Open AccessEXPERIMENTAL INVESTIGATION OF TOOL WEAR AND INDUCED VIBRATION IN TURNING HIGH HARDNESS AISI52100 STEEL USING CUTTING PARAMETERS AND TOOL ACCELERATION
Author(s) -
Nitin Ambhore,
Dinesh Kamble
Publication year - 2020
Publication title -
facta universitatis. series: mechanical engineering
Language(s) - English
Resource type - Journals
eISSN - 2335-0164
pISSN - 0354-2025
DOI - 10.22190/fume200116018a
Subject(s) - acceleration , machining , vibration , context (archaeology) , tool wear , machine tool , carbide , cutting tool , artificial neural network , tool steel , mechanical engineering , materials science , structural engineering , computer science , engineering , metallurgy , acoustics , machine learning , paleontology , physics , classical mechanics , biology
In machining of high hardness steel, vibration of cutting tool increases tool wear which reduces its life. Tool wear is catastrophic in nature and hence investigation of its assessment is important. This study investigates experimentally induced vibration during turning of hardened AISI52100 steel of hardness 54±2 HRC using coated carbide insert. In this context, cutting tool acceleration is measured and used to develop a novel mathematical model based on acquired real time acceleration signals of cutting tool. The obtained model is validated as R2= 0.93 while its residuals values closely follow the straight line. The predictions are confirmed by conducting conformity test which revealed a close degree of agreement with respect to the experimental values. The Artificial Neural Network (ANN) examination is performed to determine the model regression value. The study shows that the examined reports forecasts of ANN are more exact than regression analysis. The future directon of this investigation is towards developing a low-cost microcontroller-based hardware unit for in-process tool wear monitoring which could be beneficial for small scale industries.