采用激光粉末床熔融(laser powder bed fusion,LPBF)技术制备K418B高温合金,利用光学显微镜、扫描电镜和硬度仪分析工艺参数激光功率(140~220 W)和扫描速度(600~1400 mm/s)对显微缺陷、致密度、微观组织及硬度的影响。结果表明,激光功...采用激光粉末床熔融(laser powder bed fusion,LPBF)技术制备K418B高温合金,利用光学显微镜、扫描电镜和硬度仪分析工艺参数激光功率(140~220 W)和扫描速度(600~1400 mm/s)对显微缺陷、致密度、微观组织及硬度的影响。结果表明,激光功率和扫描速度均显著影响样品的相对密度与缺陷分布。低能量密度易产生不规则孔洞,高能量密度则易形成球形气孔与凝固裂纹;体积能量密度(volume energy density,VED)过低或过高都会降低致密度和性能。最佳工艺参数为激光功率180 W、扫描速度1400 mm/s,在该条件下样品致密度可达99.95%以上,表面缺陷少,仅有少量凝固裂纹,显微组织呈明显熔池边界和胞状结构,维氏硬度达366.8HV_(0.2)。微观组织观察显示,熔池边界处晶粒较粗大,内部可见细胞状柱状晶,局部连续跨越多个熔池,表现出快速凝固特征。硬度随VED先升后降,与孔隙含量及致密度变化一致。研究揭示热应力是裂纹产生的主要原因,为K418B合金LPBF成形的参数优化提供依据,对提升航空发动机关键部件制造质量具有工程应用价值。展开更多
The distribution of igneous rocks is closely related to hydrocarbon resources.This study utilized high-precision gravity,magnetic,and rock physical property data,employing gravity-magnetic field fusion technology and ...The distribution of igneous rocks is closely related to hydrocarbon resources.This study utilized high-precision gravity,magnetic,and rock physical property data,employing gravity-magnetic field fusion technology and Euler deconvolution technology.The objective was to identify the distribution of igneous rocks in the China Seas and neighboring regions and investigate their relationships with petroliferous basins.Our results reveal that igneous rocks are widely scattered throughout the China Seas and neighboring regions,with the highest concentration in the northwest(NW)and the second highest concentration in the east-northeast(ENE).The largest-scale igneous rocks are those with a north-south(N-S)orientation,followed by those with northeast(NE),NW,and ENE orientations.The depths of igneous rocks within petroliferous basins typically range from 3 km to 9 km and are associated with hydrocarbon resource distributions characterized by deep oil and shallow gas.The proportions of igneous rocks in different types of basins exhibit varying correlations with the total hydrocarbon resources.In particular,the proportion of igneous rocks in rift-type basins in the China Seas exhibits a strong linear correlation with the total hydrocarbon resources.These research findings provide valuable guidance for studying the relationship between igneous rock distribution and petroliferous basins,offering insights that can inform future hydrocarbon exploration endeavors.展开更多
This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and...This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both classifiers.With BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke predictions.The findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.展开更多
文摘采用激光粉末床熔融(laser powder bed fusion,LPBF)技术制备K418B高温合金,利用光学显微镜、扫描电镜和硬度仪分析工艺参数激光功率(140~220 W)和扫描速度(600~1400 mm/s)对显微缺陷、致密度、微观组织及硬度的影响。结果表明,激光功率和扫描速度均显著影响样品的相对密度与缺陷分布。低能量密度易产生不规则孔洞,高能量密度则易形成球形气孔与凝固裂纹;体积能量密度(volume energy density,VED)过低或过高都会降低致密度和性能。最佳工艺参数为激光功率180 W、扫描速度1400 mm/s,在该条件下样品致密度可达99.95%以上,表面缺陷少,仅有少量凝固裂纹,显微组织呈明显熔池边界和胞状结构,维氏硬度达366.8HV_(0.2)。微观组织观察显示,熔池边界处晶粒较粗大,内部可见细胞状柱状晶,局部连续跨越多个熔池,表现出快速凝固特征。硬度随VED先升后降,与孔隙含量及致密度变化一致。研究揭示热应力是裂纹产生的主要原因,为K418B合金LPBF成形的参数优化提供依据,对提升航空发动机关键部件制造质量具有工程应用价值。
基金The National Key Research and Development Program of China under contract No.2017YFC0602202.
文摘The distribution of igneous rocks is closely related to hydrocarbon resources.This study utilized high-precision gravity,magnetic,and rock physical property data,employing gravity-magnetic field fusion technology and Euler deconvolution technology.The objective was to identify the distribution of igneous rocks in the China Seas and neighboring regions and investigate their relationships with petroliferous basins.Our results reveal that igneous rocks are widely scattered throughout the China Seas and neighboring regions,with the highest concentration in the northwest(NW)and the second highest concentration in the east-northeast(ENE).The largest-scale igneous rocks are those with a north-south(N-S)orientation,followed by those with northeast(NE),NW,and ENE orientations.The depths of igneous rocks within petroliferous basins typically range from 3 km to 9 km and are associated with hydrocarbon resource distributions characterized by deep oil and shallow gas.The proportions of igneous rocks in different types of basins exhibit varying correlations with the total hydrocarbon resources.In particular,the proportion of igneous rocks in rift-type basins in the China Seas exhibits a strong linear correlation with the total hydrocarbon resources.These research findings provide valuable guidance for studying the relationship between igneous rock distribution and petroliferous basins,offering insights that can inform future hydrocarbon exploration endeavors.
基金funded by FCT/MECI through national funds and,when applicable,co-funded EU funds under UID/50008:Instituto de Telecomunicacoes.
文摘This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both classifiers.With BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke predictions.The findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.
