权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Optimization of correction margin in a CAD/CAM system applying neuro-logic

应用神经逻辑的 CAD/CAM 系统中校正裕度的优化

基本信息

批准号：
11671904
负责人：
TAKASE Yasuaki
金额：
$ 2.24万
依托单位：
TOKYO DENTAL COLLEGE
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1999
资助国家：
日本
起止时间：
1999 至 2001
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-11671904/
关键词：
CAD CAM H-Net Neuro cavity margin three dimensional measurement H-Netニューロ歯頸部マージン

项目摘要

Optimization of correction margin in a CDA/CAM system applying neuro-logicAs a result of comparing the die and the three dimensional point group data obtained from the same sample model using an optical three dimensional coordinate entering equipment, we learned that the measurement accuracy at axial surfaces with a large inclination and at sharp corners is degraded and that the measurement are affected by the measuring steps of the shape measuring equlpment and arrangement of the optical system including illumination and optical receiver devices. While we earned that the application of neuro-logic resulted in higher ability of correcting errors over most of the areas of a smooth surface comparing to the case of moving average, not much difference was observed between the two methods at a section connecting the shoulder type margin and axial surface and at a transitional section from axial surface to the mating surface section, and there were many cases where correction by learning effect could not be recognized. On the other hand, while correction is made with rounded corner in every case under the moving average method, more sharp edges were observed under this method although some errors were included. In curve interpolation using H-NeT, too, there were cases where partial accuracy improvement could not be observed, which indicated a need for further analysis by changing parameters. Furthermore, it is considered that improvements are required in the optical reading device and the reading method.

应用神经逻辑的 CDA/CAM 系统中校正余量的优化通过比较模具和使用光学三维坐标输入设备从同一样品模型获得的三维点组数据，我们了解到，大倾角轴向表面和尖角处的测量精度下降，并且测量受到形状测量设备的测量步骤和布置的影响。光学系统包括照明装置和光接收装置。虽然我们发现，与移动平均的情况相比，神经逻辑的应用在光滑表面的大部分区域上产生了更高的误差校正能力，但在连接肩型边缘和轴表面的部分以及从轴表面到配合表面部分的过渡部分中，两种方法之间没有观察到太大差异，并且在许多情况下无法识别学习效果的校正。另一方面，虽然在移动平均法下每种情况下都用圆角进行校正，但在此方法下观察到更锐利的边缘，尽管包括一些误差。在使用H-NeT进行曲线插值时，也存在无法观察到部分精度提高的情况，这表明需要通过改变参数来进一步分析。此外，认为光学读取装置和读取方法需要改进。