Prereflow automatic optical inspection (AOI) has been widely used to ensure product quality in surface mount technology (SMT). When confronted with a complex industrial environment, traditional hand-designed visual inspection algorithms may lack robustness and generalizability. In this article, PCBNet, a convolutional neural network (CNN) method that combines data preprocessing, detection network, and visualization, is proposed to localize electronic components and recognize defects. In the data preprocessing stage, raw images are segmented into several regions of interest (ROIs). The ROI patches are inspected by a CNN-based detection system, which is capable of classifying defects and positioning components. After inspection, the reporting system visualizes the results via the human–computer interface. In comparative studies, the effectiveness of the proposed PCBNet was validated on a large-scale PCB component defect dataset. The PCBNet backbone outperforms other well-known lightweight CNN backbones in terms of accuracy and latency on $4\times $ ARM Cortex A72 CPU @ 1.5 GHz. Compared to other learning-based methods on the small-scale benchmark dataset, the PCBNet also achieves the best balance between inference speed and accuracy. In addition, extensive experiments demonstrate the superior efficiency of PCBNet in comparison to some famous traditional object detectors and novel oriented object detection algorithms.

Machining feature recognition is a research hotspot in recent years. A point cloud is a geometry data representation format of three-dimensional (3D) models. The use of point cloud-based convolutional neural networks (CNNs) for machining feature recognition has received increasing research attention. However, these point cloud-based networks usually have large complexity size and training time. In this paper, a selective downsampling-based point neural network for machining feature recognition is proposed. Firstly, a machining feature dataset called MFDataset is constructed and contains 33 feature types. Secondly, a selective downsampling algorithm of the input points is presented, which drops out unimportant points while keeping the important ones. In single-machining feature recognition, MFPointNet is proposed by utilizing the selective downsampling of the input points. In multi-machining feature recognition, the segmentation part of the MFPointNet is adopted with the selective downsampling algorithm to segment and recognize multiple features. Compared with other point cloud-based networks, experimental results show that MFPointNet reduces the computational complexity without losing the recognition accuracy basically. MFPointNet is more robust to model complexity when more machining feature points are input to the network. Moreover, several intersecting feature models validate the segmentation performance of MFPointNet.

In this paper, the design of a fractional-order proportional integral (FOPI) controller and integer-order (IOPI) controller are compared for the permanent magnet synchronous motor (PMSM) speed regulation system. A high-precision implementation method of a fractional-order proportional integral (FOPI) controller is proposed in this work. Three commonly used numerical implementation methods of fractional operators are investigated and compared for comprehensively evaluating the numerical implementation performance in this work. Furthermore, for the impulse response invariant implementation method, the effects of different discretization orders on the control performance of the system are compared. The high-order fractional-order controller can be implemented accurately in a control system with the field-programmable gate array (FPGA) with the capability of parallel calculation. The simulation and experimental results show that the high-precision numerical implementation method of the designed high-order FOPI controller has better performance than the ordinary precision fractional operation implementation method and traditional order integer order PI controller.

In this paper, a fractional-order active disturbance rejection controller (FOADRC), combining a fractional-order proportional derivative (FOPD) controller and an extended state observer (ESO), is proposed for a permanent magnet synchronous motor (PMSM) speed servo system. The global stable region in the parameter (K_p, K_d, μ)-space corresponding to the observer bandwidth ω_o can be obtained by D-decomposition method. To achieve a satisfied tracking and anti-load disturbance performance, an optimal ADRC tuning strategy is proposed. This tuning strategy is applicable to both FOADRC and integer-order active disturbance rejection controller (IOADRC). The tuning method not only meets user-specified frequency-domain indicators but also achieves a time-domain performance index. Simulation and experimental results demonstrate that the proposed FOADRC achieves better speed tracking, and more robustness to external disturbance performances than traditional IOADRC and typical Proportional-Integral-Derivative (PID) controller. For example, the J_ITAE for speed tracking of the designed FOADRC are less than 52.59% and 55.36% of the J_ITAE of IOADRC and PID controller, respectively. Besides, the J_ITAE for anti-load disturbance of the designed FOADRC are less than 17.11% and 52.50% of the J_ITAE of IOADRC and PID controller, respectively.