To improve the wear resistance of aluminum alloy frictional parts, Ti B2particles reinforced Ni-base alloy composite coatings were prepared on aluminum alloy 7005 by laser cladding. The microstructure and tribological...To improve the wear resistance of aluminum alloy frictional parts, Ti B2particles reinforced Ni-base alloy composite coatings were prepared on aluminum alloy 7005 by laser cladding. The microstructure and tribological properties of the composite coatings were investigated. The results show that the composite coating contains the phases of Ni Al, Ni3Al, Al3Ni2, TiB2, TiB, TiC, CrB, and Cr23C6.Its microhardness is HV0.5855.8, which is 15.4 % higher than that of the Ni-base alloy coating and is 6.7 times as high as that of the aluminum alloy. The friction coefficients of the composite coatings are reduced by 6.8 %–21.6 % and 13.2 %–32.4 % compared with those of the Ni-base alloy coatings and the aluminum alloys, while the wear losses are 27.4 %–43.2 % less than those of the Ni-base alloy coatings and are only 16.5 %–32.7 % of those of the aluminum alloys at different loads. At the light loads ranging from 3 to 6 N, the calculated maximum contact stress is smaller than the elastic limit contact stress. The wear mechanism of the composite coatings is micro-cutting wear, but changes into multi-plastic deformation wear at 9 N due to the higher calculated maximum contact stress than the elastic limit contact stress. As the loads increase to 12 N, the calculated flash temperature rises to 332.1 °C.The composite coating experiences multi-plastic deformation wear, micro-brittle fracture wear, and oxidative wear.展开更多
Rock bursts represent a formidable challenge in underground engineering,posing substantial risks to both infrastructure and human safety.These sudden and violent failures of rock masses are characterized by the rapid ...Rock bursts represent a formidable challenge in underground engineering,posing substantial risks to both infrastructure and human safety.These sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock,leading to severe seismic events and structural damage.Therefore,the development of reliable prediction models for rock bursts is paramount to mitigating these hazards.This study aims to propose a tree-based model—a Light Gradient Boosting Machine(LightGBM)—to predict the intensity of rock bursts in underground engineering.322 actual rock burst cases are collected to constitute an exhaustive rock burst dataset,which serves to train the LightGBMmodel.Two population-basedmetaheuristic algorithms are used to optimize the hyperparameters of the LightGBM model.Finally,the sensitivity analysis is used to identify the predominant factors that may incur the occurrence of rock bursts.The results show that the population-based metaheuristic algorithms have a good ability to search out the optimal hyperparameters of the LightGBM model.The developed LightGBM model yields promising performance in predicting the intensity of rock bursts,with which accuracy on training and testing sets are 0.972 and 0.944,respectively.The sensitivity analysis discloses that the risk of occurring rock burst is significantly sensitive to three factors:uniaxial compressive strength(σc),stress concentration factor(SCF),and elastic strain energy index(Wet).Moreover,this study clarifies the particular impact of these three factors on the intensity of rock bursts through the partial dependence plot.展开更多
Landslides are one of the most common geological hazards worldwide,especially in Sichuan Province(Southwest China).The current study's main,purposes are to explore the potential applications of convolutional neura...Landslides are one of the most common geological hazards worldwide,especially in Sichuan Province(Southwest China).The current study's main,purposes are to explore the potential applications of convolutional neural networks(CNN)hybrid ensemble metaheuristic optimization algorithms,namely beluga whale optimization(BWO)and coati optimization algorithm(COA),for landslide susceptibility mapping in Sichuan Province(China).For this aim,fourteen landslide conditioning factors were compiled in a spatial database.The effectiveness of the conditioning factors in the development of the landslide predictive model was quantified using the linear support vector machine model.The receiver operating characteristic(ROC)curve(AUC),the root mean square error,and six statistical indices were used to test and compare the three resultant models.For the training dataset,the AUC values of the CNN-COA,CNN-BWO and CNN models were 0.946,0.937 and 0.855,respectively.In terms of the validation dataset,the CNN-COA model exhibited a higher AUC value of 0.919,while the AUC values of the CNN-BWO and CNN models were 0.906 and 0.805,respectively.The results indicate that the CNN-COA model,followed by the CNN-BWO model,and the CNN model,offers the best overall performance for landslide susceptibility analysis.展开更多
从材料保护的角度出发,在分析了Nd Fe B永磁材料的氢脆过程及氢脆的特点后,用RF磁控溅射制备一定厚度的Al薄膜并在一定条件下进行氧化处理,得到了Al+Al2O3复合涂层。用SEM和XRD分析了涂层形貌和组成,并用高压气相充氢的方式测试了涂层...从材料保护的角度出发,在分析了Nd Fe B永磁材料的氢脆过程及氢脆的特点后,用RF磁控溅射制备一定厚度的Al薄膜并在一定条件下进行氧化处理,得到了Al+Al2O3复合涂层。用SEM和XRD分析了涂层形貌和组成,并用高压气相充氢的方式测试了涂层的阻氢性能。研究表明,厚度为8.0μm复合涂层的阻氢性能为:在10MPa的H2环境中(25℃),阻氢时间达65min,且对磁体的磁性能无不良影响。展开更多
基金financially supported by the Research Program of General Armament Department of China (No. 2012500)
文摘To improve the wear resistance of aluminum alloy frictional parts, Ti B2particles reinforced Ni-base alloy composite coatings were prepared on aluminum alloy 7005 by laser cladding. The microstructure and tribological properties of the composite coatings were investigated. The results show that the composite coating contains the phases of Ni Al, Ni3Al, Al3Ni2, TiB2, TiB, TiC, CrB, and Cr23C6.Its microhardness is HV0.5855.8, which is 15.4 % higher than that of the Ni-base alloy coating and is 6.7 times as high as that of the aluminum alloy. The friction coefficients of the composite coatings are reduced by 6.8 %–21.6 % and 13.2 %–32.4 % compared with those of the Ni-base alloy coatings and the aluminum alloys, while the wear losses are 27.4 %–43.2 % less than those of the Ni-base alloy coatings and are only 16.5 %–32.7 % of those of the aluminum alloys at different loads. At the light loads ranging from 3 to 6 N, the calculated maximum contact stress is smaller than the elastic limit contact stress. The wear mechanism of the composite coatings is micro-cutting wear, but changes into multi-plastic deformation wear at 9 N due to the higher calculated maximum contact stress than the elastic limit contact stress. As the loads increase to 12 N, the calculated flash temperature rises to 332.1 °C.The composite coating experiences multi-plastic deformation wear, micro-brittle fracture wear, and oxidative wear.
文摘Rock bursts represent a formidable challenge in underground engineering,posing substantial risks to both infrastructure and human safety.These sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock,leading to severe seismic events and structural damage.Therefore,the development of reliable prediction models for rock bursts is paramount to mitigating these hazards.This study aims to propose a tree-based model—a Light Gradient Boosting Machine(LightGBM)—to predict the intensity of rock bursts in underground engineering.322 actual rock burst cases are collected to constitute an exhaustive rock burst dataset,which serves to train the LightGBMmodel.Two population-basedmetaheuristic algorithms are used to optimize the hyperparameters of the LightGBM model.Finally,the sensitivity analysis is used to identify the predominant factors that may incur the occurrence of rock bursts.The results show that the population-based metaheuristic algorithms have a good ability to search out the optimal hyperparameters of the LightGBM model.The developed LightGBM model yields promising performance in predicting the intensity of rock bursts,with which accuracy on training and testing sets are 0.972 and 0.944,respectively.The sensitivity analysis discloses that the risk of occurring rock burst is significantly sensitive to three factors:uniaxial compressive strength(σc),stress concentration factor(SCF),and elastic strain energy index(Wet).Moreover,this study clarifies the particular impact of these three factors on the intensity of rock bursts through the partial dependence plot.
基金supported by China Postdoctoral Science Foundation:[grant number 2020M680583]National Natural Science Foundation of China[grant number 52208359]+1 种基金National Natural Science Foundation of China:[grant number 52109125]National Postdoctoral Program for Innovative Talents:[grant number BX20200191].
文摘Landslides are one of the most common geological hazards worldwide,especially in Sichuan Province(Southwest China).The current study's main,purposes are to explore the potential applications of convolutional neural networks(CNN)hybrid ensemble metaheuristic optimization algorithms,namely beluga whale optimization(BWO)and coati optimization algorithm(COA),for landslide susceptibility mapping in Sichuan Province(China).For this aim,fourteen landslide conditioning factors were compiled in a spatial database.The effectiveness of the conditioning factors in the development of the landslide predictive model was quantified using the linear support vector machine model.The receiver operating characteristic(ROC)curve(AUC),the root mean square error,and six statistical indices were used to test and compare the three resultant models.For the training dataset,the AUC values of the CNN-COA,CNN-BWO and CNN models were 0.946,0.937 and 0.855,respectively.In terms of the validation dataset,the CNN-COA model exhibited a higher AUC value of 0.919,while the AUC values of the CNN-BWO and CNN models were 0.906 and 0.805,respectively.The results indicate that the CNN-COA model,followed by the CNN-BWO model,and the CNN model,offers the best overall performance for landslide susceptibility analysis.
文摘针对碳排放价格预测精度欠佳的问题,提出一种基于完全集成经验模态分解(Complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和浣熊算法(Coati optimization algorithm,COA)优化的双向长短期记忆网络(Bidirectional long short term memory network,BiLSTM)碳价预测模型。首先,采用CEEMDAN将原始碳价数据分解为若干内涵模态分量(Intrinsic mode functions,IMF);然后,运用COA对BiLSTM的参数进行优化;最后,使用COA-BiLSTM模型对IMF进行预测,将IMF预测值重构得到碳价预测值。实证结果表明,采用CEEMDAN对原始数据进行分解,使用BiLSTM模型进行预测,运用COA对预测模型参数进行优化均能有效提高碳价预测精度。
文摘从材料保护的角度出发,在分析了Nd Fe B永磁材料的氢脆过程及氢脆的特点后,用RF磁控溅射制备一定厚度的Al薄膜并在一定条件下进行氧化处理,得到了Al+Al2O3复合涂层。用SEM和XRD分析了涂层形貌和组成,并用高压气相充氢的方式测试了涂层的阻氢性能。研究表明,厚度为8.0μm复合涂层的阻氢性能为:在10MPa的H2环境中(25℃),阻氢时间达65min,且对磁体的磁性能无不良影响。
