Characterization of Surface Defects in Fast Tool Servo Machining of Microlens Array Using a Pattern Recognition and Analysis Method

X. Q. Jiang, C. F. Cheung, K. Hu, L. B. Kong

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

Microlens array (MLA) is a type of structured freeform surfaces which are widely used in advanced optical products. Fast tool servo (FTS) machining provides an indispensible solution for machining MLA with superior surface quality than traditional fabrication process for MLA. However, there are a lot of challenges in the characterization of the surface defects in FTS machining of MLA. This paper presents a pattern recognition and analysis method (PRAM) for the characterization of surface defects in FTS machining of MLA. The PRAM makes use of the Gabor filters to extract the features from the MLA. These features are used to train a Support Vector Machine (SVM) classifier for defects detection and analysis. To verify the method, a series of experiments have been conducted and the results show that the PRAM produces good accuracy of defects detection using different features and different classifiers. The successful development of PRAM throws some light on further study of surface characterization of other types of structure freeform surfaces.

Original languageEnglish
Pages (from-to)1240-1249
Number of pages10
JournalMeasurement: Journal of the International Measurement Confederation
Volume43
Issue number9
DOIs
Publication statusPublished - Nov 2010

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Surface defects
surface defects
machining
pattern recognition
Pattern recognition
Machining
Classifiers
classifiers
Gabor filters
Surface properties
Support vector machines
defects
Fabrication
fabrication
Experiments
products
Defect detection

Cite this

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title = "Characterization of Surface Defects in Fast Tool Servo Machining of Microlens Array Using a Pattern Recognition and Analysis Method",
abstract = "Microlens array (MLA) is a type of structured freeform surfaces which are widely used in advanced optical products. Fast tool servo (FTS) machining provides an indispensible solution for machining MLA with superior surface quality than traditional fabrication process for MLA. However, there are a lot of challenges in the characterization of the surface defects in FTS machining of MLA. This paper presents a pattern recognition and analysis method (PRAM) for the characterization of surface defects in FTS machining of MLA. The PRAM makes use of the Gabor filters to extract the features from the MLA. These features are used to train a Support Vector Machine (SVM) classifier for defects detection and analysis. To verify the method, a series of experiments have been conducted and the results show that the PRAM produces good accuracy of defects detection using different features and different classifiers. The successful development of PRAM throws some light on further study of surface characterization of other types of structure freeform surfaces.",
keywords = "Fast Tool Servo, Freeform Optics, Microlens Array, Pattern Recognition, Structured Freeform Surfaces, Surface Characterization, Surface Measurement, Ultra-Precision Machining",
author = "Jiang, {X. Q.} and Cheung, {C. F.} and K. Hu and Kong, {L. B.}",
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AU - Jiang, X. Q.

AU - Cheung, C. F.

AU - Hu, K.

AU - Kong, L. B.

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KW - Freeform Optics

KW - Microlens Array

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KW - Structured Freeform Surfaces

KW - Surface Characterization

KW - Surface Measurement

KW - Ultra-Precision Machining

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