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Article
Publication date: 22 November 2017

Obrad Anicic, Srdjan Jovic, Srdan Tasic, Aleksa Vulovic and Milivoje Jovanovic

This study aims to detect the temperature distribution in the cutting zone during the machining process. Furthermore, temperature influence in the cutting zone on the…

Abstract

Purpose

This study aims to detect the temperature distribution in the cutting zone during the machining process. Furthermore, temperature influence in the cutting zone on the forms of chip shapes during the turning of Steel 30CrNiMo8 was evaluated. It is very important to use optimal machining parameters to get the best production results or for high control of the machining process.

Design/methodology/approach

Temperature distribution in the cutting zone during the machining process could affect the forms of chip shapes. Forms of chip shapes could be considered as the most important indicator for the quality of the machining process.

Findings

Therefore, in this study, the forms of chip shapes based on the temperature distribution in the cutting zone were examined.

Originality/value

It was found that the snarled chip type and the loose chip type have the highest temperature variation during the machining process.

Details

Sensor Review, vol. 38 no. 1
Type: Research Article
ISSN: 0260-2288

Keywords

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Article
Publication date: 16 January 2017

Srdjan Jovic, Obrad Anicic and Milivoje Jovanovic

Acoustic emission (AE) could be used for prevention and detection of tool errors in Computer Numerical Control (CNC) machining. The purpose of this study is to analyze the…

Abstract

Purpose

Acoustic emission (AE) could be used for prevention and detection of tool errors in Computer Numerical Control (CNC) machining. The purpose of this study is to analyze the AE form of CNC machining operations.

Design/methodology/approach

Experimental measurements were performed with three sensors on the CNC lathe to collect the data of the CNC machining. Adaptive neuro-fuzzy inference system (ANFIS) was applied for the fusion from the sensors’ signals to determine the strength of the signal periodic component among the sensors.

Findings

There were three inputs, namely, spindle speed, feed rate and depth of cut. ANFIS was also used to determine the inputs’ influence on the prediction of strength of the signal periodic component. Variable selection process was used to select the most dominant factors which affect the prediction of strength of the signal periodic component.

Originality/value

Results were shown that the spindle speed has the most dominant effect on the strength of the signal periodic component.

Details

Sensor Review, vol. 37 no. 1
Type: Research Article
ISSN: 0260-2288

Keywords

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