Tool Condition Monitoring in Hard Turning of Inconel 718 by using Vibration Technique
D. Kondala Rao1, Kolla Srinivas2
1D.Kondala Rao, Research scholar, Dept. of Mechanical Engg, University college of engineering & Technology, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India.
2Kolla Srinivas, Professor, Dept. of Mechanical Engg., R.V.R. & J.C. College of Engineering, Guntur, Andhra Pradesh, India.
Manuscript received on 24 August 2019. | Revised Manuscript received on 06 September 2019. | Manuscript published on 30 September 2019. | PP: 1143-1147 | Volume-8 Issue-11, September 2019. | Retrieval Number: J90460881019/2019©BEIESP | DOI: 10.35940/ijitee.J9046.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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 and coated carbide 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.
Keywords: Hard turning, Tool condition Monitoring (TCM), Dominant features, Vibration signals, GRA, ANOVA.
Scope of the Article: