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Abstract A brushless DC (BLDC) motor is synchronous motor with trapezoidal/square wave counter-electromotive force, which is a typical example of highly coupled nonlinear systems. In industrial control, BLDC motor drive usually uses proportional–integral (PI) controller to control the speed, but it is very difficult to adjust the scale factors. In this study, we present a particle swarm algorithm-tuned fuzzy logic-PI (PF-PI) controller applied to the speed control system. The objective of this paper is to optimally tune the PI controller parameters to obtain the best drive response. The scale factors are optimized using particle swarm optimized-PI (P-PI) controller and PF-PI controller. The three performance indicators integral time absolute error (ITAE), integral time square error (ITSE) and integral square error (ISE) are used to measure the effectiveness of PF-PI controller optimization. The results show that the optimal torque ripple and speed response curves are obtained by using ITAE as the performance indicator. The conclusions demonstrate that the proposed method provides superior dynamic performance for BLDC motor. Highlights (1) In terms of research content, we propose a new PF-PI controller driven control system based on the traditional BLDC speed control system, and the applicability of three performance indicators on the controller is discussed. (2) In terms of research method, we compare the no-load start, variable speed and sudden addition disturbance load start capabilities of P-PI controller and PF-PI controller, and verify the fast and robustness of PF-PI controller. (3) In the research significance, the PI controller structure is improved and the dynamic performance of BLDC speed control system is enhanced.

Abstract Wafer dicing saw is one of the core equipment in the manufacturing process of semiconductor integrated circuit components. The cutting accuracy of dicing saw directly affects the overall quality of processed chips. This paper systematically investigates the relationship between the main cutting process parameters and the cutting quality of the dicing saw. The orthogonal experimental design method and genetic algorithm are used to optimize the cutting process parameters, solving the high cost and low efficiency problems caused by the traditional way of selecting parameters by trial and error. After optimization, the average maximum chipping width is only 38.54 μm, which is 8.23% better than the traditional way of cutting quality. Based on the blade thickness of 35 μm, the maximum chipping width reached the industry-recognized best standard of 1.1 times the blade thickness, further proving the effectiveness of the method. Article Highlights The first joint application of orthogonal regression design method and evoluti-onary algorithm for parameter optimiza-tion of dicing saw. A set of optimal cutting parameters are found and verified by experiments. Compare and contrast the optimal cutting parameters cutting’s performance.

In order to solve the problem that steel surface defects are easily covered or submerged by other objects or noise, this paper proposes an open–closed transformation algorithm which can eliminate or weaken multiple noises. In the case of a small number of samples, this paper establishes a super-resolution generative adversarial neural network to achieve the enhancement of sample data. For avoiding unrealistic image defects caused by cuts or brightness variations, an enhancement method is given which incorporates the original defective high-frequency information into classical image fusion methods, such as rotation and error slicing. Experimental results show that the accuracy of the proposed denoising method reaches over 90%, which is more than 2.6% of that of the most primitive classification network. To compare with existing denoising methods, the denoising method proposed in this paper not only has higher accuracy, faster denoising speed, and stronger anti-interference ability, but also has better adaptation to the environment. This research will provide a new solution method for the denoising of multi-noise phenomena in multiple different environments.

Crack identification plays a vital role in preventive maintenance strategies during highway pavement maintenance. Therefore, accurate identification of cracks in highway pavement images is the key to highway maintenance work. In this paper, an improved U-Net network adopting multi-scale feature prediction fusion and the improved parallel attention module was put forward to better identify concrete cracks. Multiscale feature prediction fusion combines multiple U-Net features generated by intermediate layers for aggregated prediction, thus using global information from different scales. The improved parallel attention module is used to process the U-Net decoded output of multi-scale feature prediction fusion, which can give more weight to the target region in the image and further capture the global contextual information of the image to improve the recognition accuracy. Improving the bottleneck layer is used to improve the robustness of the model and prevent overfitting. Experiments show that the improved U-Net network in this paper has a significant improvement over the original U-Net network. The performance of the proposed method in this paper was investigated on two publicly available datasets (Crack500 and CFD) and compared with competing methods proposed in the literature. Using the Crack500 dataset, the method in this paper achieved the highest score in precision (89.60%), recall (95.83%), mIOU (83.80%), and F1-score (92.61%). Similarly, for the CFD dataset, the method in this paper achieved high values for precision (93.29%), mIOU (82.07%), recall (86.26%), and F1-score (89.64%). Thus, the method has several advantages for identifying cracks in highway pavements and is an ideal tool for practical work. In future work, identifying more crack types and model light-weighting are the key objectives. Meanwhile, this paper provides a new idea for road crack identification.

Path planning is an important aspect and component in the research of mobile-robot-related technologies. Many path planning algorithms are only applicable to static environments, while in practical tasks, the uncertainty in dynamic environments increases the difficulty of path planning and obstacle avoidance compared with static environments. To address this problem, this paper proposes an RRT*-FDWA algorithm. RRT* first generates a global optimal path, and then, when obstacles exist nearby, an FDWA algorithm fixes the local path in real time. Compared with other path planning algorithms, RRT*-FDWA can avoid local minima, rapidly perform path replanning, generate a smooth optimal route, and improve the robot’s maneuvering amplitude. In this paper, the effectiveness of the algorithm is verified through experiments in dynamic environments.