Automatic drill wear measurement using colour image processing and artificial neural network

Urs Bopp; Takao Sajima; Hiromichi Onikura

Automatic drill wear measurement using colour image processing and artificial neural network

Urs Bopp, Takao Sajima, Hiromichi Onikura

Manufacturing Process

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

The corner wear of drills is measured automatically in order to predict end of drill life, using hole quality as criterion. Drilling experiments show a strong correlation between the progress of maximum hole diameter and hole surface roughness R_α over drill life. The proposed measurement system uses colour image processing and an artificial neural network. It can detect the corner wear of a drill accurately and predict the surface roughness R_α of the hole to be drilled with mean and maximum errors of 0.32pm and -1.00μm, respectively. The presence of a built-up edge does not influence the measurement results.

Original language	English
Pages (from-to)	287-292
Number of pages	6
Journal	International Journal of the Japan Society for Precision Engineering
Volume	31
Issue number	4
Publication status	Published - Dec 1997

All Science Journal Classification (ASJC) codes

Engineering(all)

Cite this

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N2 - The corner wear of drills is measured automatically in order to predict end of drill life, using hole quality as criterion. Drilling experiments show a strong correlation between the progress of maximum hole diameter and hole surface roughness Rα over drill life. The proposed measurement system uses colour image processing and an artificial neural network. It can detect the corner wear of a drill accurately and predict the surface roughness Rα of the hole to be drilled with mean and maximum errors of 0.32pm and -1.00μm, respectively. The presence of a built-up edge does not influence the measurement results.

AB - The corner wear of drills is measured automatically in order to predict end of drill life, using hole quality as criterion. Drilling experiments show a strong correlation between the progress of maximum hole diameter and hole surface roughness Rα over drill life. The proposed measurement system uses colour image processing and an artificial neural network. It can detect the corner wear of a drill accurately and predict the surface roughness Rα of the hole to be drilled with mean and maximum errors of 0.32pm and -1.00μm, respectively. The presence of a built-up edge does not influence the measurement results.

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Automatic drill wear measurement using colour image processing and artificial neural network

Abstract

All Science Journal Classification (ASJC) codes

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