Publication Date

2009-01-01

Availability

Open access

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Industrial Engineering (Engineering)

Date of Defense

2009-11-19

First Committee Member

Shihab Asfour - Committee Chair

Second Committee Member

Murat Erkoc - Committee Member

Third Committee Member

Khaled A. Zakaria - Committee Member

Fourth Committee Member

Sohyung Cho - Outside Committee Member

Abstract

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.

Keywords

Octave; Java; Support Vector Machines; Support Vector Machine; Cylindricity; Geometric Tolerancing; Geometric Tolerance; Support Vector Regression

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