Abstract
Minimum Quantity Lubricant (MQL) technique with nanoparticles application has become one of the most effective approaches in cutting hard materials. In this present work, SiO2 particles based on cutting oil CT232 were applied in milling JIS SDK61 steel under MQL condition. The two main targets were to build a mathematical model form machining parameters to predict the surface roughness Ra of machined surface and find the optimum value of Ra. The cutting speed, feed rate, and depth of cut together with nanoparticle concentration were chosen to validate the experiments, which were designed by L27 orthogonal of the Taguchi DOE method. A fitted linear regression model was established with the coefficient of determination R-sq of 88.33%. The minimum Ra of 0.094 µm verified the predictive ability of the model. Further investigation with S/N ratio and analysis of variance (ANOVA) showed that the most significant factor was the feed rate followed by the nanoparticle concentration.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Davim, J.P.: Machining of hard materials: Springer Science & Business Media (2011)
Do, T.-V., Hsu, Q.-C.: Optimization of minimum quantity lubricant conditions and cutting parameters in hard milling of AISI H13 steel. Appl. Sci. 6, 83 (2016)
König, W., Berktold, A., Koch, K.-F.: Turning versus grinding–a comparison of surface integrity aspects and attainable accuracies. CIRP Ann. 42, 39–43 (1993)
Klocke, F., Brinksmeier, E., Weinert, K.: Capability profile of hard cutting and grinding processes. CIRP Ann. 54, 22–45 (2005)
Samantaraya, D., Lakade, S., Keche, A.: An alternate machining method for hardened automotive gears. Procedia Manuf. 20, 517–522 (2018)
Ding, T., Zhang, S., Wang, Y., Zhu, X.: Empirical models and optimal cutting parameters for cutting forces and surface roughness in hard milling of AISI H13 steel. Int. J. Adv. Manuf. Technol. 51, 45–55 (2010)
Do, T.-V., Vu, N.-C., Nguyen, Q.-M.: Optimization of cooling conditions and cutting parameters during hard milling of AISI H13 steel by using Taguchi method. In: 2018 IEEE International Conference on Advanced Manufacturing (ICAM), pp. 396-398 (2018)
Do, T.-V., Le, N.-A.-V.: Optimization of surface roughness and cutting force in MQL hard-milling of AISI H13 steel. In: Advances in Engineering Research and Application: Proceedings of the International Conference, ICERA 2018, pp. 448–454 (2019)
Boswell, B., Islam, M.N., Davies, I.J., Ginting, Y., Ong, A.K.: A review identifying the effectiveness of minimum quantity lubrication (MQL) during conventional machining. Int. J. Adv. Manuf. Tech. 92, 321–340 (2017)
Dong, L., Li, C., Bai, X., Zhai, M., Qi, Q., Yin, Q., et al.: Analysis of the cooling performance of Ti–6Al–4 V in minimum quantity lubricant milling with different nanoparticles. Int. J. Adv. Manuf. Technol. 103, 2197–2206 (2019)
Li, M., Yu, T., Li, H., Yang, L., Shi, J., Wang, W.: Research on surface integrity in graphene nanofluid MQL milling of TC21 alloy. Int. J. Abras. Technol. 9, 49–59 (2019)
Bayat, M., Abootorabi, M.M.: Estimation of energy consumption in milling process with minimum quantity lubrication and comparison with wet cutting state. Modares Mech. Eng. 20, 1701–1708 (2020)
Do, T.-V.: Empirical model for surface roughness in hard milling of AISI H13 steel under nanofluid-MQL condition based on analysis of cutting parameters. J. Mech. Eng. Res. Develop. 43, 89–94 (2020)
Vu, N.-C., Dang, X.-P., Huang, S.-C.: Multi-objective optimization of hard milling process of AISI H13 in terms of productivity, quality, and cutting energy under nanofluid minimum quantity lubrication condition. Measurement and Control, pp. 0020294020919457 (2020)
Do, T.V., Nguyen, Q.M., Pham, M.T.: Optimization of cutting parameters for improving surface roughness during hard milling of AISI H13 steel. In: Key Engineering Materials, pp. 35–39 (2020)
Acknowledgments
The authors wish to thank Thai Nguyen University of Technology. This work was supported by Thai Nguyen University of Technology
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Phan, TD., Do, TV., Pham, TL., Duong, HL. (2021). Optimization of Cutting Parameters and Nanoparticle Concentration in Hard Milling for Surface Roughness of JIS SKD61 Steel Using Linear Regression and Taguchi Method. In: Sattler, KU., Nguyen, D.C., Vu, N.P., Long, B.T., Puta, H. (eds) Advances in Engineering Research and Application. ICERA 2020. Lecture Notes in Networks and Systems, vol 178. Springer, Cham. https://doi.org/10.1007/978-3-030-64719-3_69
Download citation
DOI: https://doi.org/10.1007/978-3-030-64719-3_69
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64718-6
Online ISBN: 978-3-030-64719-3
eBook Packages: EngineeringEngineering (R0)