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    Ultrasonic cutting with a disc cutter is an advanced machining method for the high-quality processing of Nomex honeycomb core. The machining quality is influenced by ultrasonic cutting parameters, as well as tool orientations, which are determined by the multi-axis machining requirements and the angle control of the cutting system. However, in existing research, the effect of the disc cutter orientation on the machining quality has not been studied in depth, and practical guidance for the use of disc cutters is lacking. In this work, the inclined ultrasonic cutting process with a disc cutter was analyzed, and cutting experiments with different inclination angles were conducted. The theoretical residual height models of the honeycomb core, as a result of the lead and tilt angles, were established and verified with the results obtained by a linear laser displacement sensor. Research shows that the residual height of the honeycomb core, as a result of the tilt angle, is much larger than that as a result of the lead angle. Furthermore, the tearing of the cell wall on the machined surface was observed, and the effects of the ultrasonic vibration, lead angle, and tilt angle on the tear rate and tear length of the cell wall were studied. Experimental results revealed that ultrasonic vibration can effectively decrease the tearing of the cell wall and improve the machining quality. Changes in the tilt angle have less effect than changes in the lead angle on the tearing of the cell wall. The determination of inclination angles should consider the actual processing requirements for the residual height and the machining quality of the cell wall. This study investigates the influence of the inclination angles of a disc cutter on the machining quality of Nomex honeycomb core in ultrasonic cutting and provides guidelines for machining.

    Yidan WANG ,   Renke KANG   et al.
    Deep learning has achieved much success in mechanical intelligent fault diagnosis in recent years. However, many deep learning methods cannot fully extract fault information to recognize mechanical health states when processing high-dimensional samples. Therefore, a multi-model ensemble deep learning method based on deep convolutional neural network (DCNN) is proposed in this study to accomplish fault recognition of high-dimensional samples. First, several 1D DCNN models with different activation functions are trained through dimension reduction learning to obtain different fault features from high-dimensional samples. Second, the obtained features are constructed into 2D images with multiple channels through a conversion method. The integrated 2D feature images can effectively represent the fault characteristic contained in raw high-dimension vibration signals. Lastly, a 2D DCNN model with multi-layer convolution and pooling is used to automatically learn features from the 2D images and identify the fault mode of the mechanical equipment by adopting a softmax classifier. The proposed method, which is validated using the bearing public dataset of Case Western Reserve University, USA and a one-stage reduction gearbox dataset, has high recognition accuracy. Compared with other classical deep learning methods, the proposed fault diagnosis method has considerable improvements.

    Xin ZHANG ,   Tao HUANG   et al.
    Safe and effective autonomous navigation in dynamic environments is challenging for four-wheel independently driven steered mobile robots (FWIDSMRs) due to the flexible allocation of multiple maneuver modes. To address this problem, this study proposes a novel multiple mode-based navigation system, which can achieve efficient motion planning and accurate tracking control. To reduce the calculation burden and obtain a comprehensive optimized global path, a kinodynamic interior–exterior cell exploration planning method, which leverages the hybrid space of available modes with an incorporated exploration guiding algorithm, is designed. By utilizing the sampled subgoals and the constructed global path, local planning is then performed to avoid unexpected obstacles and potential collisions. With the desired profile curvature and preselected mode, a fuzzy adaptive receding horizon control is proposed such that the online updating of the predictive horizon is realized to enhance the trajectory-following precision. The tracking controller design is achieved using the quadratic programming (QP) technique, and the primal–dual neural network optimization technique is used to solve the QP problem. Experimental results on a real-time FWIDSMR validate that the proposed method shows superior features over some existing methods in terms of efficiency and accuracy.

    Xiaolong ZHANG ,   Yu HUANG   et al.
    Taping is often used to protect patterned wafers and reduce fragmentation during backgrinding of silicon wafers. Grinding experiments using coarse and fine resin-bond diamond grinding wheels were performed on silicon wafers with tapes of different thicknesses to investigate the effects of taping on peak-to-valley (PV), surface roughness, and subsurface damage of silicon wafers after grinding. Results showed that taping in backgrinding could provide effective protection for ground wafers from breakage. However, the PV value, surface roughness, and subsurface damage of silicon wafers with taping deteriorated compared with those without taping although the deterioration extents were very limited. The PV value of silicon wafers with taping decreased with increasing mesh size of the grinding wheel and the final thickness. The surface roughness and subsurface damage of silicon wafers with taping decreased with increasing mesh size of grinding wheel but was not affected by removal thickness. We hope the experimental finding could help fully understand the role of taping in backgrinding.

    Zhigang DONG ,   Qian ZHANG   et al.

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