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[转载]【计算机科学】【2016.07】基于点云数据和CAD模型的夹具自动设置

已有 1151 次阅读 2021-1-15 19:01 |系统分类:科研笔记|文章来源:转载

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本文为美国亚利桑那州立大学(作者:Satchit Ramnath)的硕士论文,共85页。

 

根据尺寸控制要求对金属铸件进行选择性加工。为了确保所有表面都经过充分加工,每个铸态零件都需要进行测量,然后在其夹具中进行最佳调整。本论文的主题是处理这一过程的两个部分:数据转换在每个铸造零件上测量点的特征拟合云。首先,需要将成品零件的CAD模型下达给机器车间,以便在金属铸件上执行各种加工操作。数据流必须包括GD&T规范以及与机械师沟通可能需要的其他特殊注释。在各种数字应用程序之间的当前数据交换仅限于通过步骤AP203转换CAD几何图形。

 

因此,开发了一种算法来读取、存储和转换CAD文件(例如SolidWorks、CREO)中的数据,并将其转换为标准的机器可读格式(ACIS格式-*.sat)。其次,铸件的几何结构因零件而异,因此每个零件的夹具设置参数必须单独调整。为了预测性地确定这些调整,分别扫描基准面和待加工面,并将点云简化为特征拟合。扫描数据存储为单独的点云文件。与基准和待加工(TBM)特征相关的标签从*.sat文件中提取。这些标签与点云数据的文件名进一步匹配,以标识各个要素的数据。然后使用点云数据和CAD模型,用现有的标准特征拟合(nFF)算法拟合适当的特征(MMC和LMC)。特征拟合完成后,基于定位方法构造一个全局基准参考框架(GDRF)用于加工零件。定位方法从指定用于加工零件的夹具类型的夹具库中提取。所有实体从其局部坐标系转换为GDRF。然后,标称几何图形、拟合特征和GD&T信息存储在称为约束公差特征(CTF)图的中性文件格式中。最后的输出用于识别每个零件上关键特征的位置,并在另一个模块(不是本文的一部分)中用于确定加工前的设置调整。

 

Metal castings are selectively machined-based on dimensionalcontrol requirements. To ensure that all the finished surfaces are fullymachined, each as-cast part needs to be measured and then adjusted optimally inits fixture. The topics of this thesis address two parts of this process: datatranslations and feature-fitting clouds of points measured on each cast part.For the first, a CAD model of the finished part is required to be communicatedto the machine shop for performing various machining operations on the metalcasting. The data flow must include GD&T specifications along with otherspecial notes that may be required to communicate to the machinist. Currentdata exchange, among various digital applications, is limited to translation ofonly CAD geometry via STEP AP203. Therefore, an algorithm is developed in orderto read, store and translate the data from a CAD file (for example SolidWorks,CREO) to a standard and machine readable format (ACIS format -*.sat). Second,the geometry of cast parts varies from piece to piece and hence fixture set-upparameters for each part must be adjusted individually. To predictivelydetermine these adjustments, the datum surfaces, and to-be-machined surfacesare scanned individually and the point clouds reduced to feature fits. Thescanned data are stored as separate point cloud files. The labels associatedwith the datum and to-be-machined (TBM) features are extracted from the *.satfile. These labels are further matched with the file name of the point clouddata to identify data for the respective features. The point cloud data and theCAD model are then used to fit the appropriate features (features at maximummaterial condition (MMC) for datums and features at least material condition(LMC) for TBM features) using the existing normative feature fitting (nFF)algorithm. Once the feature fitting is complete, a global datum reference frame(GDRF) is constructed based on the locating method that will be used to machinethe part. The locating method is extracted from a fixture library thatspecifies the type of fixturing used to machine the part. All entities aretransformed from its local coordinate system into the GDRF. The nominalgeometry, fitted features, and the GD&T information are then stored in a neutralfile format called the Constraint Tolerance Feature (CTF) Graph. The finaloutputs are then used to identify the locations of the critical features oneach part and these are used to establish the adjustments for its setup priorto machining, in another module, not part of this thesis.

 

1.引言

2. 文献回顾

3. 概念设计

4. 算法与实现

5. 设计验证

6. 结论


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