TiC particles reinforced Ni-based alloy composite coatings were prepared on 7005 aluminum alloy by plasma spray. The effects of load, speed and temperature on the tribological behavior and mechanisms of the composite ...TiC particles reinforced Ni-based alloy composite coatings were prepared on 7005 aluminum alloy by plasma spray. The effects of load, speed and temperature on the tribological behavior and mechanisms of the composite coatings under dry friction were researched. The wear prediction model of the composite coatings was established based on the least square support vector machine (LS-SVM). The results show that the composite coatings exhibit smaller friction coefficients and wear losses than the Ni-based alloy coatings under different friction conditions. The predicting time of the LS-SVM model is only 12.93%of that of the BP-ANN model, and the predicting accuracies on friction coefficients and wear losses of the former are increased by 58.74%and 41.87%compared with the latter. The LS-SVM model can effectively predict the tribological behavior of the TiCP/Ni-base alloy composite coatings under dry friction.展开更多
University endowment funds are important capitals in many universities in China today. A lot of universities target their endowment spending at a fixed spending ratio. Because of volatile inflation, it is not optimal....University endowment funds are important capitals in many universities in China today. A lot of universities target their endowment spending at a fixed spending ratio. Because of volatile inflation, it is not optimal. In this paper, the author analyzes the importance of spending policy of university endowment funds, and suggests two models in accordance with inflation-adjusted value.展开更多
Machine learning prediction models for thin wire-based metal additive manufacturing(MAM)process were proposed,aiming at the complex relationship between the process parameters and the geometric characteristics of sing...Machine learning prediction models for thin wire-based metal additive manufacturing(MAM)process were proposed,aiming at the complex relationship between the process parameters and the geometric characteristics of single track of the deposition layer and surface roughness.The effects of laser power,wire feeding speed and scanning speed on the width and height of the single track and surface roughness were experimentally studied.The results show that laser power has a significant impact on the width of the single track but little effect on the height.As the wire feeding speed increases,the width and height of the single track increase,especially the height.The faster the scanning speed,the smaller the width of the single track,while the height does not change much.Then,support vector regression(SVR)and artificial neural network(ANN)regression methods were employed to set up prediction models.The SVR and ANN regression models perform well in predicting the width,with a smaller root mean square error and a higher correlation coefficient R2.Compared with the ANN model,the SVR model performs better both in predicting geometric characteristics of single track and surface roughness.Multi-layer thin-walled parts were manufactured to verify the accuracy of the models.展开更多
文摘TiC particles reinforced Ni-based alloy composite coatings were prepared on 7005 aluminum alloy by plasma spray. The effects of load, speed and temperature on the tribological behavior and mechanisms of the composite coatings under dry friction were researched. The wear prediction model of the composite coatings was established based on the least square support vector machine (LS-SVM). The results show that the composite coatings exhibit smaller friction coefficients and wear losses than the Ni-based alloy coatings under different friction conditions. The predicting time of the LS-SVM model is only 12.93%of that of the BP-ANN model, and the predicting accuracies on friction coefficients and wear losses of the former are increased by 58.74%and 41.87%compared with the latter. The LS-SVM model can effectively predict the tribological behavior of the TiCP/Ni-base alloy composite coatings under dry friction.
文摘University endowment funds are important capitals in many universities in China today. A lot of universities target their endowment spending at a fixed spending ratio. Because of volatile inflation, it is not optimal. In this paper, the author analyzes the importance of spending policy of university endowment funds, and suggests two models in accordance with inflation-adjusted value.
基金173 Basic Strengthening ProgramXi'an Science and Technology Plan(21ZCZZHXJS-QCY6-0002)。
文摘Machine learning prediction models for thin wire-based metal additive manufacturing(MAM)process were proposed,aiming at the complex relationship between the process parameters and the geometric characteristics of single track of the deposition layer and surface roughness.The effects of laser power,wire feeding speed and scanning speed on the width and height of the single track and surface roughness were experimentally studied.The results show that laser power has a significant impact on the width of the single track but little effect on the height.As the wire feeding speed increases,the width and height of the single track increase,especially the height.The faster the scanning speed,the smaller the width of the single track,while the height does not change much.Then,support vector regression(SVR)and artificial neural network(ANN)regression methods were employed to set up prediction models.The SVR and ANN regression models perform well in predicting the width,with a smaller root mean square error and a higher correlation coefficient R2.Compared with the ANN model,the SVR model performs better both in predicting geometric characteristics of single track and surface roughness.Multi-layer thin-walled parts were manufactured to verify the accuracy of the models.
