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

Second Committee Member

Murat Erkoc

Third Committee Member

Khaled A. Zakaria

Fourth Committee Member

Sohyung Cho

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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