Modeling of external cylindrical grinding process using T1 tool steel

Keywords: External cylindrical grinding, Modeling, Genetic Algorithms, productivity, quality, surface roughness, workpiece Rockwell hardness, cutting force, vibration

Abstract

The selection of the optimal external cylindrical grinding conditions importantly contributes to increase of productivity and quality of the products. The external cylindrical grinding is a method of finishing machine elements surface with an indeterminate blade shape. External cylindrical grinding can process surfaces that require high gloss and precision, although it can also be used to remove large surplus stock. Therefore, multi objective optimization for the external cylindrical grinding process is a problem with high complexity. In this study, an experimental study was performed to improve the productivity and quality of grinding process. By using the experimental date, the surface roughness, cutting force, and vibrations were modeled. To achieve the minimum value of surface roughness and maximum value of material removal rate, the optimal values of external cylindrical grinding conditions were determined by using the combination of Genetic Algorithms (GAs) and weighting method. The optimum values of surface roughness and material removal rate are 0.510 μm and 5.906 mm2/s, respectively. The obtained optimal values of cutting parameters were a feed rate of 0.3 mm/rev, a workpiece speed of 188.1 rpm, a cutting depth of 0.015 mm, and a workpiece Rockwell hardness of 54.78 HRC. The optimal values of cutting parameters, and workpiece hardness were successfully verified by comparing of experimental and predicted results. The approach method of this study can be applied in industrial machining to improve the productivity and quality of the products in external cylindrical grinding process of the T1 tool steel

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Author Biography

Tuan-Linh Nguyen, Hanoi University of Industry

Department of Mechanical Engineering

References

Brinksmeier, E., Tönshoff, H. K., Czenkusch, C., Heinzel, C. (1998). Modelling and optimization of grinding processes. Journal of Intelligent Manufacturing, 9 (4), 303–314. doi: https://doi.org/10.1023/a:1008908724050

Lee, C. W., Shin, Y. C. (2000). Evolutionary modelling and optimization of grinding processes. International Journal of Production Research, 38 (12), 2787–2813. doi: https://doi.org/10.1080/002075400411484

Lee, T. S., Ting, T. O., Lin, Y. J., Htay, T. (2006). A particle swarm approach for grinding process optimization analysis. The International Journal of Advanced Manufacturing Technology, 33 (11-12), 1128–1135. doi: https://doi.org/10.1007/s00170-006-0538-y

Krishna, A. G., Rao, K. M. (2006). Multi-objective optimisation of surface grinding operations using scatter search approach. The International Journal of Advanced Manufacturing Technology, 29 (5-6), 475–480. doi: https://doi.org/10.1007/bf02729099

Sedighi, M., Afshari, D. (2009). Creep feed grinding optimization by an integrated GA-NN system. Journal of Intelligent Manufacturing, 21 (6), 657–663. doi: https://doi.org/10.1007/s10845-009-0243-4

Lin, X., Li, H. (2008). Enhanced Pareto Particle Swarm Approach for Multi-Objective Optimization of Surface Grinding Process. 2008 Second International Symposium on Intelligent Information Technology Application. doi: https://doi.org/10.1109/iita.2008.75

Baskar, N., Saravanan, R., Asokan, P., Prabhaharan, G. (2004). Ants colony algorithm approach for multi-objective optimisation of surface grinding operations. The International Journal of Advanced Manufacturing Technology, 23 (5-6), 311–317. doi: https://doi.org/10.1007/s00170-002-1533-6

Saravanan, R., Sachithanandam, M. (2001). Genetic Algorithm (GA) for Multivariable Surface Grinding Process Optimisation Using a Multi-objective Function Model. The International Journal of Advanced Manufacturing Technology, 17 (5), 330–338. doi: https://doi.org/10.1007/s001700170167

McCall, J. (2005). Genetic algorithms for modelling and optimisation. Journal of Computational and Applied Mathematics, 184 (1), 205–222. doi: https://doi.org/10.1016/j.cam.2004.07.034

Mitchell, M. (1999). An Introduction to Genetic Algorithms. A Bradford Book, 221.

Arora, P. K., Haleem, A., Singh, M. K., Kumar, H., Kaushik, M. (2014). Design of a Production System Using Genetic Algorithm. Procedia Technology, 14, 390–396. doi: https://doi.org/10.1016/j.protcy.2014.08.050

Nee, A. Y. C. (Ed.) (2015) Handbook of Manufacturing Engineering and Technology. Springer-Verlag London, 3500. doi: https://doi.org/10.1007/978-1-4471-4670-4

Arora, J. S. (2012). Introduction to Optimum Design. Academic Press. doi: https://doi.org/10.1016/c2009-0-61700-1

Nguyen, T.-L., Nguyen, N.-T., Hoang, L. (2020). A study on the vibrations in the external cylindrical grinding process of the alloy steels. International Journal of Modern Physics B, 34 (22n24), 2040150. doi: https://doi.org/10.1142/s0217979220401505

Montgomery, D. C. (2019). Design and Analysis of Experiments. Wiley, 688.


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Published
2021-05-27
How to Cite
Nguyen, T.-L. (2021). Modeling of external cylindrical grinding process using T1 tool steel. EUREKA: Physics and Engineering, (3), 85-98. https://doi.org/10.21303/2461-4262.2021.001698
Section
Engineering