Publication Date



Open access

Degree Type


Degree Name

Master of Science (MS)


Industrial Engineering (Engineering)

Date of Defense


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


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.


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