Condition Monitoring in Drilling Operation based on Vibration Signals
John Stephen R1, Thangeswari T2, Palani S3, D.Dinakaran4
1John Stephen R*, Department of Mechanical Engineering, Vel Tech Multitech, Avadi, Chennai-62, India.
2Dr.Thangeswari T, Department of Physics, Vel Tech Multitech, Avadi, Chennai-62, India.
3Dr.Palani S, Department of Mechanical Engineering, Vel Tech Multitech, Avadi, Chennai-62, India.
4Dr.D.Dinakaran , Department of Mechanical Engineering, Hindustan University, Chennai-603103, India.
Manuscript received on 15 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 1272-1277 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4416098319 /19©BEIESP | DOI: 10.35940/ijrte.C4416.098319
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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: Tool condition monitoring is the efficient process for all machining managing operation and the maintenance of machinery operation. Tool condition monitoring implies effective production cost, the rate of tool life, tool quality, dimensional accuracy in terms of tolerance and surface finish in machine shop. Here the machining operation is fully depending on the whims & fancies of the operator. So when a new person operating the machine it makes more troubles in terms to find out the tool wearing point and it make operation difficulty by the operator. To overcome this difficulty a systematic methodology required for machining operation. This paper deals with monitoring the condition on the drilling operation with the help of Accelerometer sensor a physical vibration model 8636C50 having a broad band sensitivity of Sensitivity (±5%) 100.0mV/g and resonant frequency up to 22.0 kHz and performing the drilling operation on EN 24 steel at various operation parameters and analyzing the time domain signal response and frequency domain response graph and implemented analyze the feasibility of proposed methodology for practical applications. Further, the Lab View was used to predict amplitude of work piece vibration which determines the tool condition after various experimental tests. In the time domain, the characteristic parameter during drill wear represent RMS value increase in flank wear and also shows the linear relationship between these two. In the frequency domain, the characteristic parameters during drill failure represent the magnitude of vibration amplitude and the increase in flank wear. Here multilayer Artificial Neural Network (ANN) model, Fuzzy Neural Network and Taguchi Method have been trained with the experimental data using back propagation algorithm. Condition monitoring of drilling is fully depending on the vibration signals. Based on the vibration signal the tool wear point is found out. Experiments results indicated the effect of unconditional drilling operation and detected the tool failure and proper operating condition for drilling machining.
Keywords: Condition monitoring, Drilling Process, Tool wear, Vibration Analysis.
Scope of the Article: Health Monitoring and Life Prediction of Structures