针对改型发动机中紧固件的防腐蚀问题进行研究。分析了改型发动机的使用环境,对目前国内外航空装备的腐蚀防护与控制研究成果进行了简要介绍。选取典型材料40Cr Ni Mo A、GH2132、GH738、GH4169和0Cr18Ni9的螺栓、螺母和管路连接件进行...针对改型发动机中紧固件的防腐蚀问题进行研究。分析了改型发动机的使用环境,对目前国内外航空装备的腐蚀防护与控制研究成果进行了简要介绍。选取典型材料40Cr Ni Mo A、GH2132、GH738、GH4169和0Cr18Ni9的螺栓、螺母和管路连接件进行盐雾试验、湿热试验和酸性大气试验以及典型连接件的盐雾试验,分析现有发动机用紧固件的腐蚀防护能力。试验表明,材料、表面质量、表面防护、安装等对紧固件的腐蚀有较大影响,进而从合理选材、结构设计、表面防护与工艺等方面提出了紧固件腐蚀防护与控制的技术要点,为后续紧固件的设计提供技术支持。展开更多
This article presents an innovative approach to automatic rule discovery for data transformation tasks leveraging XGBoost,a machine learning algorithm renowned for its efficiency and performance.The framework proposed...This article presents an innovative approach to automatic rule discovery for data transformation tasks leveraging XGBoost,a machine learning algorithm renowned for its efficiency and performance.The framework proposed herein utilizes the fusion of diversified feature formats,specifically,metadata,textual,and pattern features.The goal is to enhance the system’s ability to discern and generalize transformation rules fromsource to destination formats in varied contexts.Firstly,the article delves into the methodology for extracting these distinct features from raw data and the pre-processing steps undertaken to prepare the data for the model.Subsequent sections expound on the mechanism of feature optimization using Recursive Feature Elimination(RFE)with linear regression,aiming to retain the most contributive features and eliminate redundant or less significant ones.The core of the research revolves around the deployment of the XGBoostmodel for training,using the prepared and optimized feature sets.The article presents a detailed overview of the mathematical model and algorithmic steps behind this procedure.Finally,the process of rule discovery(prediction phase)by the trained XGBoost model is explained,underscoring its role in real-time,automated data transformations.By employingmachine learning and particularly,the XGBoost model in the context of Business Rule Engine(BRE)data transformation,the article underscores a paradigm shift towardsmore scalable,efficient,and less human-dependent data transformation systems.This research opens doors for further exploration into automated rule discovery systems and their applications in various sectors.展开更多
文摘针对改型发动机中紧固件的防腐蚀问题进行研究。分析了改型发动机的使用环境,对目前国内外航空装备的腐蚀防护与控制研究成果进行了简要介绍。选取典型材料40Cr Ni Mo A、GH2132、GH738、GH4169和0Cr18Ni9的螺栓、螺母和管路连接件进行盐雾试验、湿热试验和酸性大气试验以及典型连接件的盐雾试验,分析现有发动机用紧固件的腐蚀防护能力。试验表明,材料、表面质量、表面防护、安装等对紧固件的腐蚀有较大影响,进而从合理选材、结构设计、表面防护与工艺等方面提出了紧固件腐蚀防护与控制的技术要点,为后续紧固件的设计提供技术支持。
文摘This article presents an innovative approach to automatic rule discovery for data transformation tasks leveraging XGBoost,a machine learning algorithm renowned for its efficiency and performance.The framework proposed herein utilizes the fusion of diversified feature formats,specifically,metadata,textual,and pattern features.The goal is to enhance the system’s ability to discern and generalize transformation rules fromsource to destination formats in varied contexts.Firstly,the article delves into the methodology for extracting these distinct features from raw data and the pre-processing steps undertaken to prepare the data for the model.Subsequent sections expound on the mechanism of feature optimization using Recursive Feature Elimination(RFE)with linear regression,aiming to retain the most contributive features and eliminate redundant or less significant ones.The core of the research revolves around the deployment of the XGBoostmodel for training,using the prepared and optimized feature sets.The article presents a detailed overview of the mathematical model and algorithmic steps behind this procedure.Finally,the process of rule discovery(prediction phase)by the trained XGBoost model is explained,underscoring its role in real-time,automated data transformations.By employingmachine learning and particularly,the XGBoost model in the context of Business Rule Engine(BRE)data transformation,the article underscores a paradigm shift towardsmore scalable,efficient,and less human-dependent data transformation systems.This research opens doors for further exploration into automated rule discovery systems and their applications in various sectors.
