Matching Items (3)
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- All Subjects: Coordinate measuring machines
- Genre: Academic theses
- Creators: Farin, Gerald
- Creators: Ramnath, Satchit
- Resource Type: Text
Description
Conformance of a manufactured feature to the applied geometric tolerances is done by analyzing the point cloud that is measured on the feature. To that end, a geometric feature is fitted to the point cloud and the results are assessed to see whether the fitted feature lies within the specified tolerance limits or not. Coordinate Measuring Machines (CMMs) use feature fitting algorithms that incorporate least square estimates as a basis for obtaining minimum, maximum, and zone fits. However, a comprehensive set of algorithms addressing the fitting procedure (all datums, targets) for every tolerance class is not available. Therefore, a Library of algorithms is developed to aid the process of feature fitting, and tolerance verification. This paper addresses linear, planar, circular, and cylindrical features only. This set of algorithms described conforms to the international Standards for GD&T.; In order to reduce the number of points to be analyzed, and to identify the possible candidate points for linear, circular and planar features, 2D and 3D convex hulls are used. For minimum, maximum, and Chebyshev cylinders, geometric search algorithms are used. Algorithms are divided into three major categories: least square, unconstrained, and constrained fits. Primary datums require one sided unconstrained fits for their verification. Secondary datums require one sided constrained fits for their verification. For size and other tolerance verifications, we require both unconstrained and constrained fits
ContributorsMohan, Prashant (Author) / Shah, Jami (Thesis advisor) / Davidson, Joseph K. (Committee member) / Farin, Gerald (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
Description
The essence of this research is the reconciliation and standardization of feature fitting algorithms used in Coordinate Measuring Machine (CMM) software and the development of Inspection Maps (i-Maps) for representing geometric tolerances in the inspection stage based on these standardized algorithms. The i-Map is a hypothetical point-space that represents the substitute feature evaluated for an actual part in the inspection stage. The first step in this research is to investigate the algorithms used for evaluating substitute features in current CMM software. For this, a survey of feature fitting algorithms available in the literature was performed and then a case study was done to reverse engineer the feature fitting algorithms used in commercial CMM software. The experiments proved that algorithms based on least squares technique are mostly used for GD&T; inspection and this wrong choice of fitting algorithm results in errors and deficiency in the inspection process. Based on the results, a standardization of fitting algorithms is proposed in light of the definition provided in the ASME Y14.5 standard and an interpretation of manual inspection practices. Standardized algorithms for evaluating substitute features from CMM data, consistent with the ASME Y14.5 standard and manual inspection practices for each tolerance type applicable to planar features are developed. Second, these standardized algorithms developed for substitute feature fitting are then used to develop i-Maps for size, orientation and flatness tolerances that apply to their respective feature types. Third, a methodology for Statistical Process Control (SPC) using the I-Maps is proposed by direct fitting of i-Maps into the parent T-Maps. Different methods of computing i-Maps, namely, finding mean, computing the convex hull and principal component analysis are explored. The control limits for the process are derived from inspection samples and a framework for statistical control of the process is developed. This also includes computation of basic SPC and process capability metrics.
ContributorsMani, Neelakantan (Author) / Shah, Jami J. (Thesis advisor) / Davidson, Joseph K. (Committee member) / Farin, Gerald (Committee member) / Arizona State University (Publisher)
Created2011