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The surface quality of milled blade-root grooves in industrial turbine blades significantly influences their mechanical properties. The surface texture reveals the interaction between the tool and the workpiece during the machining process, which plays a key role in determining the surface quality. In addition, there is a significant correlation between acoustic vibration signals and surface texture features. However, current research on surface quality is still relatively limited, and most considers only a single signal. In this paper, 160 sets of industrial field data were collected by multiple sensors to study the surface quality of a blade-root groove. A surface texture feature prediction method based on acoustic vibration signal fusion is proposed to evaluate the surface quality. Fast Fourier transform (FFT) is used to process the signal, and the clean and smooth features are extracted by combining wavelet denoising and multivariate smoothing denoising. At the same time, based on the gray-level co-occurrence matrix, the surface texture image features of different angles of the blade-root groove are extracted to describe the texture features. The fused acoustic vibration signal features are input, and the texture features are output to establish a texture feature prediction model. After predicting the texture features, the surface quality is evaluated by setting a threshold value. The threshold is selected based on all sample data, and the final judgment accuracy is 90%.

Tool wear is a critical factor that directly impacts product performance, making accurate and timely detection essential for ensuring machining quality. In particular, under conditions of shallow cutting depth, tool tip wear significantly exceeds edge wear, yet the detection of tool tip wear has received little attention. Therefore, this paper proposes an image segmentation algorithm for detecting milling cutter tip wear, enabling precise measurement of tool tip wear. Initially, Valley-emphasis method is employed for initial segmentation of ground images to detect and segment the bottom edges. Subsequently, the detected edges serve as masks for parallel computation, achieving precise edge segmentation. Finally, the XOR result of the finely segmented edges and the mask is used to determine the wear region. Compared to existing detection algorithms, this method enhances edge detection accuracy without increasing detection time. The maximum error compared to manual measurement is within 0.007 mm, with a minimum accuracy rate of 97.92%. Additionally, the algorithm’s runtime has been reduced to 15.53 s, a decrease of approximately 94.68%. These results substantiate the efficacy of the proposed approach.

The combined pollution of heavy metals and organic pollutants in water body has become one of vital environmental issues. Herein, a series of BiVO 4 /rGO/g-C 3 N 4 nanocomposites were synthesized for concurrent removals of organic pollutant and heavy metal. Results showed that using the optimized photocatalyst BiVO 4 /rGO/g-C 3 N 4 -28, tetracycline (TC) removal of 87.3% and copper (Cu (II)) removal of 90.6% were achieved under visible-light irradiation within 3 h, respectively; much higher than those using BiVO 4 and g-C 3 N 4 . More importantly, synergistic effect of TC and Cu (II) removals occurred on the surface of BiVO 4 /rGO/g-C 3 N 4 in the TC-Cu (II) coexistence condition. Additionally, the ·OH and ·O 2 ˉ were the most important active species for TC oxidation, while photogenerated electrons were the most responsible for Cu (II) reduction. Results of various characterizations and electron spin resonance test demonstrated that BiVO 4 /rGO/g-C 3 N 4 was a Z-scheme photocatalyst. Based on the identified intermediates, possible degradation pathways and mechanisms for photocatalytic degradation of TC were proposed. This study advances the development and mechanism of photocatalysts for collaborative removal of pollutants.

In the milling process, the wear area of the tool is often segmented using the traditional image processing method to quantify the tool wear value. However, these methods have the disadvantages of having weak anti-noise capabilities and low segmentation accuracy. Although the semantic segmentation network can achieve excellent segmentation accuracy, obtaining enough end mill wear images to support the semantic segmentation network’s training is challenging due to the high acquisition cost of wear images. As a result, this paper suggests a small sample end mill wear area segmentation method based on transfer learning and generative adversarial networks to address the issue of insufficient samples of end mill wear images. In this paper, WGAN is used to generate wear images to expand the dataset with a few samples, and the transfer learning method is used to improve the generalization ability of the segmentation network and finally achieve small sample training. This approach increases mPA by 4.46% and mIOU by 8.97% when compared to merely using the semantic segmentation network for small sample training. According to experimental findings, this method not only has high stability and segmentation accuracy but also solves the problem of insufficient end mill wear image samples. The method proposed in this paper can be effectively applied to the intelligent detection of the tool wear state, improving the accuracy and stability of the measurement of the tool wear value.

Surface micro-texture has been shown to enhance wettability and reduce wear on cutting tools. However, there is limited research on how laser parameters impact the dimensional accuracy of surface texture and its wettability. This study focuses on producing arrayed groove textures on WC/Co cemented carbide surfaces using Nd: YAG laser, evaluating the effect of the laser parameters on surface topography and texture accuracy through microscopic observation and simulation. The results indicate that, with laser parameters such as a number of passes less than 5, approximately 16 W power, scanning speed of 100–150 mm/s, and pulse frequency of 30 kHz, the error between the groove width and laser spot diameter was 4.7%. Additionally, the study explores the impact of the groove texture on surface wettability using the solid droplet method and XPS analysis. Comparative experiments reveal that increased surface roughness enhanced oleophobicity, with surfaces exhibiting high texture accuracy and integrity showing improved oleophobic and spreading properties. Thus, the precise regulation of laser processes is crucial for maintaining surface texture integrity and enhancing surface wettability.

The combined pollution of heavy metals and organic pollutants in water body has become one of vital environmental issues. Herein, a series of BiVO /rGO/g-C N nanocomposites were synthesized for concurrent removals of organic pollutant and heavy metal. Results showed that using the optimized photocatalyst BiVO /rGO/g-C N -28, tetracycline (TC) removal of 87.3% and copper (Cu (II)) removal of 90.6% were achieved under visible-light irradiation within 3 h, respectively; much higher than those using BiVO and g-C N . More importantly, synergistic effect of TC and Cu (II) removals occurred on the surface of BiVO /rGO/g-C N in the TC-Cu (II) coexistence condition. Additionally, the ·OH and ·O - were the most important active species for TC oxidation, while photogenerated electrons were the most responsible for Cu (II) reduction. Results of various characterizations and electron spin resonance test demonstrated that BiVO /rGO/g-C N was a Z-scheme photocatalyst. Based on the identified intermediates, possible degradation pathways and mechanisms for photocatalytic degradation of TC were proposed. This study advances the development and mechanism of photocatalysts for collaborative removal of pollutants.

Water is vital for producing summer maize (SM) and winter wheat (WW); therefore, its proper management is crucial for sustainable farming. This study aimed to develop new tri-band spectral vegetation indices that enhance the accuracy of monitoring plant moisture content (PMC) in SM and WW. We conducted irrigation treatments, including W0, W1, W2, W3, and W4, in SM–WW rotations to address this issue. Canopy reflectance was measured with a field spectroradiometer. Tri-band hyperspectral vegetation indices were constructed: Normalised Water Stress Index (NWSI), Normalised Difference Index (NDI), and Exponential Water Stress Index (EWSI), for assessing the PMC of SM and WW. Results indicate that NWSI outperformed other indices. In the maize trials, the correlation reached R = −0.8369, while in wheat, it reached R = −0.9313, surpassing traditional indices. Four mainstream machine learning models (Random Forest, Partial Least Squares Regression, Support Vector Machine, and Artificial Neural Network) were employed for modelling. NWSI-PLSR exhibited the best index-type performance with an R2 of 0.7878. When the new indices were combined with traditional indices as input data, the NWSI-Published indices-SVM model achieved superior performance with an R2 of 0.8203, outperforming other models. The RF model produced the most consistent performance and achieved the highest average R2 across all input types. The NDI-Published indices models also outperformed those of the published indices alone. This indicates that these new indices improve the accuracy of moisture content monitoring in SM and WW fields. It provides a technical basis and support for precision irrigation, holding significant potential for application.

Drought is a significant challenge to winter wheat production. Its impact can be mitigated by preventing plant moisture stress through precision agriculture. Remote sensing and machine learning have proven effective for managing moisture stress in winter wheat. This study highlights the potential of new indices that combine visible (VIS) and near-infrared (NIR) bands along with canopy temperature (Tc), to monitor plant moisture content (PMC) and leaf moisture content (LMC) in winter wheat under irrigation treatments: W0 (no irrigation), W1 (45–65%), W2 (55–75%), W3 (65–85%), W4 (75–95%) of field capacity, and Z (irrigation and rainfall). Our findings show that the ratio stress index (RSI), with band combinations such as RSI7 (650, 428) , RSI8 (663, 422) , and RSI9 (671, 450) , performs better in tracking PMC and LMC, demonstrating high correlation and improved average prediction metrics for vegetation index (VI) models with R 2 , RMSE, and MAE of 0.838, 2.791, and 2.093 respectively, for LMC and VI-Tc input models with 0.850, 2.731, and 2.105 for PMC. Incorporating Tc into RSI models enhances prediction accuracy, increasing R² by up to 13.82% in the RSI-Tc-SVM-PMC model and decreasing RMSE and MAE by 15.89% and 18.33%, respectively. Therefore, a combination of RSI-Tc-SVM-ANN is recommended to monitor winter wheat moisture stress.

Soil drought impact on irrigation areas is not merely a single reduction in crop yields, but rather a chain reaction that occurs from multiple dimensions including crop growth, water resource allocation, soil environment, operation of irrigation area projects, agricultural economy and ecosystems. The changing trend and mutation characteristics of soil drought are unclear in the People’s Victory Canal Irrigation District (PVCID). The Standardized Soil Moisture Index (SSMI) and the breaks for additive seasons and trend (BFAST) decomposition algorithm were adopted, combined with the eXtreme Gradient Boosting (XGBoost) model, to explore spatio-temporal evolution characteristics, driving factors and response to meteorological drought of soil drought. During the research period, the area percentage of SSMI showing a downward trend was 97.30%. The most severe soil drought occurred in 2019. In addition, the optimal trivariate combination is precipitation, evapotranspiration, and air temperature. This study has clarified the spatio-temporal evolution laws and driving mechanisms of soil drought in the PVCID, providing an important theoretical basis for the early warning, prevention and control of soil drought and the adaptive management of the ecosystem.