Study the Vibration of Turning Aluminum 6500 Surface Roughness Based on Sound Chatter Using Machine Learning

Abstract:

This paper experimented with aluminum grade 6500 to predict surface roughness based on the projected workpiece on a CNC lathe machine. The operation used a 50 mm diameter solid bar, 240 mm long, divided into six equal segments. Each segment was machined at depths of 0.25 mm, 0.5 mm, 0.75 mm, 1.0 mm, 1.25 mm, and 1.5 mm. The actual cutting depths were analyzed based on SolidWorks G-code. Under constant feed rate and spindle speed, the sound chatter was recorded for machine learning. The librosa library enhanced the sound chatter utilizing 50 neurons and a 71051 input shape. In parallel, a batch size of 32 and the 10-epoch training over a 9-layer model achieved 50.4% accuracy. Besides, the spectrogram’s purple color indicated significant signal energy between 0 Hz to 64 Hz and 4096 Hz to 8192 Hz, while lighter color intensity showed weaker energy. The peak intensity of energy represents high vibration, and the weak intensity of energy is linked to the low vibration. Additionally, the Abacus simulation showed cutting depths of 0.25 mm and 0.75 mm, resulting in deformations between 8.713e^-08 mm and -1.176e^-00 mm. The higher deformation values corresponded to less chatter, while lower values indicated low vibration. Overall, deeper cuts on the aluminum grade resulted in peak frequencies associated with a smoother surface finish, whereas shallower cuts produced rougher machined surfaces.

• Periodical: Engineering Headway (Volume 41)
• Pages: 25-34
• DOI: https://doi.org/10.4028/p-cBeo1W
• Online since: July 2026
• Authors: Robsan Abebe*, Dame Alemayehu Efa, Tesfaye Gadisa, Mahesh Gopal
• Keywords: Abacus, CNC Lathe Turning, Machine Learning, Machining Chatter, Vibration

References

[1] V. Jain, K.K. Jain. Vibration on Milling Tool during Milling of Curves. Journal of Emerging Technologies and Innovative Research. 5(02), (2018).

[2] S. A. Shasheekant, P. A. Narwade. Analyzing the Vibration Effect of Cutting Tool on Surface Roughness of Turning Work Piece in Lathe Machine. International Journal of Innovations in Engineering Research and Technology. 9(02), 79-89, (2021).

DOI: 10.22214/ijraset.2021.36446

[3] Y. Shrivastava, B. Singh. Tool chatter prediction based on empirical mode decomposition and response surface methodology. Measurement. 173, 108585, (2021).

DOI: 10.1016/j.measurement.2020.108585

[4] R. Abebe, R, M. Gopal. Exploring the effects of vibration on surface roughness during CNC face milling on aluminum 6061-T6 using sound chatter. Materials Today: Proceedings. 90, 43-49, (2023).

DOI: 10.1016/j.matpr.2023.04.332

[5] G. Quintana I Badosa. Stability lobes diagram identification and surface roughness monitoring in milling processes. Universitat de Girona. (2010).

[6] A. Şahinoğlu, Ş. Karabulut, A. Güllü. Study on spindle vibration and surface finish in turning of Al 7075. Solid State Phenomena. 261, 321-327, (2017).

DOI: 10.4028/www.scientific.net/ssp.261.321

[7] V. M. Shetty, M. Pallapothu, V.R. Pothuganti. Experimental analysis on the influence of spindle vibrations of CNC Lathe on surface roughness using Taguchi method. Int. J. Curr. Eng. Technol. 5(2), 1273-1276, (2015).

[8] C. M. Taylor, S. Turner, N.D. Sims. Chatter, process damping, and chip segmentation in turning: A signal processing approach. Journal of Sound and Vibration. 329(23), 4922-4935, (2010).

DOI: 10.1016/j.jsv.2010.05.025

[9] A. K. Verma, V. Kumhar, M. Verma, V. Rastogi. Vibration analysis of partially cracked symmetric laminated composite plates using Grey-Taguchi. Biointerface Res. Appl. Chem. 12, 4529-4543, (2021).

DOI: 10.33263/briac124.45294543

[10] A. Pal, S.K. Choudhury, S. Chinchanikar. Machinability assessment through experimental investigation during hard and soft turning of hardened steel. Procedia Materials Science. 6, 80-91, (2014).

DOI: 10.1016/j.mspro.2014.07.010

[11] D. K. Das, A. K. Sahoo, R. Das, B. C. Routara. Investigations on hard turning using coated carbide insert: Grey-based Taguchi and regression methodology. Procedia Materials Science, 6, 1351-1358, (2014).

DOI: 10.1016/j.mspro.2014.07.114

[12] S. Kumar, Meenu, P.S. Satsangi. Multiple-response optimization of turning machining by the Taguchi method and the utility concept using uni-directional glass fiber-reinforced plastic composite and carbide (k10) cutting tool. Journal of Mechanical Science and Technology. 27, 2829-2837, (2013).

DOI: 10.1007/s12206-013-0731-5

[13] D. Nguyen, S. Yin, Q. Tang, P.X. Son. Online monitoring of surface roughness and grinding wheel wear when grinding Ti-6Al-4V titanium alloy using ANFIS-GPR hybrid algorithm and Taguchi analysis. Precision Engineering. 55, 275-292, (2019).

DOI: 10.1016/j.precisioneng.2018.09.018

[14] P.M. Huang, C. H. Lee. Estimation of tool wear and surface roughness development using deep learning and sensor fusion. Sensors. 21(16), 5338, (2021).

DOI: 10.3390/s21165338

[15] H. Mamledesai, Y. Zheng, R. Ahmad. Teaching machines to optimize machining parameters: using the independent fuzzy logic controller and image data. SN Applied Sciences. 4(4), 107, (2022).

DOI: 10.1007/s42452-022-04987-0

[16] Asilturk, I. Online surface roughness recognition system by vibration monitoring in CNC turning using adaptive neuro-fuzzy inference system (ANFIS). International Journal of the Physical Sciences. 6(22), 5353-5360, (2011).

[17] S.S. Patil, S.S. Pardeshi, A.D. Patange, R. Jegadeeshwaran. Deep learning algorithms for tool condition monitoring in milling: A review. In Journal of Physics: Conference Series. 1969 (1), 012039). IOP Publishing, (2021).

DOI: 10.1088/1742-6596/1969/1/012039

[18] F. Aggogeri, N. Pellegrini, F.L. Tagliani. Recent advances in machine learning applications in machining processes. Applied sciences. 11(18), 8764, (2021).

DOI: 10.3390/app11188764

[19] S. Kumar, B. Singh. Ascertaining of chatter stability using wavelet denoising and artificial neural network. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 233(1), 39-62, (2019).

DOI: 10.1177/0954406218756440

[20] M. Chinnasamy, R. Rathanasamy, G.V. Kaliyannan, P. Paramasivam, S.K. Jaganathan. A frontier statistical approach towards online tool condition monitoring and optimization for dry turning operation of SAE 1015 steel. Archives of Metallurgy and Materials. 901-909, (2021).

DOI: 10.24425/amm.2021.136396

[21] M. Kuntoğlu, A. Aslan, D.Y. Pimenov, U.A. Usca, E. Salur, M.K. Gupta, S. Sharma. A review of indirect tool condition monitoring systems and decision-making methods in turning: Critical analysis and trends. Sensors. 21(1), 108, (2020).

DOI: 10.3390/s21010108

[22] A. Gok, C. Gologlu, H.I. Demirci, M. Kurt. Determination of surface qualities on inclined surface machining with acoustic sound pressure. Strojniški vestnik-Journal of Mechanical Engineering. 58(10), 587-597, (2012).

DOI: 10.5545/sv-jme.2012.352

[23] S. Sahu, B.B. Choudhury. Optimization of surface roughness using Taguchi methodology & prediction of tool wear in hard-turning tools. Materials Today: Proceedings, 2(4-5), 2615-2623, (2015).

DOI: 10.1016/j.matpr.2015.07.218

[24] M. Kuntoğlu, A. Aslan, H. Sağlam, D.Y. Pimenov, K. Giasin, T. Mikolajczyk, T. Optimization and analysis of surface roughness, flank wear and 5 different sensorial data via tool condition monitoring system in turning of AISI 5140. Sensors. 20(16), 4377, (2020).

DOI: 10.3390/s20164377

[25] A. Al-Shayea, F.M. Abdullah, M.A. Noman, H. Kaid, E. Abouel Nasr. Studying and optimizing the effect of process parameters on machining vibration in the turning process of AISI 1040 steel. Advances in Materials Science and Engineering. (1), (2020), 5480614.

DOI: 10.1155/2020/5480614

[26] C. Lu, J.P. Costes. Surface profile prediction and analysis applied to the turning process. International Journal of Machining and Machinability of Materials, 4(2-3), 158-180, (2008).

DOI: 10.1504/ijmmm.2008.023192

[27] V. Upadhyay, P.K. Jain, N. K. Mehta. In-process prediction of surface roughness in turning of Ti–6Al–4V alloy using cutting parameters and vibration signals. Measurement. 46(1), 154-160, (2013).

DOI: 10.1016/j.measurement.2012.06.002

[28] T.C. Chan, H. H. Lin. Performance of Five-Axis Machine Tool and Intelligent Machining Process. Research Square. (2021).

[29] N. Hassan. Development of a dynamic model for vibration during turning operation and numerical studies (Doctoral dissertation, University of Liverpool. (2014).

[30] M. Magesh, K. Jothiraj, G. Janarthanan. Optimization of wire-EDM parameters to calculate MRR and measure surface finish on SS410. Int. J. Innov. Sci. Res. Technol., 2(3), 2456-2165, (2017).

[31] L. Straka, G. Dittrich. Intelligent control system of generated electrical pulses at discharge machining. Emerging trends in mechatronics. 1, (2020).

DOI: 10.5772/intechopen.88454