Design and Study of Machine Tools for the Fly-Cutting of Ceramic-Copper Substrates.

Ceramic-copper substrates, as high-power, load-bearing components, are widely used in new energy vehicles, electric locomotives, high-energy lasers, integrated circuits, and other fields. The service length will depend on the substrate's copper-coated surface quality, which frequently achieved by utilising an abrasive strip polishing procedure on the substrate's copper-coated surface. Precision diamond fly-cutting processing machine tools were made because of the low processing accuracy and inability to match the production line's efficiency. An analysis of the fly-cutting machining principle and the structural makeup of the ceramic-copper substrate is the first step in creating a roughness prediction model based on a tool tip trajectory. This model demonstrates that a shift in the tool tip trajectory due to spindle runout error directly impacts the machined surface's roughness. The device's structural optimisation design is derived from the above analyses and implemented using finite element software. Modal and harmonic response analysis validated the machine's gantry symmetrical structural layout, a parametric variable optimisation design optimised the machine tool's overall dimensions, and simulation validated the fly-cutterring's constituent parts. Enhancing the machine tool's stability and motion accuracy requires using the LK-G5000 laser sensor to measure the guideway's straightness. The result verified the machine tool's design index, with the Z- and Y-axes' straightness being better than 2.42 µm/800 mm and 2.32 µm/200 mm, respectively. Ultimately, the device's machining accuracy was confirmed. Experiments with flying-cut machining on a 190 × 140 mm ceramic-copper substrate yielded a roughness of Sa9.058 nm. According to the experimental results, the developed machine tool can fulfil the design specifications.

Recycling and Reutilizing Polymer Waste via Electrospun Micro/Nanofibers: A Review.

The accumulation of plastic waste resulting from the increasing demand for non-degradable plastics has led to a global environmental crisis. The severe environmental and economic drawbacks of inefficient, expensive, and impractical traditional waste disposal methods, such as landfills, incineration, plastic recycling, and energy production, limit the expansion of their applications to solving the plastic waste problem. Finding novel ways to manage the large amount of disposed plastic waste is urgent. Until now, one of the most valuable strategies for the handling of plastic waste has been to reutilize the waste as raw material for the preparation of functional and high-value products. Electrospun micro/nanofibers have drawn much attention in recent years due to their advantages of small diameter, large specific area, and excellent physicochemical features. Thus, electrospinning recycled plastic waste into micro/nanofibers creates diverse opportunities to deal with the environmental issue caused by the growing accumulation of plastic waste. This paper presents a review of recycling and reutilizing polymer waste via electrospinning. Firstly, the advantages of the electrospinning approach to recycling plastic waste are summarized. Then, the studies of electrospun recycled plastic waste are concluded. Finally, the challenges and future perspectives of electrospun recycled plastic waste are provided. In conclusion, this paper aims to provide a comprehensive overview of electrospun recycled plastic waste for researchers to develop further studies.

Research Progress on Sound Absorption of Electrospun Fibrous Composite Materials.

Noise is considered severe environmental pollutant that affects human health. Using sound absorption materials to reduce noise is a way to decrease the hazards of noise pollution. Micro/nanofibers have advantages in sound absorption due to their properties such as small diameter, large specific surface area, and high porosity. Electrospinning is a technology for producing micro/nanofibers, and this technology has attracted interest in the field of sound absorption. To broaden the applications of electrospun micro/nanofibers in acoustics, the present study of electrospun micro/nano fibrous materials for sound absorption is summarized. First, the factors affecting the micro/nanofibers' sound absorption properties in the process of electrospinning are presented. Through changing the materials, process parameters, and duration of electrospinning, the properties, morphologies, and thicknesses of electrospun micro/nanofibers can be controlled. Hence, the sound absorption characteristics of electrospun micro/nanofibers will be affected. Second, the studies on porous sound absorbers, combined with electrospun micro/nanofibers, are introduced. Then, the studies of electrospun micro/nanofibers in resonant sound absorption are concluded. Finally, the shortcomings of electrospun micro/nano fibrous sound absorption materials are discussed, and the future research is forecasted.

A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy.

BACKGROUND AND PURPOSE: Delineating organs at risk (OARs) on computed tomography (CT) images is an essential step in radiation therapy; however, it is notoriously time-consuming and prone to inter-observer variation. Herein, we report a deep learning-based automatic segmentation (AS) algorithm (WBNet) that can accurately and efficiently delineate all major OARs in the entire body directly on CT scans. MATERIALS AND METHODS: We collected 755 CT scans of the head and neck, thorax, abdomen, and pelvis and manually delineated 50 OARs on the CT images. The CT images with contours were split into training and test sets consisting of 505 and 250 cases, respectively, to develop and validate WBNet. The volumetric Dice similarity coefficient (DSC) and 95th-percentile Hausdorff distance (95% HD) were calculated to evaluate delineation quality for each OAR. We compared the performance of WBNet with three AS algorithms: one commercial multi-atlas-based automatic segmentation (ABAS) software, and two deep learning-based AS algorithms, namely, AnatomyNet and nnU-Net. We have also evaluated the time saving and dose accuracy of WBNet. RESULTS: WBNet achieved average DSCs of 0.84 and 0.81 on in-house and public datasets, respectively, which outperformed ABAS, AnatomyNet, and nnU-Net. WBNet could reduce the delineation time significantly and perform well in treatment planning, with clinically acceptable dose differences compared with those in manual delineation. CONCLUSION: This study shows the feasibility and benefits of using WBNet in clinical practice.

Aspheric Surface Measurement Using Capacitive Sensors.

This paper proposes a new method for the measurement of spherical coordinates by using capacitive sensors as a non-contact probe solution of measurement of aspheric surfaces. The measurement of the average effect of the capacitive probe and the influence of capacitive probe tilting were studied with respect to an eccentric spherical surface. Based on the tested characteristic curve of the average effect of the sphere and probe, it was found that nonlinear and linear compensation resulted in high measurement accuracy. The capacitance probe was found to be trying to fulfill a need for performing nm-level precision measurement of aspheric electromagnetic surfaces.

Multi-Repeated Projection Lithography for High-Precision Linear Scale Based on Average Homogenization Effect.

A multi-repeated photolithography method for manufacturing an incremental linear scale using projection lithography is presented. The method is based on the average homogenization effect that periodically superposes the light intensity of different locations of pitches in the mask to make a consistent energy distribution at a specific wavelength, from which the accuracy of a linear scale can be improved precisely using the average pitch with different step distances. The method's theoretical error is within 0.01 µm for a periodic mask with a 2-µm sine-wave error. The intensity error models in the focal plane include the rectangular grating error on the mask, static positioning error, and lithography lens focal plane alignment error, which affect pitch uniformity less than in the common linear scale projection lithography splicing process. It was analyzed and confirmed that increasing the repeat exposure number of a single stripe could improve accuracy, as could adjusting the exposure spacing to achieve a set proportion of black and white stripes. According to the experimental results, the effectiveness of the multi-repeated photolithography method is confirmed to easily realize a pitch accuracy of 43 nm in any 10 locations of 1 m, and the whole length accuracy of the linear scale is less than 1 µm/m.