Degree Type

Thesis

Date of Award

2017

Degree Name

Master of Science

Department

Industrial and Manufacturing Systems Engineering

Major

Industrial and Manufacturing Systems Engineering

First Advisor

Frank Peters

Abstract

This paper defines a methodology to estimate the surface roughness of metal castings by 3D laser scanning. The proposed method applies Principal Component Analysis (PCA) which transforms the point cloud of the casting surface into an orthogonal coordinate system. Using this coordinate system, the Root Mean Square (RMS) deviation of the surface peaks and valleys is estimated. This method is used to analyze the factors affecting point cloud generation and evaluate the technique used to obtain a consistent roughness parameter. A correlation curve was then established by plotting the roughness parameters obtained by PCA method against the corresponding root-mean square (RMS) readings on the cast micro finish comparator. Surface roughness measurements is performed on SCRATA ‘A’ plates and independent casting surfaces; whose roughness is previously unknown; is measured and the results are found to be consistent with the roughness values of the known cast micro finish comparator. The results from the surface comparators and areas of the scanned castings are also validated using a laser interferometer. The proposed method provides a fast, accurate and automated way of calculating surface roughness from the point cloud data. Its repeatability and versatility compares favorably with existing methods and would aid process control and standard interpretation.

DOI

https://doi.org/10.31274/etd-180810-5928

Copyright Owner

Jaipravin Vijayarangan

Language

en

File Format

application/pdf

File Size

74 pages

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