Welding defects recognition based on arc sound signals remains a significant challenge in robot arc welding. Existing deep learning methods mainly focus on classifying penetration states rather than recognizing multiclass defects, such as slag inclusion and porosity. In addition, these methods lack physical interpretability of used neural network models, resulting in big risks if using in real welding applications. To address these issues, this article proposes an interpretable neural network named interpretable multiclass defect recognition network (IMDRNet) to recognize multiple welding defect types from arc sound signals. The original signals are first adaptively decomposed into several intrinsic mode functions (IMFs) with limited bandwidths and distinct center frequencies using the variational mode decomposition algorithm, which helps suppress noise interference and prevents the loss of useful information. Then, a multichannel continuous wavelet convolutional layer based on IMFs is designed as the first layer of the IMDRNet, which increases the depth of IMDRNet by using multiple parallel channels, thereby enhancing its capability to extract multiclass defect features. The proposed IMDRNet is tested and compared on two datasets with 9749 samples, and results indicate that the IMDRNet exhibits better effectiveness and interpretability in recognizing multiclass welding defects based on arc sound signals, achieving the highest accuracy of 97.09% on the SS355 dataset with six defect types and 91.70% on the AL5083 dataset with eight defect types.

With multiple fingers and multi-DOFs, an anthropomorphic hand can compliantly grasp objects with complex shape and low stiffness. In order to realize compliant grasp, contact force control between the anthropomorphic hand and the object is necessary. However, due to limited finger space for sensors and wiring, many anthropomorphic hands do not possess finger force sensors, so how to realize their compliant grasp control becomes a key issue. To solve this problem, this paper presents an impedance control method based on contact force observers and joint friction compensation for anthropomorphic hands without finger force sensors. A generalized momentum observer is used to estimate contact force, and an improved “static friction$+$Coulomb$+$viscous” model is adopted to realize joint friction compensation. The proposed impedance control method is verified both by simulations in Simulink and grasp experiments of an anthropomorphic hand. All the results show the method can not only estimate contact forces accurately and compensate joint friction, but also compliantly grasp objects with low stiffness and complex shape.Note to Practitioners—This paper is motivated by the demand for grasp control in anthropomorphic hands without finger force sensors. Force feedback is important in grasp control of anthropomorphic hands, but installing force sensors is highly challenging due to limited finger space for sensors and wiring, and the attendant consequences of increasing weight, volume and expenses. In order to realize such sensor-less grasp control, we present an impedance control method by calculating contact forces with a generalized momentum observer and estimating finger joint friction by an improved speed-friction model. Simulation studies and prototype experiments show the proposed method can stably grasp objects with varying stiffness and complex shapes while realizing expected dynamic characteristics. The proposed method can be used for other anthropomorphic hands without finger force sensors to improve their grasping capability. In future research, we will test the proposed method on more anthropomorphic hands without finger force sensors.

Automatic Term Extraction is an important issue in Natural Language Processing. This paper presents a new approach of terminology extraction combining with machine learning based on cascaded conditional random fields and corpus-based statistical model. In this approach, firstly, the low-layer and high-layer conditional random fields (CRFs) are used to extract the simple and compound terminologies respectively. Then, Domain Relevance (DR) and Domain Consensus (DC) degrees are calculated to acquire the final domain terminologies. Experimental results show that the precision, recall and F-score are 83.29%, 80.75%, 82.01% respectively. The comparison with CRFs and MI+T-value shows that the proposed method for extracting terminology is effective.

This paper presents a novel design of an omni-directional spherical robot that is mainly composed of a lucent ball-shaped shell and an internal driving unit. Two motors installed on the internal driving unit are used to realize the omni-directional motion of the robot, one motor is used to make the robot move straight and another is used to make it steer. Its motion analysis, kinematics modeling and controllability analysis are presented. Two typical motion simulations show that the unevenness of ground has big influence on the open-loop trajectory tracking of the robot. At last, motion performance of this spherical robot in several typical environments is presented with prototype experiments.