of state-of-the-art deep learning and transfer learning models. Fused with Spatial Pyramid Pooling (SPP) and MobileNet networks, a light deep convolutional neural network (DCNN) model based on You Only Look Once (YOLO) V3 network is proposed for fast and accurate insulator location to remove complex background interference. On the basis, combined with Dempster–Shafer (DS) evidence theory, the improved transfer learning model based on feature fusion is proposed for high-precision defect identification of power insulators. Experiments show that the proposed method could acquire a better identification performance against complex power inspection environment compared with other related detection models.

Robot welding is a basic but indispensable technology for many industries in modern manufacturing. However, many welding parameters affect welding quality. During the real welding process, welding defects are inevitably generated that affect the structural strengths and comprehensive performances of different welding products. Therefore, an accurate welding defect recognition algorithm is necessary for automatic robot welding to assess the effects of defects on structural properties and system maintenance. Much work has been devoted to welding defect recognition. It can be mainly divided into two categories: feature-based and deep learning-based methods. The detection performances of feature-based methods rely on effective image features and strong classifiers. However, faced with weak-textured and weak-contrast welding images, the realization of strong image feature expression still faces a certain challenge. Deep learning-based methods can provide end-to-end detection schemes for welding robots. Nevertheless, an effective deep network model relies on much training data that are not easily collected during real manufacturing. To address the above issues regarding defect detection, a novel welding defect recognition algorithm is proposed based on multi-feature fusion for accurate defect detection based on X-ray images. To improve network training, an effective data augmentation process is proposed to construct the dataset. Combined with transfer learning, the multi-scale features of welding images are acquired for effective feature expression with the pre-trained AlexNet network. On this basis, based on multi-feature fusion, a welding defect recognition algorithm fused to a support vector machine with Dempster–Shafer evidence theory is proposed for multi-scale defect detection. Experiments show that the proposed method achieves a better recognition performance in terms of detecting welding defects than those of other related recognition algorithms.

Background Drought and nitrogen (N) deficiency are two major limiting factors for forest productivity in many ecosystems. Elucidating the mechanisms underlying the influence of soil N availability on drought responses of tree species is crucial to improve tree growth under drought. Results The root proliferation under drought was enhanced by adequate N application. Vessel frequency in xylem increased upon drought, with more significant increase under adequate N conditions compared with that under low N conditions, possibly leading to increased hydraulic safety. Nitrogen application under drought increased indole acetic acid (IAA), which contributed to the adaptive changes of xylem. Nitrogen application increased leaf abscisic acid (ABA) concentration, therefore regulated stomata adjustment, and promoted intrinsic water use efficiency ( WUE i ). Moreover, N application promoted antioxidant defense in leaves by showing increased level of free proline and carotenoid, which improved drought tolerance and growth performance of poplars. Conclusions Anatomical and physiological responses of Populus to drought were suppressed by N deficiency. Adequate N application promoted adaptive changes of root and xylem under drought and increased hydraulic safety. Nitrogen addition under drought also increased leaf ABA level which may regulate stomata adjustment and promote WUE i . Moreover, nitrogen application improved antioxidant defense in leaves with increased levels of antioxidants. These positive regulations improved drought tolerance and growth performance of poplars.

Nowadays, off-line robot trajectory generation methods based on pre-scanned target model are highly desirable for robotic spray painting application. For actual implementation of the generated trajectory, the relative pose between the actual target and the model needs to be calibrated in the first place. However, obtaining this relative pose remains a challenge, especially from a safe distance in industrial setting. In this paper, a pose estimation system that is able to meet the robotic spray painting requirements is proposed to estimate the pose accurately. The system captures the image of the target using RGB-D vision sensor. The image is then segmented using a modified U-SegNet segmentation network and the resulting segmentation is registered with the pre-scanned model candidates using iterative closest point (ICP) registration to obtain the estimated pose. To strengthen the robustness, a deep convolutional neural network is proposed to determine the rough orientation of the target and guide the selection of model candidates accordingly thus preventing misalignment during registration. The experimental results are compared with relevant researches and validate the accuracy and effectiveness of the proposed system.

In the modern manufacturing industry, the welding quality is one of the key factors which affect the structural strength and the comprehensive quality of the products. It is an important part to establish the standard of welding quality detection and evaluation in the process of production management. At present, the detection technologies of welding quality are mainly performed based on the 2D image features. However, due to the influence of environmental factors and illumination conditions, the welding quality detection results based on grey images are not robust. In this paper, a novel welding detection system is established based on the 3D reconstruct technology for the arc welding robot. The shape from shading (SFS) algorithm is used to reconstruct the 3D shapes of the welding seam and the curvature information is extracted as the feature vector of the welds. Furthermore, the SVM classification method is adopted to perform the evaluation task of welding quality. The experimental results show that the system can quickly and efficiently fulfill the detection task of welding quality, especially with good robustness for environmental influence cases. Meanwhile, the method proposed in this paper can well solve the weakness issues of conventional welding quality detection technologies.

During the surface milling process, the curved surface components, which were made of carbon fiber-reinforced plastics (CFRP), were prone to complex deformations. These resulted in ineffective material removal, thereby inducing undesirable damages. To address this problem, the present study analyzed the material removal process during the curved surface milling of CFRP components. A novel residual height calculation method, considering the deformation of the curved CFRP surface components, was initially proposed, to describe the material removal quality quantificationally. After applying this method for CFRP cylindrical components, surface milling experiments were conducted, and the influences of the process parameters on material removal were analyzed. Results suggested that reducing the axial cutting depth and the tool feed resulted in better surface appearance and less machining damage of the curved CFRP surfaces, while increasing the spindle speed reduced the machining damage only, but had little effect on the surface appearance. The findings of the present study facilitated the understanding of the material removal law in the surface milling processes of curved CFRP surface components, and also provided foundations for improving milling quality.

In this paper, an initial point alignment method of narrow weld using laser vision sensor is presented on the basis of the relationship between the feature point of laser stripe and initial point. The whole initial point alignment process contains two stages. At the first stage, the initial point image is captured, and the image coordinates of the feature point of laser stripe and initial point are obtained. At the second stage, according to the relationship between the feature point of laser stripe and initial point, the three-dimensional (3D) coordinates of initial point could be determined to achieve initial point alignment. The initial point alignment method mainly includes vision sensing and motion control two parts. Firstly, a new laser vision sensor with a uniform LED surface light source is developed to capture the high signal-to-noise ratio (SNR) image including narrow weld, and the feature point of laser stripe and initial point are detected using the image processing method. Secondly, initial point alignment control system including feature verification and controller is designed to achieve initial point alignment control. Finally, a series of initial point alignment experiments of straight and curve narrow weld are conducted to test the performance of the proposed method. Experimental results indicate the alignment error is less than previous methods, which could be used in automatic welding process.

Beneficial health effects of fruits and vegetables in the diet have been attributed to their high flavonoid content. Dipeptidyl peptidase IV (DPP-IV) is a serine aminopeptidase that is a novel target for type 2 diabetes therapy due to its incretin hormone regulatory effects. In this study, well-characterized anthocyanins (ANC) isolated from berry wine blends and twenty-seven other phenolic compounds commonly present in citrus, berry, grape, and soybean, were individually investigated for their inhibitory effects on DPP-IV by using a luminescence assay and computational modeling. ANC from blueberry-blackberry wine blends strongly inhibited DPP-IV activity (IC50, 0.07 ± 0.02 to >300 μM). Of the twenty-seven phenolics tested, the most potent DPP-IV inhibitors were resveratrol (IC50, 0.6 ± 0.4 nM), luteolin (0.12 ± 0.01 μM), apigenin (0.14 ± 0.02 μM), and flavone (0.17 ± 0.01 μM), with IC50 values lower than diprotin A (4.21 ± 2.01 μM), a reference standard inhibitory compound. Analyses of computational modeling showed that resveratrol and flavone were competitive inhibitors which could dock directly into all three active sites of DPP-IV, while luteolin and apigenin docked in a noncompetitive manner. Hydrogen bonding was the main binding mode of all tested phenolic compounds with DPP-IV. These results indicate that flavonoids, particularly luteolin, apigenin, and flavone, and the stilbenoid resveratrol can act as naturally occurring DPP-IV inhibitors.