Abstract

This paper presents a surface inspection prototype of an automatic system for precision ground metallic surfaces, in this case bearing rolls. The surface reflectance properties are modeled and verified with optical experiments. The aim being to determine the optical arrangement for illumination and observation, where the contrast between errors and intact surface is maximized. A new adaptive threshold selection algorithm for segmentation is presented. Additionally, is included an evaluation of a large number of published sequential search algorithms for selection of the best subset of features for the classification with a comparison of their computational requirements. Finally, the results of classification for 540 flaw images are presented.

Highlights

  • In industry, there is an increasing demand for automatic surface inspection systems for quality control of final products

  • (9) Adaptive sequential forward floating selection (ASFFS): this algorithm is similar to the Sequential forward floating selection (SFFS) procedure where the Sequential forward selection (SFS) and the Sequential backward selection (SBS) methods are replaced by their generalized versions GSFS (r) and GSBS (r)

  • This paper presents a prototype for the detection and classification of flaws on bearing rolls

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Summary

INTRODUCTION

There is an increasing demand for automatic surface inspection systems for quality control of final products. In the first step a statistical procedure, which is numerically efficient (see Section 4), is used to locate irregularities in the acquired data These portions of the data are extracted and submitted as subimages for further processing. These images are segmented so as to separate the flaw from the surroundings, so enabling the calculation of characteristic features. EURASIP Journal on Applied Signal Processing oonnl-ilniene aaccSSqquuuuririfsfsaiaitcticeioen on This end, a comparative study of sequential feature selection algorithms with experimental results from this application is presented. The paper closes with a summary and gives a perspective on the future work

PROBLEM STATEMENT
Characteristic of flaws
OPTICAL ARRANGEMENT AND EXPERIMENTAL SETUP
Reflection model
Measurements of surface roughness
Optical measurements
Experimental setup
Practical implementation for the production environment
FLAW DETECTION
CLASSIFICATION
Segmentation
Feature extraction
Feature selection
24 Compactness of the largest segment
Method
Statistical classification
CONCLUSION AND FURTHER WORK
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