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A New Approach to Automatic Detection of Life of Coated Tool Based on Acoustic Emission Measurement

Taro Moriwaki, M. Tobito

Open publisher page 59 citations

Abstract

Characteristic features of acoustic emission (AE) signals are analyzed and measured during turning of medium carbon steel with both coated and uncoated tools. It was found that the AE signal changes from burst-type to continuous-type as the coated tool is worn away and the ceramic coating is removed. The AE signal further changes to one with a large amplitude and variation as the tool approaches termination. A procedure is proposed to classify the AE signal into three categories based on the experimental results and to identify the condition of tool by AE signal measured employing the pattern recognition technique. Further cutting experiments proved that the state of wear and the life of the coated tool can be identified by the method proposed.

About this research paper

What this paper is about

Characteristic features of acoustic emission (AE) signals are analyzed and measured during turning of medium carbon steel with both coated and uncoated tools. It was found that the AE signal changes from burst-type to continuous-type as the coated tool is worn away and the ceramic coating is removed. The AE signal further changes to one with a large amplitude and variation as the tool approaches termination. A procedure is proposed to classify the AE signal into three categories based on the experimental results and to identify the condition of tool by AE signal measured employing the pattern recognition technique. Further cutting experiments proved that the state of wear and the life of the coated tool can be identified by the method proposed.

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OpenAlex reports 59 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Characteristic features of acoustic emission (AE) signals are analyzed and measured during turning of medium carbon steel with both coated and uncoated tools. It was found that the AE signal changes from burst-type to continuous-type as the coated tool is worn away and the ceramic coating is removed. The AE signal further changes to one with a large amplitude and variation as the tool approaches termination. A procedure is proposed to classify the AE signal into three categories based on the experimental results and to identify the condition of tool by AE signal measured employing the pattern recognition technique. Further cutting experiments proved that the state of wear and the life of the coated tool can be identified by the method proposed.

Key concepts: Acoustic emission, SIGNAL (programming language), Coating, Acoustics, Tool wear, Materials science, Ceramic, Computer science

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