Matching Items (3)
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- All Subjects: Feature Fitting
- All Subjects: Tolerance (Engineering)
- All Subjects: Coordinate measuring machines
- Genre: Academic theses
- Creators: Huebner, Kenneth
- Resource Type: Text
Description
The goal of this research project is to develop a DOF (degree of freedom) algebra for entity clusters to support tolerance specification, validation, and tolerance automation. This representation is required to capture the relation between geometric entities, metric constraints and tolerance specification. This research project is a part of an on-going project on creating a bi-level model of GD&T; (Geometric Dimensioning and Tolerancing). This thesis presents the systematic derivation of degree of freedoms of entity clusters corresponding to tolerance classes. The clusters can be datum reference frames (DRFs) or targets. A binary vector representation of degree of freedom and operations for combining them are proposed. An algebraic method is developed by using DOF representation. The ASME Y14.5.1 companion to the Geometric Dimensioning and Tolerancing (GD&T;) standard gives an exhaustive tabulation of active and invariant degrees of freedom (DOF) for Datum Reference Frames (DRF). This algebra is validated by checking it against all cases in the Y14.5.1 tabulation. This algebra allows the derivation of the general rules for tolerance specification and validation. A computer tool is implemented to support GD&T; specification and validation. The computer implementation outputs the geometric and tolerance information in the form of a CTF (Constraint-Tolerance-Feature) file which can be used for tolerance stack analysis.
ContributorsShen, Yadong (Author) / Shah, Jami (Thesis advisor) / Davidson, Joseph (Committee member) / Huebner, Kenneth (Committee member) / Arizona State University (Publisher)
Created2013
Description
Dimensional Metrology is the branch of science that determines length, angular, and geometric relationships within manufactured parts and compares them with required tolerances. The measurements can be made using either manual methods or sampled coordinate metrology (Coordinate measuring machines). Manual measurement methods have been in practice for a long time and are well accepted in the industry, but are slow for the present day manufacturing. On the other hand CMMs are relatively fast, but these methods are not well established yet. The major problem that needs to be addressed is the type of feature fitting algorithm used for evaluating tolerances. In a CMM the use of different feature fitting algorithms on a feature gives different values, and there is no standard that describes the type of feature fitting algorithm to be used for a specific tolerance. Our research is focused on identifying the feature fitting algorithm that is best used for each type of tolerance. Each algorithm is identified as the one to best represent the interpretation of geometric control as defined by the ASME Y14.5 standard and on the manual methods used for the measurement of a specific tolerance type. Using these algorithms normative procedures for CMMs are proposed for verifying tolerances. The proposed normative procedures are implemented as software. Then the procedures are verified by comparing the results from software with that of manual measurements.
To aid this research a library of feature fitting algorithms is developed in parallel. The library consists of least squares, Chebyshev and one sided fits applied on the features of line, plane, circle and cylinder. The proposed normative procedures are useful for evaluating tolerances in CMMs. The results evaluated will be in accordance to the standard. The ambiguity in choosing the algorithms is prevented. The software developed can be used in quality control for inspection purposes.
To aid this research a library of feature fitting algorithms is developed in parallel. The library consists of least squares, Chebyshev and one sided fits applied on the features of line, plane, circle and cylinder. The proposed normative procedures are useful for evaluating tolerances in CMMs. The results evaluated will be in accordance to the standard. The ambiguity in choosing the algorithms is prevented. The software developed can be used in quality control for inspection purposes.
ContributorsVemulapalli, Prabath (Author) / Shah, Jami J. (Thesis advisor) / Davidson, Joseph K. (Committee member) / Takahashi, Timothy (Committee member) / Arizona State University (Publisher)
Created2014
Description
Metal castings are selectively machined-based on dimensional control requirements. To ensure that all the finished surfaces are fully machined, each as-cast part needs to be measured and then adjusted optimally in its fixture. The topics of this thesis address two parts of this process: data translations 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 communicated to the machine shop for performing various machining operations on the metal casting. The data flow must include GD&T specifications along with other special notes that may be required to communicate to the machinist. Current data exchange, among various digital applications, is limited to translation of only CAD geometry via STEP AP203. Therefore, an algorithm is developed in order to 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-up parameters for each part must be adjusted individually. To predictively determine these adjustments, the datum surfaces, and to-be-machined surfaces are scanned individually and the point clouds reduced to feature fits. The scanned data are stored as separate point cloud files. The labels associated with the datum and to-be-machined (TBM) features are extracted from the *.sat file. These labels are further matched with the file name of the point cloud data to identify data for the respective features. The point cloud data and the CAD model are then used to fit the appropriate features (features at maximum material 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 machine the part. The locating method is extracted from a fixture library that specifies the type of fixturing used to machine the part. All entities are transformed from its local coordinate system into the GDRF. The nominal geometry, fitted features, and the GD&T information are then stored in a neutral file format called the Constraint Tolerance Feature (CTF) Graph. The final outputs are then used to identify the locations of the critical features on each part and these are used to establish the adjustments for its setup prior to machining, in another module, not part of this thesis.
ContributorsRamnath, Satchit (Author) / Shah, Jami J. (Thesis advisor) / Davidson, Joseph (Committee member) / Hansford, Dianne (Committee member) / Arizona State University (Publisher)
Created2016