In this paper an axisymmetric model of an omnidirectional electromagnetic acoustic transducer (EMAT) used to generate Lamb waves in conductive plates is introduced. Based on the EMAT model, the structural parameters of the permanent magnet were used as the design variables while other parameters were fixed. The goal of the optimization was to strengthen the generation of the A0 mode and suppress the generation of the S0 mode. The amplitudes of the displacement components at the observation point of the plate were used for calculation of the objective functions. Three approaches to obtain the amplitudes were discussed. The first approach was solving the peak values of the envelopes of the time waveforms from the time domain simulations. The second approach also involved calculation of the peaks, but the waveforms were from frequency domain model combined with the forward and inverse Fourier transforms. The third approach involved a single frequency in the frequency domain model. Single and multi-objective optimizations were attempted, implemented with the genetic algorithms. In the single objective optimizations, the goal was decreasing the ratio of the amplitudes of the S0 and A0 modes, while in the multi-objective optimizations, an extra goal was strengthening the A0 mode directly. The Pareto front from the multi-objective optimizations was compared with the estimation from the data on the discrete grid of the design variables. From the analysis of the results, it could be concluded that for a linearized steel plate with a thickness of 10mm and testing frequency of 50kHz, the point with minimum S0/A0 could be selected, thus the multi-objective optimization effectively degenerated to the single objective optimization. While for an aluminum plate with a thickness of 3mm and frequency of 150kHz, without further information it would be difficult to select one particular solution from the Pareto front.
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Sci Rep
January 2025
Xingtai Naknor Technology Co., Ltd, Xingtai, 054000, China.
The heating oil circuit plays an essential role in the heating calendering roller for the lithium battery pole piece. To achieve the optimization of the heating oil circuit, a fluid-thermal-structural coupling method and a multi-objective optimization procedure are proposed to obtain the optimal solution. A fluid-thermal-structural coupling flowchart based on the numerical modeling for the calendering roller temperature distribution is created to automate the analysis processes in the optimization iteration.
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January 2025
Advanced Manufacturing Lab, ETH Zürich, Leonhardstrasse 21, 8092, Zurich, Switzerland.
The rapid advancements in additive manufacturing (AM) across different scales and material classes have enabled the creation of architected materials with highly tailored properties. Beyond geometric flexibility, multi-material AM further expands design possibilities by combining materials with distinct characteristics. While machine learning has recently shown great potential for the fast inverse design of lattice structures, its application has largely been limited to single-material systems.
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January 2025
Changchun Institute of Optics, Fine Mechanics and Physics (CIOMP), Chinese Academy of Sciences, Changchun 130033, China.
To improve maneuverability, the focus of photoelectric theodolites is on reducing the weight of the primary mirror and enhancing its optical performance. This study uses MOAT and Sobol methods to identify key parameters that affect design. Using the high-sensitivity part as the optimization domain, six optimization results were obtained based on the multi-objective SIMP topology optimization method and synthesized into a compromise optimization structure.
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January 2025
Free-Space Optical Communication Technology Research Center, Harbin Institute of Technology, Harbin 150001, China.
To achieve real-time deep learning wavefront sensing (DLWFS) of dynamic random wavefront distortions induced by atmospheric turbulence, this study proposes an enhanced wavefront sensing neural network (WFSNet) based on convolutional neural networks (CNN). We introduce a novel multi-objective neural architecture search (MNAS) method designed to attain Pareto optimality in terms of error and floating-point operations (FLOPs) for the WFSNet. Utilizing EfficientNet-B0 prototypes, we propose a WFSNet with enhanced neural architecture which significantly reduces computational costs by 80% while improving wavefront sensing accuracy by 22%.
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January 2025
School of Biomedical Engineering, Tsinghua University, Shuang Qing Road, Beijing 100084, China.
Mastoidectomy is critical in acoustic neuroma surgery, where precise planning of the bone milling area is essential for surgical navigation. The complexity of representing the irregular volumetric area and the presence of high-risk structures (e.g.
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