Inspection of printed circuit boards using data mining of image features

Author

Swapna Ragini Ayyavari

Abstract

"This thesis focuses on an idea to inspect the defects on a bare PCB and to build a model, which identifies the board type as defective or non-defective and to identify the kind of defect with respect to hole and line, which are the features that define the PCB. The model is based on the Data Mining concepts and different data mining techniques such as Rough sets, Quinlan's C45, Neural networks, ART2 and K-means clustering.--Abstract, page iii.

Department(s)

Computer Science

Degree Name

M.S. in Computer Science

Publisher

University of Missouri--Rolla

Publication Date

Fall 2003

Pagination

xii, 152 pages

Note about bibliography

Includes bibliographical references.

Rights

© 2003 Swapna Ragini Ayyavari, All rights reserved.

Document Type

Thesis - Citation

File Type

text

Language

English

Subject Headings

Data mining
Quality control -- Optical methods
Printed circuits

Thesis Number

T 8415

Print OCLC #

55480944

Link to Catalog Record

Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.

Recommended Citation

Ayyavari, Swapna Ragini, "Inspection of printed circuit boards using data mining of image features" (2003). Masters Theses. 2459.
https://scholarsmine.mst.edu/masters_theses/2459