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Author

Brian Perry

Date of Award

9-2010

Degree Type

Thesis

Degree Name

Master of Applied Science (MASc)

Department

Mechanical Engineering

Supervisor

S. C. Veldhuis

Language

English

Abstract

Statistical Process Control (SPC) provides tools to monitor process quality and productivity. When coupled with closed loop control theory, SPC algorithms can be utilized to compensate for various error sources in stable, high volume, discrete part manufacturing processes. These error sources include environmental effects, tool wear, measurement, and material errors.

Closed loop machining cells must be analyzed from both Quality and Manufacturing Engineering perspectives for efficient and successful implementation. Discrete, stochastic, time event manufacturing simulation is used to analyze process organization, data flow and control system performance. SPC and Engineering Process Control (EPC) control algorithms are compared using data gathered from a high volume machining process involving steel turned components with a critical machined surface.

McMaster University Library

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