Experimental Analysis of Surface Roughness Optimization of EN19 Alloy Steel Milling by the Cuckoo Search Algorithm

Shubham Jain; Anil Mulewa

doi:10.52756/ijerr.2024.v38.009

Authors

Shubham Jain Department of Mechanical Engineering, Maulana Azad National Institute of Technology Bhopal, Madhya Pradesh, India https://orcid.org/0000-0003-1254-522X
Anil Mulewa Faculty of Mechanical Engineering, Shri G. S. Institute of Technology and Science, Indore, Madhya Pradesh, India https://orcid.org/0000-0002-0731-9458

DOI:

https://doi.org/10.52756/ijerr.2024.v38.009

Keywords:

Cuckoo search algorithm, EN19 alloy steel, milling, optimization, surface roughness

Abstract

In the present paper, end milling has been performed on EN19 alloy steel by selecting cutting speed, feed rate, & depth of cut as input parameters and surface roughness (SR) as a response. EN19 alloy steel milling is widely used in various sectors, such as the automotive, defence, construction, aerospace, and nuclear sectors. A parametric study of EN19 alloy steel is needed for better machining. The central composite design was used for designing the experiments & modeling the surface roughness as a response. A cuckoo search algorithm was applied to minimize the surface roughness. It was found that feed rate is the most important factor affecting surface roughness (SR). The Cuckoo Search Algorithm also reveals that a minimum SR 1.8576 micrometer has been achieved at a higher speed of 765 RPM, a lower feed rate of 55.9516 mm/min., & a lower depth of cut of 0.4846 mm. The experiment concludes that it is so that the optimum SR is exhibited at both lower feed rates & high speeds. This, in turn, indicates that our implementation of CCD-based SR, followed by the real cuckoo search algorithm optimization, provides similar results and a good model to the practical results we would expect.

References

Aslan, N. (2008). Application of response surface methodology and central composite rotatable design for modeling and optimization of a multi-gravity separator for chromite concentration. Powder Technology, 185(1), 80–86. https://doi.org/10.1016/j.powtec.2007.10.002

Çakır, O. (2023). Effects of etchant concentration and process temperature in chemical milling of 430 stainless steel with FeCl3. Materials Today: Proceedings. https://doi.org/https://doi.org/10.1016/j.matpr.2023.07.165

Chakraborty, S., & Mali, K. (2024). A balanced hybrid cuckoo search algorithm for microscopic image segmentation. Soft Comput, 28, 5097–5124. https://doi.org/10.1007/s00500-023-09186-6

Chen, W.H., Carrera Uribe, M., Kwon, E. E., Lin, K.Y. A., Park, Y.K., Ding, L., & Saw, L. H. (2022). A comprehensive review of thermoelectric generation optimization by statistical approach: Taguchi method, analysis of variance (ANOVA), and response surface methodology (RSM). Renewable and Sustainable Energy Reviews, 169, 112917. https://doi.org/https://doi.org/10.1016/j.rser.2022.112917

Das, C. R., & Ghosh, A. (2023). Performance of carbide end mills coated with new generation nano-composite TiAlSiN in machining of austenitic stainless steel under near-dry (MQL) and flood cooling conditions. Journal of Manufacturing Processes, 104, 418–442. https://doi.org/https://doi.org/10.1016/j.jmapro.2023.09.020

Ermergen, T., & Taylan, F. (2024). Investigation of DOE model analyses for open atmosphere laser polishing of additively manufactured Ti-6Al-4V samples by using ANOVA. Optics & Laser Technology, 168, 109832. https://doi.org/https://doi.org/10.1016/j.optlastec.2023.109832

Gaikhe, V., Sahu, J., & Pawade, R. (2018). Optimization of cutting parameters for cutting force minimization in helical ball end milling of inconel 718 by using genetic algorithm. Procedia CIRP, 77(Hpc), 477–480. https://doi.org/10.1016/j.procir.2018.08.261

Jackson, M. J., Robinson, G. M., Whitt, M. D., da Silva, R. B., da Silva, M. B., & Machado, A. R. (2023). Achieving clean production with nanostructured coated milling tools dry machining low carbon steel. Journal of Cleaner Production, 422, 138523. https://doi.org/https://doi.org/10.1016/j.jclepro.2023.138523

Jain, S., & Parashar, V. (2022). Analysis of high-speed CNC milling of Ti-6Al-4V by multi-objective crow optimisation and multi-objective PSO. International Journal of Materials Engineering Innovation, 13(2), 128–156. https://doi.org/10.1504/IJMATEI.2022.124196

Kaushik, V. S., Subramanian, M., & Sakthivel, M. (2018). Optimization of Processes Parameters on Temperature Rise in CNC End Milling of Al 7068 using Hybrid Techniques. Materials Today: Proceedings, 5(2), 7037–7046. https://doi.org/10.1016/j.matpr.2017.11.367

Kumar, A., Bala, N., Singh Dhami, S., & Kumar, S. (2023). Effects of cryogenic treatment on the performance of coated tungsten carbide inserts during milling of EN24 steel. Materials Today: Proceedings. https://doi.org/https://doi.org/10.1016/j.matpr.2023.03.449

Kumar, M.V., Kumar, B.J., & N, R. (2018). Optimization of Machining Parameters in CNC Turning of Stainless Steel (EN19) By TAGUCHI’S Orthogonal Array Experiments. Materials Today: Proceedings, 5, 11395–11407. https://doi.org/10.1016/j.matpr.2018.02.107

Kumar, T. B., Panda, A., Kumar Sharma, G., Johar, A. K., Kar, S. K., & Boolchandani, D. (2020). Taguchi DoE and ANOVA: A systematic perspective for performance optimization of cross-coupled channel length modulation OTA. AEU - International Journal of Electronics and Communications, 116, 153070. https://doi.org/https://doi.org/10.1016/j.aeue.2020.153070

Mandal, P., Bala, S., Poddar, S., Sarkar, S., & Biswas, H. S. (2022). Fabrication of Graphene-Fe3O4-Polypyrrole based ternary material as an electrode for Pseudocapacitor application. Materials Today: Proceedings, 65, 1001-1010. https://doi.org/10.1016/j.matpr.2022.04.103

Mohanty, G., Mondal, G., Surekha, B., & Tripathy, S. (2018). Experimental investigations on graphite mixed electric discharge machining of En-19 alloy steel. Materials Today: Proceedings, 5(9), 19418–19423. https://doi.org/10.1016/j.matpr.2018.06.302

Muanpaopong, N., Davé, R., & Bilgili, E. (2023). A comparative analysis of steel and alumina balls in fine milling of cement clinker via PBM and DEM. Powder Technology, 421, 118454.

https://doi.org/https://doi.org/10.1016/j.powtec.2023.118454

Ng, N. Y. Z., Abdul Haq, R. H., Marwah, O. M. F., Ho, F. H., & Adzila, S. (2022). Optimization of polyvinyl alcohol (PVA) support parameters for fused deposition modelling (FDM) by using design of experiments (DOE). Materials Today: Proceedings, 57, 1226–1234. https://doi.org/https://doi.org/10.1016/j.matpr.2021.11.046

Parashar, V., Rehman, A., Bhagoria, J. L., & Puri, D.Y. M. (2010). Statistical and regression analysis of Material Removal Rate for wire cut Electro Discharge Machining of SS 304L using design of experiments. International Journal of Engineering Science and Technology, 2(5), 1021–1028.

Patel, R. D., Bhavsar, S. N., & Patel, A. K. (2023). Experimental investigation on cutting force during end milling of AISI D2 tool steel using AlCrN coated tool. Materials Today: Proceedings, 80, 1397–1402. https://doi.org/https://doi.org/10.1016/j.matpr.2023.01.153

Pisani, S., Genta, I., Dorati, R., Modena, T., Chiesa, E., Bruni, G., Benazzo, M., & Conti, B. (2022). A Design of Experiment (DOE) approach to correlate PLA-PCL electrospun fibers diameter and mechanical properties for soft tissue regeneration purposes. Journal of Drug Delivery Science and Technology, 68, 103060.

https://doi.org/https://doi.org/10.1016/j.jddst.2021.103060

Pratap Singh, D., Kumar Dwivedi, V., & Agarwal, M. (2023). Application of the DoE approach to the fabrication of cast Al2O3-LM6 composite material and evaluation of its mechanical and microstructural properties. Materials Today: Proceedings. https://doi.org/https://doi.org/10.1016/j.matpr.2023.03.127

Selvarajan, L., Katherasan, D., Srivijai, B., Rajavel, R., & Ramamoorthi, M. (2018). Experimental Analysis of en 19 Alloy Material on EDM for Improving Geometrical Errors Using Copper Pentagon Shaped Electrode. Materials Today: Proceedings, 5(2), 4508–4514. https://doi.org/10.1016/j.matpr.2017.12.020

Sharma, N., Kumar, S., & Singh, K. K. (2022). Taguchi’s DOE and artificial neural network analysis for the prediction of tribological performance of graphene nano-platelets filled glass fiber reinforced epoxy composites under the dry sliding condition. Tribology International, 172, 107580. https://doi.org/https://doi.org/10.1016/j.triboint.2022.107580

Shashwath, Sudhakar Rao, P., Prabhudev, M. S., Kohir, V., & Anjaiah, G. (2023). CNC milling of EN24 steel for assessment of the process parameters using OFAT technique: A preliminary investigation. Materials Today: Proceedings. https://doi.org/https://doi.org/10.1016/j.matpr.2023.05.465

Tansukatanon, S., Tangwarodomnukun, V., Dumkum, C., Kruytong, P., Plaichum, N., & Charee, W. (2019). Micromachining of stainless steel using TiAlN-coated tungsten carbide end mill. Procedia Manufacturing, 30, 419–426. https://doi.org/10.1016/j.promfg.2019.02.058

Tilak, K. B. G., & Nagaraju, D. (2018). Investigation on Aluminium Alloy 1100 Using Taguchi Robust Design Methodology on CNC Milling. Materials Today: Proceedings, 5(5), 12719–12724. https://doi.org/10.1016/j.matpr.2018.02.255

Tiwari, A., Mandal, A., & Kumar, K. (2015). Optimization of Overcut in Electrochemical Machining for EN 19 Tool Steel Using Taguchi Approach. Materials Today: Proceedings, 2(4–5), 2337–2345. https://doi.org/10.1016/j.matpr.2015.07.293

Unnikrishna Pillai, J., Sanghrajka, I., Shunmugavel, M., Muthuramalingam, T., Goldberg, M., & Littlefair, G. (2018). Optimisation of multiple response characteristics on end milling of aluminium alloy using Taguchi-Grey relational approach. Measurement: Journal of the International Measurement Confederation, 124, 291–298. https://doi.org/10.1016/j.measurement.2018.04.052

Vardhan, V. M., Sankaraiah, G., & Yohan, M. (2018). Optimization of cutting Parameters and Prediction of Ra & MRR for machining of P20 Steel on CNC milling using Artificial Neural Networks. Materials Today: Proceedings, 5(13), 27058–27064. https://doi.org/10.1016/j.matpr.2018.09.010

Wu, S., Liu, G., Zhang, W., Chen, W., & Wang, C. (2023). High-speed milling of hardened steel under minimal quantity lubrication with liquid nitrogen. Journal of Manufacturing Processes, 95, 351–368. https://doi.org/https://doi.org/10.1016/j.jmapro.2023.04.013

Yang, X. S., & Deb, S. (2010). Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation, 1(4), 330–343. https://doi.org/10.1504/IJMMNO.2010.035430

Zhao, X., Li, Z., Yang, B., Sun, X., Sun, G., Wang, S., & Chen, C. (2023). Microstructure and mechanical properties of 304 stainless steel produced by interpass milling hybrid direct energy deposition-arc. Journal of Materials Research and Technology, 27, 3744–3756. https://doi.org/https://doi.org/10.1016/j.jmrt.2023.10.137