Influence of cutting parameters causing variation of surface roughness and chip characteristics of Mg AZ31B Alloy

Authors

  • Raisul Haque Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technnology, Dhaka
  • G. M. Shah Imran Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technnology, Dhaka
  • Anannya Zaman Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technnology, Dhaka
  • M. Azizur Rahman Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technnology, Dhaka

Keywords:

Machining, Surface roughness, Optimum parameters, Chip categorization

Abstract

This study aims to investigate the machining of magnesium-based alloy (AZ31B) through experiment as well as analysis based on the turning process with different machining parameters— cutting speed, feed rate and depth of cut. In addition, micro scale studies were performed to determine the surface roughness. The relation between the cutting parameters with the surface roughness can be used to determine the optimal cutting conditions of this material for certain processes. The chips produced under these circumstances are categorized by their shape. These various factors have been considered to find out the optimal cutting condition for the suitable surface roughness of this material. The results show, the dominant parameter for cutting this material is feed rate which drastically influences the roughness of the surface of the material.

References

Abbas, A. T., Pimenov, D. Y., Erdakov, I. N., Taha, M. A., Soliman, M. S., & El Rayes, M. M. (2018). ANN surface roughness optimization of AZ61 magnesium alloy finish turning: Minimum machining times at prime machining costs. Materials, 11(5). https://doi.org/10.3390/ma11050808

Akhyar, G., Harun, S., & Hamni, A. (2016). Surface Roughness Values of Magnesium Alloy AZ31 When Turning by Using Rotary Cutting Tool. INSIST, 1(1), 54. https://doi.org/10.23960/ins.v1i1.20

Azizur Rahman, M., Rahman, M., & Senthil Kumar, A. (2018). Influence of relative tool sharpness (RTS) on different ultra-precision machining regimes of Mg alloy. International Journal of Advanced Manufacturing Technology, 96(9–12), 3545–3563. https://doi.org/10.1007/s00170-018-1599-4

AZoM. (2013). Magnesium AZ31B ­ H24 ( UNS M11311 ) Alloy. 24, 1–2.

Baji?, D., Lela, B., & Cukor, G. (2008). Examination and modelling of the influence of cutting parameters on the cutting force and the surface roughness in longitudinal turning. Strojniski Vestnik/Journal of Mechanical Engineering, 54(5), 322–333.

Benardos, P. G., & Vosniakos, G. C. (2003). Predicting surface roughness in machining: A review. International Journal of Machine Tools and Manufacture, 43(8), 833–844. https://doi.org/10.1016/S0890-6955(03)00059-2

D’Addona, D. M., & Raykar, S. J. (2016). Analysis of Surface Roughness in Hard Turning Using Wiper Insert Geometry. Procedia CIRP, 41, 841–846. https://doi.org/10.1016/j.procir.2015.12.087

Danish, M., Ginta, T. L., Habib, K., Abdul Rani, A. M., & Saha, B. B. (2019). Effect of Cryogenic Cooling on the Heat Transfer during Turning of AZ31C Magnesium Alloy. Heat Transfer Engineering. https://doi.org/10.1080/01457632.2018.1450345

Danish, M., Ginta, T. L., Habib, K., Carou, D., Rani, A. M. A., & Saha, B. B. (2017). Thermal analysis during turning of AZ31 magnesium alloy under dry and cryogenic conditions. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-016-9893-5

Dimla, Snr, D. E. (1999). Application of perceptron neural networks to tool-state classification in a metal-turning operation. Engineering Applications of Artificial Intelligence. https://doi.org/10.1016/s0952-1976(99)00015-9

Dinesh, S., Senthilkumar, V., Asokan, P., & Arulkirubakaran, D. (2015). Effect of cryogenic cooling on machinability and surface quality of bio-degradable ZK60 Mg alloy. Materials and Design. https://doi.org/10.1016/j.matdes.2015.08.099

Frank, S., & Gneiger, S. (2017). Development of cost-effective non-flammable magnesium alloys. Light Metal Age.

Harun, S., Shibasaka, T., & Moriwaki, T. (2009). Cutting temperature measurement in turning with actively driven rotary tool. Key Engineering Materials. https://doi.org/10.4028/0-87849-364-6.138

Hessainia, Z., Belbah, A., Yallese, M. A., Mabrouki, T., & Rigal, J. F. (2013). On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations. Measurement: Journal of the International Measurement Confederation, 46(5), 1671–1681. https://doi.org/10.1016/j.measurement.2012.12.016

Jovi?, S., Arsi?, N., Vukojevi?, V., Anicic, O., & Vuji?i?, S. (2017). Determination of the important machining parameters on the chip shape classification by adaptive neuro-fuzzy technique. Precision Engineering, 48, 18–23. https://doi.org/10.1016/j.precisioneng.2016.11.001

Kopa?, J., Bahor, M., & Sokovi, M. (2002). Optimal machining parameters for achieving the desired surface roughness in fine turning of cold pre-formed steel workpieces. International Journal of Machine Tools and Manufacture, 42(6), 707–716. https://doi.org/10.1016/S0890-6955(01)00163-8

Lu, L., Hu, S., Liu, L., & Yin, Z. (2016). High speed cutting of AZ31 magnesium alloy. Journal of Magnesium and Alloys. https://doi.org/10.1016/j.jma.2016.04.004

Mia, M., & Dhar, N. R. (2017). Optimization of surface roughness and cutting temperature in high-pressure coolant-assisted hard turning using Taguchi method. International Journal of Advanced Manufacturing Technology, 88(1–4), 739–753. https://doi.org/10.1007/s00170-016-8810-2

Muthukrishnan, N., & Davim, J. P. (2009). Optimization of machining parameters of Al/SiC-MMC with ANOVA and ANN analysis. Journal of Materials Processing Technology, 209(1), 225–232. https://doi.org/10.1016/j.jmatprotec.2008.01.041

Nalbant, M., Gökkaya, H., & Sur, G. (2007). Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning. Materials and Design, 28(4), 1379–1385. https://doi.org/10.1016/j.matdes.2006.01.008

Özel, T., & Karpat, Y. (2005). Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. International Journal of Machine Tools and Manufacture, 45(4–5), 467–479. https://doi.org/10.1016/j.ijmachtools.2004.09.007

Pontes, F. J., Paiva, A. P. De, Balestrassi, P. P., Ferreira, J. R., & Silva, M. B. Da. (2012). Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi’s orthogonal arrays. Expert Systems with Applications, 39(9), 7776–7787. https://doi.org/10.1016/j.eswa.2012.01.058

Pu, Z., Outeiro, J. C., Batista, A. C., Dillon, O. W., Puleo, D. A., & Jawahir, I. S. (2012). Enhanced surface integrity of AZ31B Mg alloy by cryogenic machining towards improved functional performance of machined components. International Journal of Machine Tools and Manufacture. https://doi.org/10.1016/j.ijmachtools.2011.12.006

Pu, Z., Umbrello, D., Dillon, O. W., Lu, T., Puleo, D. A., & Jawahir, I. S. (2014). Finite element modeling of microstructural changes in dry and cryogenic machining of AZ31B magnesium alloy. Journal of Manufacturing Processes. https://doi.org/10.1016/j.jmapro.2014.02.002

Rafai, N. H., Othman, M. H., Hasan, S., & Sinnasalam, T. R. A. L. (2013). The optimization in machining AISI 1030 using taguchi method for dry and flood cutting condition. Applied Mechanics and Materials, 315, 841–845. https://doi.org/10.4028/www.scientific.net/AMM.315.841

Ramesh, S., Karunamoorthy, L., & Palanikumar, K. (2012). Measurement and analysis of surface roughness in turning of aerospace titanium alloy (gr5). Measurement: Journal of the International Measurement Confederation, 45(5), 1266–1276. https://doi.org/10.1016/j.measurement.2012.01.010

Risbood, K. A., Dixit, U. S., & Sahasrabudhe, A. D. (2003). Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process. Journal of Materials Processing Technology, 132(1–3), 203–214. https://doi.org/10.1016/S0924-0136(02)00920-2

Rubio, E. M., Camacho, a M., Marcos, M., & Design, I. (2006). Chip arrangement in the dry cutting of aluminium alloys. Journal of Achievements in Materials and Manufacturing Engineering, 16(1–2), 164–170.

Sankaran, K. K., & Mishra, R. S. (2017). Chapter 7 - Magnesium Alloys. In Metallurgy and Design of Alloys with Hierarchical Microstructures. https://doi.org/https://doi.org/10.1016/B978-0-12-812068-2.00007-2

Shi, K., Zhang, D., Ren, J., Yao, C., & Huang, X. (2016). Effect of cutting parameters on machinability characteristics in milling of magnesium alloy with carbide tool. Advances in Mechanical Engineering. https://doi.org/10.1177/1687814016628392

Singh, D., & Rao, P. V. (2007). A surface roughness prediction model for hard turning process. International Journal of Advanced Manufacturing Technology, 32(11–12), 1115–1124. https://doi.org/10.1007/s00170-006-0429-2

Suri, N. (2016). Magnesium Alloys and its Machining: A Review. International Research Journal of Engineering and Technology, 2111–2119. www.irjet.net

Thomas, M., Beauchamp, Y., Youssef, A. Y., & Masounave, J. (1996). Effect of tool vibrations on surface roughness during lathe dry turning process. Computers and Industrial Engineering, 31(3–4), 637–644. https://doi.org/10.1016/s0360-8352(96)00235-5

Tomac, N., Tønnessen, K., & Mikac, T. (2008). Study of influence of aluminium content on machinability of magnesium alloys. Strojarstvo.

Tootooni, M. S., Liu, C., Roberson, D., Donovan, R., Rao, P. K., Kong, Z. (James), & Bukkapatnam, S. T. S. (2016). Online non-contact surface finish measurement in machining using graph theory-based image analysis. Journal of Manufacturing Systems, 41, 266–276. https://doi.org/10.1016/j.jmsy.2016.09.007

Villeta, M., De Agustina, B., De Pipaón, J. M. S., & Rubio, E. M. (2012). Efficient optimisation of machining processes based on technical specifications for surface roughness: Application to magnesium pieces in the aerospace industry. International Journal of Advanced Manufacturing Technology, 60(9–12), 1237–1246. https://doi.org/10.1007/s00170-011-3685-8

Zsolt János Viharos, Markos, S., & Szekeres, C. (2003). ANN-based chip-form classification in turning. XVII IMEKO World Congress, 1977, 1469–1473.

Published

2020-06-30 — Updated on 2020-08-22

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How to Cite

Haque, R., Imran, G. M. S., Zaman, A., & Rahman, M. A. (2020). Influence of cutting parameters causing variation of surface roughness and chip characteristics of Mg AZ31B Alloy. Journal of Production Systems and Manufacturing Science, 1(1), 42-56. Retrieved from http://www.imperialopen.com/index.php/JPSMS/article/view/8 (Original work published June 30, 2020)

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Original Research Articles