Open AccessMathematical modelling of dominant features identification for tool wear monitoring in hard turning by using Acoustic emission
Author(s) -
Dasari Kondala Rao
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i2.1104
Subject(s) - tool wear , taguchi methods , machining , akaike information criterion , artificial neural network , acoustic emission , computer science , matlab , ranking (information retrieval) , grey relational analysis , kurtosis , pattern recognition (psychology) , artificial intelligence , mechanical engineering , materials science , machine learning , mathematics , statistics , engineering , composite material , operating system
In machining processes generally tool wear will be obtained with varying proportions. In the present work, the number of dominant features, which affect the tool wear, are studied and computed on Inconel 718 as work material with varying hardness (51, 53&55HRC) levels. The condition monitoring was done on three tools namely uncoated carbide, coated carbide and ceramic tools. By using L9 Taguchi’s orthogonal array, speed, feed, depth of cut (DOC) and hardness are considered as input operating parameters. By indirect method of Acoustic emission (AE) technique, signals were collected using Lab VIEW software and dominating features were calculated using the MATLAB. The features were trained in neural network and got the relation between tool wear, surface roughness, temperature and features. The simulated data was analyzed by Grey relational analysis (GRA) and the dominating features ranking sequence was obtained for all the three tools and same ranking was also observed with ANOVA. Since there are no common influencing features among these three tools and hence further investigation continued with statistical mathematical modeling. With Akaike information criterion a mathematical model is developed to find the dominant features. By mathematical modeling the sequence in evaluating tool wear was found to be Kurtosis, Frequency, Variance, Mean and RMS and also a relation between tool wear and dominant features was developed which can be readily used by layman for calculating the tool wear.