<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In various machining processes, the vibration signals are studied for tool condition monitoring often referred as wear monitoring. It is essential to overcome unpredicted machining trouble and to improvise the efficiency of the machine. Tool wear is a vital problem in materials such as nickel based alloys as they have high hardness ranges. Though they have high hardness, a nickel based alloy Inconel 718 with varying HRC (51, 53, and 55), is opted as work material for hard turning process in this work. Uncoated carbide, coated carbide and ceramic tools are employed as cutting tools. Taguchi’s L9 orthogonal array is considered by taking hardness, speed, feed and depth of cut as four input parameters, the number of experiments and the combinations of parameters for every run is obtained. The vibration signals are recorded at various stages of cutting, till the tool failure is observed. Taking this vibration signal data as input to ANOVA and Grey relation analysis (GRA) which categorizes the optimal and utmost dominant features such as Root Mean Square (RMS), Crest Factor (CF), Skewness (Sk), Kurtosis (Ku), Absolute Deviation (AD), Mean, Standard Deviation (SD), Variance, peak, Frequency and Time in the tool wear process.</p></abstract>

In various machining processes, the vibration signals are studied for tool condition monitoring often referred as wear monitoring. It is essential to overcome unpredicted machining trouble and to improvise the efficiency of the machine. Tool wear is a vital problem in materials such as nickel based alloys as they have high hardness ranges. Though they have high hardness, a nickel based alloy Inconel 718 with varying HRC (51, 53, and 55), is opted as work material for hard turning process in this work. Uncoated carbide, coated carbide and ceramic tools are employed as cutting tools. Taguchi’s L9 orthogonal array is considered by taking hardness, speed, feed and depth of cut as four input parameters, the number of experiments and the combinations of parameters for every run is obtained. The vibration signals are recorded at various stages of cutting, till the tool failure is observed. Taking this vibration signal data as input to ANOVA and Grey relation analysis (GRA) which categorizes the optimal and utmost dominant features such as Root Mean Square (RMS), Crest Factor (CF), Skewness (Sk), Kurtosis (Ku), Absolute Deviation (AD), Mean, Standard Deviation (SD), Variance, peak, Frequency and Time in the tool wear process.

Comparision of Dominant Features Identification for Tool Wear in Hard Turning of Inconel 718 by Using Vibration Analysis

University college of engineering & Technology, Acharya Nagarjuna University

Strojnícky časopis - Journal of Mechanical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

In various machining processes, the vibration signals are studied for tool condition monitoring often referred as wear monitoring. It is essential to overcome...

Comparision of Dominant Features Identification for Tool Wear in Hard Turning of Inconel 718 by Using Vibration Analysis

Publié en ligne: 05 déc. 2019

Pages: 1 - 8

DOI: https://doi.org/10.2478/scjme-2019-0038

Mots clés
Hard turning, Tool condition Monitoring, Dominant features, Vibration signals, GRA, ANOVA

© 2019 Dasari Kondala Rao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Comparision of Dominant Features Identification for Tool Wear in Hard Turning of Inconel 718 by Using Vibration Analysis

Publié en ligne: 05 déc. 2019

Pages: 1 - 8

DOI: https://doi.org/10.2478/scjme-2019-0038

Mots clésHard turning, Tool condition Monitoring, Dominant features, Vibration signals, GRA, ANOVA

© 2019 Dasari Kondala Rao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
Hard turning, Tool condition Monitoring, Dominant features, Vibration signals, GRA, ANOVA