Predictive mapping of surface roughness in turning of hardened AISI 4340 using carbide tools

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DOI: https://doi.org/10.21924/cst.9.1.2024.1417
Keywords:
Hard turning, linear regression, machine learning, productivity

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Armansyah Ginting
Department of Mechanical Engineering, Universitas Sumatera Utara, Medan 20155, Indonesia
Zuhrina Masyithah
Department of Chemical Engineering, Universitas Sumatera Utara, Medan 20155, Indonesia

Abstract

This study presents a novel approach to predict surface roughness in the hard turning of AISI 4340 steel using carbide tools, aimed to develop a comprehensive predictive map. The hypothesis that surface roughness can be accurately predicted using a linear regression model was tested and confirmed. Experimental results showed surface roughness in the range of 1.946 to 5.636 microns. Statistical analysis revealed a normal distribution of surface roughness data with linear regression as the best-fit model, significantly determined by feed rate and explaining 98.41% of the variance. Machine learning validated this model, achieving high prediction accuracy (R² = 96.91%, MSE = 0.058, RMSE = 0.242). The innovative predictive map, created using a full factorial design, demonstrated a strong agreement between predicted and validated values. This work highlights the potential of integrating statistical and machine learning techniques for precise surface roughness prediction, recommending industrial validation to enhance machining productivity.

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How to Cite
Ginting, A., & Masyithah, Z. (2024). Predictive mapping of surface roughness in turning of hardened AISI 4340 using carbide tools. Communications in Science and Technology, 9(1), 179-184. https://doi.org/10.21924/cst.9.1.2024.1417
Issue
Vol. 9 No. 1 (2024)
Section
Articles
Author Biography

Zuhrina Masyithah, Department of Chemical Engineering, Universitas Sumatera Utara, Medan 20155, Indonesia

Professor in Department of Chemical Engineering

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References

1. B. Wibowo, N.A. Masrurah, Y.U. Kasanah, F. Trapsilawati, Subagyo, and M.A. Ilhami, towards a taxonomy of micro and small manufacturing enterprises, Commun. Sci. Technol. 4 (2019) 74–80.
2. D.C. Montgomery, Design and Analysis of Experiments, 10th Ed., Wiley, 2019.
3. T. Mori, and S.-C. Tsai, Taguchi Methods, ASME, 2011.
4. A.C. Mu?ller, and S. Guido, Introduction to Machine Learning with Python and Scikit-Learn, O’Reilly, 2016.
5. A.C. Müller and S. Guido, Introduction to Machine Learning with Python A Guide for Data Scientists, O’Reilly, 2016.
6. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2nd. Ed., Springer, 2009.
7. M. Soori, B. Arezoo, and R. Dastres, Machine learning and artificial intelligence in CNC machine tools, A review, Sustain. Manuf. Serv. Econ. 2 (2023) 100009.
8. P. Srikanth, P.S. Teja, B.L. Prasad, and V.P. Kalyan, A comprehensive review of machine learning techniques in computer numerical controlled machines, Int. J. Sci. Res. Arch. 9 (2023) 627-637.
9. T.G. Pratama, R. Hartanto, and N.A. Setiawan, Machine learning algorithm for improving performance on 3 AQ-screening classification, Commun. Sci. Technol. 4 (2019) 44–49.
10. M.A. Putra, N.A. Setiawan, and S. Wibirama, Wart treatment method selection using AdaBoost with random forests as a weak learner, Commun. Sci. Technol. 3 (2018) 52–56.
11. K. Ullrich, et al., AI-based optimisation of total machining performance: A review, CIRP J. Manuf. Sci. Technol. 50 (2024) 40–54.
12. R. Kumar, and S. Chauhan, Study on surface roughness measurement for turning of Al 7075/10/SiCp and Al 7075 hybrid composites by using response surface methodology (RSM) and artificial neural networking (ANN), Meas. 65 (2015) 166–180.
13. M. Gopal, Prediction of surface roughness in turning of duplex stainless steel (DSS) using response surface methodology (RSM) and artificial neural network (ANN), Mater. Today. Proc. 47 (2021) 6704–6711.
14. A.J. Santhosh, et al., Optimization of CNC turning parameters using face centred CCD approach in RSM and ANN-genetic algorithm for AISI 4340 alloy steel, Res. Eng. 11 (2021) 100251.
15. A.K. Gupta, S.C. Guntuku, R.K. Desu, and A. Balu, Optimisation of turning parameters by integrating genetic algorithm with support vector regression and artificial neural networks, Int. J. Adv. Manuf. Technol. 77 (2015) 331-339.
16. D. Kong, J. Zhu, C. Duan, L. Lu, and D. Chen, Bayesian linear regression for surface roughness prediction, Mech. Syst. Signal Process. 142 (2020) 106770.
17. V. Dubey, A.K. Sharma, and D.Y. Pimenov, Prediction of Surface Roughness Using Machine Learning Approach in MQL Turning of AISI 304 Steel by Varying Nanoparticle Size in the Cutting Fluid, Lubricants 10 (2022) 81.
18. A. Zollanvari, Machine Learning with Python: Theory and Implementation, Springer, 2023.
19. ISO Standard, Geometrical Product Specifications (GPS) - Indication of surface texture in technical product documentation, ISO 1302 (2002).
20. W. Koenig, R. Komanduri, H.K. Toenshoff, and G. Ackershott, Machining of hard materials, CIRP Annals, 33 (1984) 417-427.
21. R. Suresh, S. Basavarajappa, V.N. Gaitonde, G.L. Samuel, and J.P. Davim, State-of-the-art research in machinability of hardened steels, Proc. Inst. Mech. Eng. B J. Eng. Manuf. 227 (2013) 191-209.
22. W.F. Sales, J. Schoop, L.R.R. da Silva, Á.R. Machado, and I.S. Jawahir, A review of surface integrity in machining of hardened steels, J. Manuf. Process. 58 (2020) 136-162.
23. S. Chinchanikar, and S.K. Choudhury, Machining of hardened steel - Experimental investigations, performance modeling and cooling techniques: A review, Int. J. Mach. Tools Manuf. 89 (2015) 95-109.
24. A. Sharma, M. Kalsia, A.S. Uppal, A. Babbar, and V. Dhawan, Machining of hard and brittle materials: A comprehensive review, Mater. Today. Proc. 50 (2021) 1048-1052.
25. R. Bag, A. Panda, A.K. Sahoo, and R. Kumar, Sustainable high-speed hard machining of AISI 4340 steel under dry environment, Arab J. Sci. Eng. 48 (2023) 3073-3096.
26. R. Suresh, S. Basavarajappa, and G.L. Samuel, Some studies on hard turning of AISI 4340 steel using multilayer coated carbide tool, Meas. 45 (2012) 1872-1884.
27. A. Ginting, C.H.C. Haron, I. Bencheikh, and M. Nouari, Study on characteristics of AlTiN and TiCN coating layers deposited on carbide cutting tools in hard turning of steel: Experimental, simulation and optimisation, Int. J. Mach. Machina. Mater. 23 (2021) 88-112.
28. A. Ginting, M. Nouari, and I. Bencheikh, Increasing productivity in hard turning of steels using CVD-coated carbide, Int. J. Mach. Machina. Mater. 22 (2020) 309-330.
29. S. Kalpakjian, S.R. Schmid, and K.S. Vijay Sekar, Manufacturing Engineering & Technology, 7th Ed., Pearson, 2021.