Ultrasonic particle manipulation technique is a non-contact label-free method for manipulating micro- and nano-scale particles using ultrasound, which has obvious advantages over traditional optical, magnetic, and electrical micro-manipulation techniques; it has gained extensive attention in micro-nano manipulation in recent years. This paper introduces the basic principles and manipulation methods of ultrasonic particle manipulation techniques, provides a detailed overview of the current mainstream acoustic field generation methods, and also highlights, in particular, the applicable scenarios for different numbers and arrangements of ultrasonic transducer devices. Ultrasonic transducer arrays have been used extensively in various particle manipulation applications, and many sound field reconstruction algorithms based on ultrasonic transducer arrays have been proposed one after another. In this paper, unlike most other previous reviews on ultrasonic particle manipulation, we analyze and summarize the current reconstruction algorithms for generating sound fields based on ultrasonic transducer arrays and compare these algorithms. Finally, we explore the applications of ultrasonic particle manipulation technology in engineering and biological fields and summarize and forecast the research progress of ultrasonic particle manipulation technology. We believe that this review will provide superior guidance for ultrasonic particle manipulation methods based on the study of micro and nano operations.

This article proposes a two-finger-based micrograsping system with high compliant borosilicate 3.3 glass probes and the corresponding sensing and control algorithms, which enables the orientation manipulation of microparts in three-dimensional (3-D) space. Compared with the existing research, the novelty of this article relies on three aspects: 1) the end-effector of the microgripper is designed to be with high compliance so that the squeeze force exerted on microparts can be more accurately regulated and the proposed microgripper is capable of manipulating fragile microparts; 2) the micrograsping system is endowed the capability to fully manipulate microparts' orientation without recurring to auxiliary probes or rotary stages; and 3) the vibration characteristic of the grasping arm is investigated as cantilever beam for gasping stability analysis and squeeze force maintaining. In specific, taking spherical microparts with dimensions in the range from tens to hundreds of micrometers as target, the grasping system configuration, and the contact model between the probe and the microparts are first presented. Afterward, kinematics-based motion control strategy for position adjustment and orientation manipulation of microparts is clarified. Then, squeeze force regulation strategy is proposed, including adhesive force evaluation, vision-based squeeze force estimation, and the micropart releasing method. Finally, the vibration characteristic of the grasping arm is investigated as cantilever beam for gasping stability analysis and the squeeze force maintaining. The reliability and availability of the proposed micrograsping system is validated by well-designed experiments.

Defect detection is a crucial task in screen-printed circuit (SPC) production, where image processing method based on deep learning is often used. This field frequently encounters challenges, such as minute surface defects, a large number of model parameters, and high computational complexity. To address these challenges, a self-made SPC defect data set and an enhanced CAAB-YOLOv8n detection algorithm were developed. A CAD module was integrated into the backbone network to improve the model’s ability to detect bar-shaped features. In addition, the ASF feature fusion and RMT modules were combined to construct the ASF-CR neck structure, which enhances the model’s capability to detect small, localized defects. To expedite inference speed, the DBB-Head reparameterization module was incorporated. Experimental results show that the enhanced algorithm achieves 88.4 $$\%$$ % accuracy, a mAP@50 of 90.2 $$\%$$ % , and a parameter count of just 33.27 million, with a detection speed of 35.2 frames per second. The real-time requirements for SPC defect detection are met by these findings. This work lays a solid theoretical foundation for subsequent defect traceability and the optimization of printing process parameters.

Phased transducer arrays (PTA) can control ultrasonic waves to produce a holographic acoustic field. However, obtaining the phase of the corresponding PTA from a given holographic acoustic field is an inverse propagation problem, which is a mathematically unsolvable nonlinear system. Most of the existing methods use iterative methods, which are complex and time-consuming. To better solve this problem, this paper proposed a novel method based on deep learning to reconstruct the holographic sound field from PTA. For the imbalance and randomness of the focal point distribution in the holographic acoustic field, we constructed a novel neural network structure incorporating attention mechanisms to focus on useful focal point information in the holographic sound field. The results showed that the transducer phase distribution obtained from the neural network fully supports the PTA to generate the corresponding holographic sound field, and the simulated holographic sound field can be reconstructed with high efficiency and quality. The method proposed in this paper has the advantage of real-time performance that is difficult to achieve by traditional iterative methods and has the advantage of higher accuracy compared with the novel AcousNet methods.

The method of physically recording acoustic holograms is time-consuming and inefficient, hindering the ability to rapidly reconstruct acoustic holograms. This text aims to propose a deep learning strategy based on an improved IU-Net to rapidly reconstruct acoustic holograms with phase information. The angular spectrum method is used to simulate the phase information distribution of different sound sources, positions, and initial angles, thereby quickly generating the required data samples, especially for complex or dynamic image scenarios. The framework employs a custom learning rate adjustment mechanism to dynamically adjust the learning rate in real-time, adapt to the training process, and support a stepwise learning strategy. Experimental results show that the proposed method achieves an accuracy of 90.9% in acoustic hologram reconstruction, with a Dice coefficient of 0.928, improving reconstruction speed by over 30% compared to traditional methods, ensuring high-quality and rapid reconstruction of acoustic holograms.