Sheet metal is widely used on auto-bodies, plane-bodies and metal furniture, etc. For instance, a typical auto-body commonly consists of hundreds of sheet metal stamping parts. Because of its complexity of structure a...Sheet metal is widely used on auto-bodies, plane-bodies and metal furniture, etc. For instance, a typical auto-body commonly consists of hundreds of sheet metal stamping parts. Because of its complexity of structure and manufacturing process, auto-bodies inevitably have geometrical variation results from a number of different sources, such as the geometrical variation of stamping parts, the transformation of assembly process parameters and even the improper design concept. As more than 30% quality defects of an auto-body are born from the dimensional deviation of Body-In-White originated during the manufacturing process, effective diagnosis and control of dimensional faults are essential to the continuous improvement of the quality of vehicles. Especially during the period of new car launching or model changing when the assembly process was changed and adjusted frequently. For continuously improving the quality of modern cars, rapid dimensional variation causes identification becomes a challenging but essential work. In this paper, main variation causes of auto-body was firstly been cataloged and analyzed, then, a dimensional variation diagnostic reasoning and decision approach was developed through the combination of data mining and knowledge discovery techniques. This approach is driven by variation pattern identification which can be discovered from the dispersive, isolated massive measured data: Correlation Analysis (CA) and Maximal Tree (MT) methods were applied to extract the large variation group from massive multidimensional measured data, while multivariate statistical analysis (MSA) approach was used to discovery the principle variation pattern. A Decision Tree (DT) approach based on the knowledge of product and assembly process was developed to fulfill the "Hypothesis and Validation" characterized variation causes reasoning procedure. An practical application case with sudden and severe dimension variation on rear end panel in up/down direction was analyzed and successfully solved aided by the devloped variation diagnostic method, which have proved that the approach is effective and efficient.展开更多
湘北常澧山地-丘陵地区地理地质环境复杂,滑坡地质灾害点多、面广、零散、频发,是造成人员伤亡和经济损失最主要的地质灾害类型。InSAR、光学遥感、LiDAR、GIS多源遥感综合技术,是目前可行性高、精度良好的滑坡地灾隐患识别和监测技术方...湘北常澧山地-丘陵地区地理地质环境复杂,滑坡地质灾害点多、面广、零散、频发,是造成人员伤亡和经济损失最主要的地质灾害类型。InSAR、光学遥感、LiDAR、GIS多源遥感综合技术,是目前可行性高、精度良好的滑坡地灾隐患识别和监测技术方法,能够满足宏观大范围、时效性等要求。该文基于InSAR形变速率数据、多光谱影像和DEM数据对湖南常澧地区的滑坡地灾隐患进行了识别和提取:首先用2种决策树分类方法对多光谱图像进行了土地利用分类,以便于观察研究区的用地类别及分布情况;然后运用DEM数据提取了高程、坡度、坡向、起伏度和曲率等5项地形地貌因子对研究区进行了滑坡危险性评价;再基于SBAS-InSAR技术对研究区进行地表时序微形变测量;最后在GIS系统内综合危险性评价结果和形变速率对研究区滑坡隐患进行提取和圈定,并基于CART决策树分类结果和研究区水系分布情况,对研究区内除圈定的滑坡隐患点以外的形变速率大于-0.01 m/a的区域进行了危险性推断。本次研究在植被覆盖区和裸露区识别出了数处隐蔽性高、规模小的滑坡隐患,并圈定了滑坡隐患的空间分布范围,面积0.126 km 2,证明了技术方法的有效性,具有一定的实践应用价值。展开更多
基金supported by the National Natural Science Foundation of China(6137301761402241+4 种基金615722606157226161472192)the Scientific&Technological Support Project of Jiangsu Province(BE2014718BE2015702)
文摘Sheet metal is widely used on auto-bodies, plane-bodies and metal furniture, etc. For instance, a typical auto-body commonly consists of hundreds of sheet metal stamping parts. Because of its complexity of structure and manufacturing process, auto-bodies inevitably have geometrical variation results from a number of different sources, such as the geometrical variation of stamping parts, the transformation of assembly process parameters and even the improper design concept. As more than 30% quality defects of an auto-body are born from the dimensional deviation of Body-In-White originated during the manufacturing process, effective diagnosis and control of dimensional faults are essential to the continuous improvement of the quality of vehicles. Especially during the period of new car launching or model changing when the assembly process was changed and adjusted frequently. For continuously improving the quality of modern cars, rapid dimensional variation causes identification becomes a challenging but essential work. In this paper, main variation causes of auto-body was firstly been cataloged and analyzed, then, a dimensional variation diagnostic reasoning and decision approach was developed through the combination of data mining and knowledge discovery techniques. This approach is driven by variation pattern identification which can be discovered from the dispersive, isolated massive measured data: Correlation Analysis (CA) and Maximal Tree (MT) methods were applied to extract the large variation group from massive multidimensional measured data, while multivariate statistical analysis (MSA) approach was used to discovery the principle variation pattern. A Decision Tree (DT) approach based on the knowledge of product and assembly process was developed to fulfill the "Hypothesis and Validation" characterized variation causes reasoning procedure. An practical application case with sudden and severe dimension variation on rear end panel in up/down direction was analyzed and successfully solved aided by the devloped variation diagnostic method, which have proved that the approach is effective and efficient.
文摘湘北常澧山地-丘陵地区地理地质环境复杂,滑坡地质灾害点多、面广、零散、频发,是造成人员伤亡和经济损失最主要的地质灾害类型。InSAR、光学遥感、LiDAR、GIS多源遥感综合技术,是目前可行性高、精度良好的滑坡地灾隐患识别和监测技术方法,能够满足宏观大范围、时效性等要求。该文基于InSAR形变速率数据、多光谱影像和DEM数据对湖南常澧地区的滑坡地灾隐患进行了识别和提取:首先用2种决策树分类方法对多光谱图像进行了土地利用分类,以便于观察研究区的用地类别及分布情况;然后运用DEM数据提取了高程、坡度、坡向、起伏度和曲率等5项地形地貌因子对研究区进行了滑坡危险性评价;再基于SBAS-InSAR技术对研究区进行地表时序微形变测量;最后在GIS系统内综合危险性评价结果和形变速率对研究区滑坡隐患进行提取和圈定,并基于CART决策树分类结果和研究区水系分布情况,对研究区内除圈定的滑坡隐患点以外的形变速率大于-0.01 m/a的区域进行了危险性推断。本次研究在植被覆盖区和裸露区识别出了数处隐蔽性高、规模小的滑坡隐患,并圈定了滑坡隐患的空间分布范围,面积0.126 km 2,证明了技术方法的有效性,具有一定的实践应用价值。
