OPTIMUM PERFORMANCE OF GREEN MACHINING ON THIN WALLED TI6AL4V USING RSM AND ANN IN TERMS OF CUTTING FORCE AND SURFACE ROUGHNESS

Authors

  • Muhammad Yanis Mechanical Engineering Department, Sriwijaya University, 30662, Inderalaya, Ogan Ilir, South Sumatera, Indonesia
  • Amrifan Saladin Mohruni Mechanical Engineering Department, Sriwijaya University, 30662, Inderalaya, Ogan Ilir, South Sumatera, Indonesia
  • Safian Sharif School of Mechanical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Irsyadi Yani Mechanical Engineering Department, Sriwijaya University, 30662, Inderalaya, Ogan Ilir, South Sumatera, Indonesia

DOI:

https://doi.org/10.11113/jt.v81.13443

Keywords:

Optimization, green machining, thin-walled Ti6Al4V, RSM, ANN, cutting force, surface roughness

Abstract

Thin walled titanium alloys are mostly applied in the aerospace industry owing to their favorable characteristic such as high strength-to-weight ratio. Besides vibration, the friction at the cutting zone in milling of thin-walled Ti6Al4V will create inconsistencies in the cutting force and increase the surface roughness. Previous researchers reported the use of vegetable oils in machining metal as an effort towards green machining in reducing the undesirable cutting friction. Machining experiments were conducted under Minimum Quantity Lubrication (MQL) using coconut oil as cutting fluid, which has better oxidative stability than other vegetable oil. Uncoated carbide tools were used in this milling experiment. The influence of cutting speed, feed and depth of cut on cutting force and surface roughness were modeled using response surface methodology (RSM) and artificial neural network (ANN). Experimental machining results indicated that ANN model prediction was more accurate compared to the RSM model. The maximum cutting force and surface roughness values recorded are 14.89 N, and 0.161 µm under machining conditions of 125 m/min cutting speed, 0.04 mm/tooth feed, 0.25 mm radial depth of cut (DOC) and 5 mm axial DOC.

 

Author Biographies

  • Muhammad Yanis, Mechanical Engineering Department, Sriwijaya University, 30662, Inderalaya, Ogan Ilir, South Sumatera, Indonesia
    PhD CandidateMechanical Engineering DepartmentFaculty of EngineeringSriwijaya UniversityIndralaya-30662South Sumatera-IndonesiaandResearch GroupsMachining of Aerospace MaterialsMechanical Engineering DepartmentFaculty of EngineeringSriwijaya University
  • Amrifan Saladin Mohruni, Mechanical Engineering Department, Sriwijaya University, 30662, Inderalaya, Ogan Ilir, South Sumatera, Indonesia
    Associate ProfessorMechanical Engineering DepartmentFaculty of EngineeringSriwijaya UniversityIndralaya-30662South Sumatera-IndonesiaandResearch GroupsMachining of Aerospace MaterialsMechanical Engineering DepartmentFaculty of EngineeringSriwijaya University
  • Safian Sharif, School of Mechanical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
    Professor in Mechanical Engineering
    Universiti Teknologi Malaysia
  • Irsyadi Yani, Mechanical Engineering Department, Sriwijaya University, 30662, Inderalaya, Ogan Ilir, South Sumatera, Indonesia
    Senior LecturerMechanical Engineering DepartmentFaculty of EngineeringSriwijaya UniversityIndralaya-30662South Sumatera-IndonesiaandResearch GroupsMachining of Aerospace MaterialsMechanical Engineering DepartmentFaculty of EngineeringSriwijaya University

References

Bolar, G., Das, A., and Joshi, S. N. 2018. Analysis of Surface Integrity and Dimensional Accuracy During Thin-Wall Machining BT - Techno-Societal 2016. In: Pawar, P. M., Ronge, B. P., Balasubramaniam, R., Seshabhattar, S., editors. Proceedings of the International Conference on Advanced Technologies for Societal Applications, Techno-Societal 2016. Cham: Springer International Publishing. 681-8.

Huang, P. L., Li, J. F., Sun, J., and Jia, X. M. 2016. Cutting Signals Analysis in Milling Titanium Alloy Thin-part Components and Non-thin-wall Components. International Journal of Advanced Manufacturing Technology 84(9): 2461-9. Doi: 10.1007/s00170-015-7837-0.

Feng, J., Sun, Z., Jiang, Z., and Yang, L. 2016. Identification of Chatter in Milling of Ti-6Al-4V Titanium Alloy Thin-walled Workpieces based on Cutting Force Signals and Surface Topography. International Journal of Advanced Manufacturing Technology 82(9-12): 1909-20. Doi: 10.1007/s00170-015-7509-0.

Jiang, Z. H., Jia, M. F., and Liu, P. H. 2017. Experimental Study on Milling Force in Processing Ti6Al4V Thin-walled Part 154(Icmia). 486-515.

Park, K.-H., Suhaimi, M. A., Yang, G.-D., Lee, D.-Y., Lee, S.-W., and Kwon, P. 2017. Milling of Titanium Alloy with Cryogenic Cooling and Minimum Quantity Lubrication (MQL). International Journal of Precision Engineering and Manufacturing. 18(1): 5-14. Doi: 10.1007/s12541-017-0001-z.

Srikant, R. R. and Rao, P. N. 2017. Use of Vegetable-based Cutting Fluids for Sustainable Machining. Doi: 10.1007/978-3-319-51961-6.

Huang, Y. A., Zhang, X., and Xiong, Y. 2012. Finite Element Analysis of Machining Thin-Wall Parts: Error Prediction and Stability Analysis. In: Ebrahimi, F., editor. Finite Element Analysis-Applications in Mechanical Engineering. 1st ed. In Tech. 327-54.

Debnath, S., Reddy, M. M., and Yi, Q. S. 2014. Environmental Friendly Cutting Fluids and Cooling Techniques in Machining: A Review. Journal of Cleaner Production 83(November): 33-47. Doi: 10.1016/j.jclepro.2014.07.071.

Boswell, B., Islam, M. N., Davies, I. J., Ginting, Y. R., and Ong, A. K. 2017. A Review Identifying the Effectiveness of Minimum Quantity Lubrication (MQL) During Conventional Machining. International Journal of Advanced Manufacturing Technology. Feb.: 1-20. Doi: 10.1007/s00170-017-0142-3.

Sharif, M. N., Pervaiz, S., and Deiab, I. 2017. Potential of Alternative Lubrication Strategies for Metal Cutting Processes: A Review. International Journal of Advanced Manufacturing Technology. Doi: 10.1007/s00170-016-9298-5.

Sharma, V. S. S., Singh, G., and Sørby, K. 2015. A Review on Minimum Quantity Lubrication for Machining Processes Machining Processes. Machining Science and Technology 30(8): 935-53. Doi: 10.1080/10426914.2014.994759.

Sodavadia, K. P. and Makwana, A. H. 2014. Experimental Investigation on the Performance of Coconut oil Based Nano Fluid as Lubricants during Turning of AISI 304 Austenitic Stainless Steel. International Journal of Advanced Mechanical Engineering. 4(1): 55-60.

Rahim, E. A. and Sasahara, H. 2011. Investigation of Tool Wear and Surface Integrity on MQL Machining of Ti-6AL-4V using Biodegradable Oil. Proceeding of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 225(9): 1505-10. Doi: 10.1177/0954405411402554.

Armendia, M., Garay, A., Iriarte, L., and Arrazola, P. 2010. Comparison of the Machinabilities of Ti6Al4V and TIMETAL ® 54M using Uncoated WC – Co Tools. Journal of Materials Processing Technology. 210: 197-203. Doi: 10.1016/j.jmatprotec.2009.08.026.

Rahman Rashid, R. A., Palanisamy, S., Sun, S., and Dargusch, M. S. 2016. Tool Wear Mechanisms Involved in Crater Formation on Uncoated Carbide Tool when Machining Ti6Al4V Alloy. International Journal of Advanced Manufacturing Technology. 83(9-12): 1457-65. Doi: 10.1007/s00170-015-7668-z.

Bolar, G. and Joshi, S. N. 2017. Three-dimensional Numerical Modeling, Simulation and Experimental Validation of Milling of a Thin-wall Component. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 231(5): 792-804. Doi: 10.1177/0954405416685387.

Rao, R. V. and Kalyankar, V. D. 2014. Optimization of Modern Machining Processes using Advanced Optimization Techniques: A Review. The International Journal of Advanced Manufacturing Technology. 1159-88. Doi: 10.1007/s00170-014-5894-4.

Myers, R. H., Montgomery, D. C., and Anderson-Cook, C. M. 2009. Response Surface Methodology: Process and Product Optimization Using Designed Experiments. 3rd ed. Hoboken, New Jersey (USA), United States of America (USA): John Wiley & Sons, Inc.

Mohruni, A. S., Yanis, M., Sharif, S., Yani, I., Yuliwati, E., Ismail, A.F., et al. 2017. A Comparison RSM and ANN Surface Roughness Models in Thin-wall Machining of Ti6Al4V using Vegetable Oils under MQL-condition. AIP Conference Proceedings 1885(020161): 1-10. Doi: 10.1063/1.5002355.

Kilickap, E., Yardimeden, A., and Çelik, Y. H. 2017. Mathematical Modelling and Optimization of Cutting Force, Tool Wear and Surface Roughness by using Artificial Neural Network and Response Surface Methodology in Milling of Ti-6242S. Applied Sciences. 7(10): 1064. Doi: 10.3390/app7101064.

Sangwan, K. S., Saxena, S., and Kant, G. 2015. Optimization of Machining Parameters to Minimize Surface Roughness using Integrated ANN-GA Approach. Procedia CIRP 29: 305-10. Doi: 10.1016/j.procir.2015.02.002.

Kant, G. and Sangwan, K. S. 2015. Predictive Modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithm. Procedia CIRP 31(CIRP 15): 453-8. Doi: 10.1016/j.procir.2015.03.043.

Sehgal, A. K. 2014. Application of Artificial Neural Network in Surface Roughness Prediction considering Mean Square Error as Performance Measure. 72-6.

Ozel, T., Thepsonthi, T., Ulutan, D., Kaftanoglu, B. 2011. Experiments and Finite Element Simulations on Micro-Milling of Ti-6Al-4V Alloy with Uncoated and cBN Coated Micro-Tools. CIRP Ann Manuf Technol. 60: 85-88. Doi: 10.1016/j.cirp.2011.03.087.

Downloads

Published

2019-09-22

Issue

Vol. 81 No. 6: November 2019

Section

Science and Engineering

License

Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.

How to Cite

OPTIMUM PERFORMANCE OF GREEN MACHINING ON THIN WALLED TI6AL4V USING RSM AND ANN IN TERMS OF CUTTING FORCE AND SURFACE ROUGHNESS. (2019). Jurnal Teknologi, 81(6). https://doi.org/10.11113/jt.v81.13443