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Nickel-based superalloy GH4169 is widely demanded in the aerospace industry due to its superior properties. However, there are some problems such as serious tool wear and poor surface quality in the cutting process of GH4169. In this study, in order to improve the machinability of GH4169, GnP-ZrO2 hybrid nanofluids (NFs) were prepared and applied to cutting machining. Thermophysical properties of the prepared hybrid NFs such as dynamic viscosity, thermal conductivity, and contact angle were tested. Meanwhile, a series of wear tests and turning processes of GH4169 under Dry, MQL, GnPNMQL (GNMQL), GnP-ZrO2 hybrid NMQL (GZNMQL) conditions were conducted to evaluate the performance of NFs. The results showed that the thermophysical property of NFs was improved and the coefficient of friction was significantly reduced with the addition of GnP and ZrO2 hybrid nanoparticles. The machined surface roughness and cutting temperatures under the GZNMQL cooling condition were reduced by 53.53% and 54.06% compared to those under the Dry condition, respectively. Additionally, the tool life was significantly improved. The "flake-spheres-flake" structure of the GnP and ZrO2 improved the lubricity, cooling effect, and friction properties of the cutting fluids, which resulted in a better performance in terms of machined surface roughness, cutting temperatures, and further improved tool life. [ABSTRACT FROM AUTHOR]

Polyether-ether-ketone (PEEK) has been widely applied in various fields due to its excellent mechanical properties and biocompatibility. The efficient and high-quality customized manufacturing of PEEK components are investigated in this study by the hybrid 3D printing and milling process. At first, the alternating hybrid process is selected and verified by comparing two typical hybrid process categories and conducting experiments, respectively. Second, a set of procedures are designed to automate the engineering application of the hybrid process trying to avoid the disadvantages of manual programing. Then, considering the tool length and possible interferences during the hybrid process, a model segmentation algorithm, namely, the exchange principle of avoiding interference (EPAI) is proposed. Based on the introduced EPAI and the programing language Python, the additive and subtractive hybrid manufacturing (ASHM) data processing procedure is proposed and realized by post-processing of the conventional 3D printing codes. Finally, the feasibility experiments have been conducted. The experimental results verify the hybrid manufacturing process in the fabrication of parts with complex internal features. The surface roughness R a and dimensional error L of the parts have been reduced by 75.5% and 85.2%, respectively, while the shear strength τ has been increased by 14.1%. Compared with conventional milling process, the material consumption is reduced by 48.7%. [ABSTRACT FROM AUTHOR]

We present a novel method for detecting red tide (Karenia brevis) blooms off the west coast of Florida, driven by a neural network classifier that combines remote sensing data with spatiotemporally distributed in situ sample data. The network detects blooms over a 1‐km grid, using seven ocean color features from the MODIS‐Aqua satellite platform (2002–2021) and in situ sample data collected by the Florida Fish and Wildlife Conservation Commission and its partners. Model performance was demonstrably enhanced by two key innovations: depth normalization of satellite features and encoding of an in situ feature. The satellite features were normalized to adjust for depth‐dependent bottom reflection effects in shallow coastal waters. The in situ data were used to engineer a feature that contextualizes recent nearby ground truth of K. brevis concentrations through a K‐nearest neighbor spatiotemporal proximity weighting scheme. A rigorous experimental comparison revealed that our model outperforms existing remote detection methods presented in the literature and applied in practice. This classifier has strong potential to be operationalized to support more efficient monitoring and mitigation of future blooms, more accurate communication about their spatial extent and distribution, and a deeper scientific understanding of bloom dynamics, transport, drivers, and impacts in the region. This approach also has the potential to be adapted for the detection of other algal blooms in coastal waters. Integr Environ Assess Manag 2024;20:1432–1446. © 2024 SETAC Key Points: We developed a novel machine learning‐based method for detecting red tide (Karenia brevis) blooms off the Florida Gulf Coast at a 1‐km spatial resolution.We present a quantitative comparison of state‐of‐the‐art satellite methods for detecting K. brevis.The use of both satellite data as well as recent in situ samples produces better model prediction than either individually. [ABSTRACT FROM AUTHOR]

Microscale parts made of Polyether-Ether-Ketone (PEEK) are widely used in biomedical, aerospace, and manufacturing industries. The formation mechanism of the chip and machining quality of PEEK are closely related during the micromilling process. However, they have not been sufficiently studied yet. Therefore, the chip shape during the micromilling process is investigated in this research to gain an in-depth understanding of the micromilling process of PEEK. Firstly, a finite-element simulation model is constructed and used to investigate the relationships of feed speed in relation to strain rate and chip shape. Secondly, the chip shape is classified from the experimental results and process intervals are explored corresponding to each kind of chip shape. Then, the relationships between the chip shape and evaluation indexes (milling force F, maximum cutting temperature T max, surface roughness R a) are investigated. It turns out that four main types of chip shapes are produced during the micromilling process of PEEK, namely, the flake chip, the ribbon chip, the curl chip, and the broken chip. Different kinds of chip shapes can be obtained by controlling the process parameters during the micromilling process. The stability of the cutting process can be judged and the machining quality can be initially discerned based on the chip shapes. [ABSTRACT FROM AUTHOR]

Staple yarn is the basic element of most textiles, and its strength affects the performance and processing efficiency of the final textile products. Meanwhile, its strength is the combination of the resistance to breakage and slip of fibers in the yarn. In this paper, a computational model is proposed to predict the strength of staple yarn. The resistance to breakage and slip of each fiber were first computed based on the random arrangement of the fibers and the idealized yarn structure, and the strength was computed by identifying fiber breakage or slip in the yarn breakage zone. The reliability and applicability of the model were verified on viscose and polyester ring-spun staple yarns. The results show that the mean error of prediction is mostly less than 5%, indicating that the model has good accuracy and applicability; and the correlation coefficient R is mostly greater than 0.95, indicating that the predicted strength is in good agreement with the tested strength. [ABSTRACT FROM AUTHOR]