In the age where quick turn around time and high speed manufacturing methods are becoming more important, quality assurance is a consistent bottleneck in production. With the development of cheap and fast computer hardware, it has become viable to use machine vision for the collection of data points from a machined part. The generation of these large sample points have necessitated a need for a comprehensive algorithm that will be able to provide accurate results while being computationally efficient. Current established methods are least-squares (LSQ) and non-linear programming (NLP). The LSQ method is often deemed too inaccurate and is prone to providing bad results, while the NLP method is computationally taxing. A novel method of using support vector regression (SVR) to solve the NP-hard problem of cylindricity of machined parts is proposed. This method was evaluated against LSQ and NLP in both accuracy and CPU processing time. An open-source, user-modifiable programming package was developed to test the model. Analysis of test results show the novel SVR algorithm to be a viable alternative in exploring different methods of cylindricity in real-world manufacturing.
| Identifer | oai:union.ndltd.org:UMIAMI/oai:scholarlyrepository.miami.edu:oa_theses-1232 |
| Date | 01 January 2009 |
| Creators | Lee, Keun Joo |
| Publisher | Scholarly Repository |
| Source Sets | University of Miami |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Open Access Theses |
Page generated in 0.0019 seconds