关键词：
Porous material;Oxidation resistance;High temperature oxidation;Oxidation mechanism
摘要：
MoAlB possesses the characteristics of both metals and ceramic materials, which has attracted extensive attention due to its excellent high-temperature oxidation resistance. For this reason, porous MoAlB is considered appli-cable to the practice of filtration under harsh environment. In this study, the high-temperature oxidation behavior of porous MoAlB ceramics is systematically studied at the temperatures ranging from 800 to 1100 degrees C. According to the results, the porous MoAlB exhibits good oxidation resistance at a maximum temperature of 1000 degrees C. The oxidation kinetics of porous MoAlB can be divided into three stages, and the estimated activation energies of the three stages are 253.83 kJ & sdot;mol-1, 367.48 kJ & sdot;mol-1 and 317.84 kJ & sdot;mol-1, respectively. In the stable stage at 1000 degrees C, the quadratic mass gain per unit area shows linearity over time, and the oxidation rate of porous MoAlB reaches 37.31 mg2 & sdot;cm-4 & sdot;h-1. As revealed by the analysis of the composition and microstructure of oxide layers, the main components of the oxide layer include MoO3, MoO2, Al2O3, B2O3. With the extension of oxidation time, the content of Al2O3 in the oxide films increases. The average pore size, permeability and open pore porosity of porous MoAlB show a trend of first decreasing and then tending to be stable. In addition, a discussion is conducted on the high-temperature oxidation mechanism of porous MoAlB.
关键词：
Brown rice kernel;crack detection;image processing;model migration;ResNet-18
摘要：
The occurrence of cracks in brown rice kernels has a substantial impact on grain quality. The timely and accurate detection of rice grains with cracks is crucial for enhancing the overall quality and flavor of processed rice. In this study, we developed an optical observation platform and optimized the original ResNet-18 neural network structure to improve the detection and classification of grain cracks. We established image datasets for japonica and indica rice varieties, and employed image augmentation and model migration techniques during training. In addition, we compared the performance of the optimized model with DenseNet-121 and GoogLeNet. The results demonstrate a notable enhancement in crack detection accuracy for japonica, reaching 96%, which is a 3.67% improvement over the original model. Furthermore, we achieved a substantial reduction in average training time, reduced by 58.66%. For indica rice, after model optimization and migration, the accuracy reached 96.67%. It's important to note that the optimized model has limitations and is not suitable for mixed datasets with limited training data. This technology offers the capability to accurately identify and detect cracks in brown rice kernels under visible light conditions, presenting a promising solution for enhancing grain quality during processing.
摘要：
Ce-MnOx composite oxide catalysts with different proportions were prepared using the coprecipitation method, and the CO-removal ability of the catalysts with the tested temperature range of 60-140 degrees C was investigated systematically. The effect of Ce and Mn ratios on the catalytic oxidation performance of CO was investigated using X-ray diffraction (XRD), X-ray energy dispersive spectroscopy (EDS), scanning electron microscopy (SEM), H-2 temperature programmed reduction (H-2-TPR), CO-temperature programmed desorption (CO-TPD), and in situ infrared spectra. The experimental results reveal that under the same test conditions, the CO conversion rate of pure Mn3O4 reaches 95.4% at 170 degrees C. Additionally, at 140 degrees C, the Ce-MnOx series composite oxide catalyst converts CO at a rate of over 96%, outperforming single-phase Mn3O4 in terms of catalytic performance. With the decrement in Ce content, the performance of Ce-MnOx series composite oxide catalysts first increase and then decrease. The Ce MnOx catalyst behaves best when Ce:Mn = 1:1, with a CO conversion rate of 99.96% at 140 degrees C and 91.98% at 100 degrees C.
通讯机构：
[Beihai Wang] C;College of Mechanical Engineering, Wuhan Polytechnic University, Wuhan 430023, China<&wdkj&>Author to whom correspondence should be addressed.
摘要：
This is a case study of cooperative development between a college and a corporation to manufacture a carton-filling machine. Specifically, a green cooperative development method was proposed that would match the college’s design capabilities with the manufacturing capacity of the enterprise. This college–enterprise cooperative development represents an extensive collaboration between industry and academia. This method integrates design for manufacturing (DFM) theory and the integrated computer aided manufacturing definition (IDEF) method to establish the IDEF0 (functional) model of manufacturing knowledge that supports the design process. The model clarifies the specific manufacturing knowledge that enterprises should provide at the conceptual design stage, preliminary design stage and detailed design stage. The forms of communication and timing of knowledge provision needed to optimize development planning and design decisions based on the manufacturing capacity of the enterprise were also determined. Through this method, the college–enterprise cooperative development project (in this case, involving a carton-filling machine) was accomplished with less time, fewer design modifications and fewer parts needing to be reworked. The results show that this method can greatly reduce the run-in period of both parties, improve the efficiency of cooperative development and reduce the cost and waste of prototyping.
摘要：
Agricultural mechanization is crucial in enhancing production efficiency, alleviating labor demands, reducing costs, improving agricultural product quality, and promoting sustainable development. However, wear and tear are inevitable when using agricultural machinery. The failure of critical wear-resistant parts is responsible for over 50% of rural machinery breakdowns. For instance, a domestic combine harvester typically only operates trouble-free for 20 to 30 h, and the service life of a rotary plow knife is approximately 80 h. Investigating the wear performance of key farm machinery components reinforces machinery design and maintenance strategies, extends machinery lifespans, enhances agricultural production efficiency, and advances agrarian sustainability. This paper provides a comprehensive overview of the latest research on the wear resistance of crucial agricultural machinery components. It delves into the factors influencing the wear resistance of these components and explores current effective measures to address wear-related issues. Additionally, it also summarizes the challenges and opportunities in researching the wear performance of key components in agricultural machinery and future development directions.
