针对金刚石滚轮是一种回转体零件以及其加工制造过程中信息化集成程度低等特点,对金刚石滚轮的特征信息提取、特征加工方案决策、数控程序后置处理等关键技术进行了研究,采用了产品模型数据交换标准STEP AP 242,实现对金刚石滚轮的制造...针对金刚石滚轮是一种回转体零件以及其加工制造过程中信息化集成程度低等特点,对金刚石滚轮的特征信息提取、特征加工方案决策、数控程序后置处理等关键技术进行了研究,采用了产品模型数据交换标准STEP AP 242,实现对金刚石滚轮的制造特征信息提取,使用STEP AP 242和PDM相结合的集成方式,将金刚石滚轮三维模型作为制造加工信息的载体,并选择SolidWorks作为CAD和CAM软件,以及选择Aras Innovator作为PDM平台,使用C#编程语言和数据库技术开发了金刚石滚轮CAD/CAPP/CAM/PDM集成系统,并进行了实例验证。展开更多
Flood disasters pose serious threats to human life and property worldwide.Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts.This study propos...Flood disasters pose serious threats to human life and property worldwide.Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts.This study proposes a comprehensive framework for integrating driving-factor optimization and interpretability,while considering spatial heterogeneity.In this framework,the Optimal Parameter-based Geographic Detector(OPGD),Recursive Feature Estimation(RFE),and Light Gradient Boosting Machine(LGBM)models were utilized to construct the OPGD–RFE–LGBM coupled model to identify the essential driving factors and simulate the spatial distribution of flood disasters.The SHapley Additive ExPlanation(SHAP)interpreter was employed to quantitatively explain the driving mechanisms behind the spatial distribution of flood disasters.Yunnan Province,a typical mountainous and plateau area in Southwest China,was selected to implement the proposed framework and conduct a case study.For this purpose,a flood disaster inventory of 7332 historical events was prepared,and 22 potential driving factors related to precipitation,surface environment,and human activity were initially selected.Results revealed that flood disasters in Yunnan Province exhibit high spatial heterogeneity,with geomorphic zoning accounting for 66.1%of the spatial variation in historical flood disasters.The OPGD–RFE–LGBM coupled model offers clear advantages over a single LGBM in identifying essential driving factors and quantitatively analyzing their impacts.Moreover,the simulation performance shows a slight improvement(a 6%average decrease in RMSE and an average increase of 1%in R2)even with reduced factor data.Factor explanatory analysis indicated that the combination of the essential driving factor sets varied across different subregions;nevertheless,precipitation-related factors,such as precipitation intensity index(SDII),wet days(R10MM),and 5-day maximum precipitation(RX5day),were the main driving factors controlling flood disasters.This study provides a quantitative analytical framework for the spatial drivers of flood disasters at large scales with significant heterogeneity,offering a reference for disaster management authorities in developing macro-strategies for disaster prevention.展开更多
文摘针对金刚石滚轮是一种回转体零件以及其加工制造过程中信息化集成程度低等特点,对金刚石滚轮的特征信息提取、特征加工方案决策、数控程序后置处理等关键技术进行了研究,采用了产品模型数据交换标准STEP AP 242,实现对金刚石滚轮的制造特征信息提取,使用STEP AP 242和PDM相结合的集成方式,将金刚石滚轮三维模型作为制造加工信息的载体,并选择SolidWorks作为CAD和CAM软件,以及选择Aras Innovator作为PDM平台,使用C#编程语言和数据库技术开发了金刚石滚轮CAD/CAPP/CAM/PDM集成系统,并进行了实例验证。
基金the National Key Research and Development Program of China(Grant No.2022YFF1302405)the Yunnan Province Key Research and Development Program(Grant No.202203AC100005)+1 种基金the National Natural Science Foundation of China(Grant No.42061005,42067033)Applied Basic Research Programs of Yunnan Province(Grant No.202101AT070110,202001BB050073).
文摘Flood disasters pose serious threats to human life and property worldwide.Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts.This study proposes a comprehensive framework for integrating driving-factor optimization and interpretability,while considering spatial heterogeneity.In this framework,the Optimal Parameter-based Geographic Detector(OPGD),Recursive Feature Estimation(RFE),and Light Gradient Boosting Machine(LGBM)models were utilized to construct the OPGD–RFE–LGBM coupled model to identify the essential driving factors and simulate the spatial distribution of flood disasters.The SHapley Additive ExPlanation(SHAP)interpreter was employed to quantitatively explain the driving mechanisms behind the spatial distribution of flood disasters.Yunnan Province,a typical mountainous and plateau area in Southwest China,was selected to implement the proposed framework and conduct a case study.For this purpose,a flood disaster inventory of 7332 historical events was prepared,and 22 potential driving factors related to precipitation,surface environment,and human activity were initially selected.Results revealed that flood disasters in Yunnan Province exhibit high spatial heterogeneity,with geomorphic zoning accounting for 66.1%of the spatial variation in historical flood disasters.The OPGD–RFE–LGBM coupled model offers clear advantages over a single LGBM in identifying essential driving factors and quantitatively analyzing their impacts.Moreover,the simulation performance shows a slight improvement(a 6%average decrease in RMSE and an average increase of 1%in R2)even with reduced factor data.Factor explanatory analysis indicated that the combination of the essential driving factor sets varied across different subregions;nevertheless,precipitation-related factors,such as precipitation intensity index(SDII),wet days(R10MM),and 5-day maximum precipitation(RX5day),were the main driving factors controlling flood disasters.This study provides a quantitative analytical framework for the spatial drivers of flood disasters at large scales with significant heterogeneity,offering a reference for disaster management authorities in developing macro-strategies for disaster prevention.
