Optimization of Turning Process By Using Taguchi Method

Year 2018, Volume: 22 Issue: 5, 1444 - 1448, 01.10.2018

Harun Akkuş , Harun Yaka

https://doi.org/10.16984/saufenbilder.409502

Cited By: 9

Abstract

In this study, AISI 1040 steel is machined on CNC lathes. Taguchi
L₁₆ ortogonal array was used as experimental design. Experiments
were carried out with selected the three cutting parameters. These parameters
were determined as feed rate, cutting speed and cutting depth. Turning
operation was carried out in dry conditions with diamond cutting tools. At the
end of experiments, the values of surface roughness (Rz) on samples were found.
Signal/Noise (S/N) rates were found with using the Taguchi method. According to
the results, feed rate had the most significant effect on Rz among three
factors. In ANOVA analysis, respectively feed rate, cutting depth and cutting
speed are effective at 95% confidence level at Rz value. In repetition
experiments carried out for parameters chosen in Taguchi prediction, it was
identified that Taguchi works with nearly 94% accuracy.

Keywords

AISI 1040, turning, Taguchi, roughness

References

• 1. Özlü B., Demir H., Nas E., 2014. The mathematical modeling of parameters effecting surface roughness and cutting force during CNC turning process. Journal of Advanced Technology Sciences, 3(2), 75-86.
• 2. Madhavi S.K., Sreeramulu D., Venkatesh M., 2017. Evaluation of optimum turning process of process parameters using DOE and PCA Taguchi method. Materials. Today: Proceedings, 4(2), 1937-1946.
• 3. Sankar B.R., 2017. Analysis of forces during hard turning of AISI 52100 steel using Taguchi method. Materials Today: Proceedings, 4(2), 2114-2118.
• 4. Dhar N.R., Kamruzzaman M., Ahmed M., 2006. Effect of minimum quantity lubrication (MQL) on tool wear and surface roughness in turning AISI-4340 steel. Journal of materials processing technology, 172(2), 299-304.
• 5. Bensouilah H., Aouici H., Meddour I., Yallese M.A., Mabrouki T., Girardin F., 2016. Performance of coated and uncoated mixed ceramic tools in hard turning process”. Measurement, 82, 1-18.
• 6. Yadav R.N., 2017. A hybrid approach of Taguchi-Response surface methodology for modeling and optimization of duplex turning process. Measurement, 100, 131-138.
• 7. Ahmed G.S., Quadri S.S.H., Mohiuddin M.S., 2015. Optimization of feed and radial force in turning process by using Taguchi design approach. Materials Today: Proceedings, 2(4), 3277-3285.
• 8. Asiltürk İ., Neşeli S., İnce M.A., 2016. Optimisation of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods. Measurement, 78, 120-128.
• 9. Gupta M., Kumar S., 2015. Investigation of surface roughness and MRR for turning of UD-GFRP using PCA and Taguchi method. Engineering Science and Technology, International Journal, 18(1), 70-81.
• 10. Grzesik W., 2008. Influence of tool wear on surface roughness in hard turning using differently shaped ceramic tools. Wear, 265(3), 327-335.
• 11. 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), 467-479.
• 12. Kaçal A., Yıldırım F., 2016. Determining of the optimal turning parameters using the response surface methodology in powder metallurgical tool steel. El-Cezeri Journal of Science and Engineering, 3(2), 272-280.
• 13. Pontes F.J., Paiva A.P., Balestrassi P. P., Ferreira J.R., Silva M.B., 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.
• 14. Ramesh S., Viswanathan R., Ambika S., 2016. Measurement and optimization of surface roughness and tool wear via grey relational analysis. TOPSIS and RSA techniques”, Measurement, 78, 63-72.
• 15. Gürbüz H., 2015. Statistical analysis and ınvestigation of main cutting forces resulting from turning of AISI 1050 steel with coated and uncoated cutting tools in different cutting parameters. Batman University Journal of Life Sciences; Volume 5 Number 2, 147-162.
• 16. Akıncıoğlu S., Şirin Ş., Akıncıoğlu G., Şirin E., 2016. Optımızation of surface roughness in turning of 100Cr6 bearing steel. Journal of Advanced Technology Sciences, 5(1), 46-52.