INVESTIGATION OF EFFECTS OF DIFFERENT CUTTING AND MACHINING PARAMETERS ON SURFACE ROUGHNESS AND MAIN CUTTING FORCES VIA RESPONSE SURFACE METHOD

Öz

In this study, the effects of the parameters of cutting speed, feed rate and minimum quantity lubrication (MQL) frequently employed in machining applications on main cutting force (Fc) and surface roughness (Ra) were investigated. For this purpose, analyses for Fc and Ra were performed utilizing Box-Behnken model. The efficiency of parameters and the changes in parameters on Fc and Ra were studied with the help of experiment set composed of 13 experiments by employing experimental parameters. Also, the effectiveness of design models was investigated by creating different design models. The high success rate modelling for Fc and Ra was realized with 99% success as a result of analyses conducted according to Box-Behnken and Stepwise, Backward and Forward methods of Box-Behnken. The most effective parameter among experimental parameters on Fc and Ra was found to be the feed rate according to Variance Analysis (ANOVA). It was demonstrated that the estimations on the created Box-Behnken model were quite successful on the data initially entered into the system; and that R2 values obtained for Fc and Ra were 0.999 and 0.996, respectively. It was determined that optimum parameters for the Fc were feed rate 0.25 mm/rev, cutting speed 125 m/min and cutting condition MQL2 ml/min, while they were feed rate 0.25 mm/rev, cutting speed 125 m/min and cutting condition MQL1 ml/min for Ra.

Anahtar Kelimeler

• Response surface methodology
• Box-behnken design
• Surface roughness
• Main cutting forces
• Machining parameters

Destekleyen Kurum

Batman University

Proje Numarası

2017-GRADUATE-2

Teşekkür

This research was given support by the Batman University Scientific Research Projects Unit (BTUBAP). project no 2017-GRADUATE-2. Thanks BTUBAP for financial support.

Kaynakça

1. 1. Aouici, H., Yallese, M. A., Chaoui, K., Mabrouki, T., Rigal, J. F. (2012). Analysis of surface roughness and cutting force components in hard turning with CBN tool: Prediction model and cutting conditions optimization. Measurement, 45(3), 344-353.
2. 2. Hessainia, Z., Belbah, A., Yallese, M. A., Mabrouki, T., Rigal, J. F. (2013). On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations. Measurement, 46(5), 1671-1681.
3. 3. Aslan, A. (2020). Optimization and Analysis of Process Parameters for Flank Wear, Cutting Forces and Vibration in Turning of AISI 5140: A Comprehensive Study. Measurement, 107959.
4. 4. Sarıkaya, M., Güllü, A. (2014). Taguchi design and response surface methodology based analysis of machining parameters in CNC turning under MQL. Journal of Cleaner Production, 65, 604-616.
5. 5. Kant, G., Sangwan, K. S. (2014). Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining. Journal of cleaner production, 83, 151-164.
6. 6. Subbaiah, K. V., Raju, C., Suresh, C. (2020). Parametric analysis and optimization of hard turning at different levels of hardness using wiper ceramic insert. Measurement, 158, 107712.
7. 7. Teimouri, R., Amini, S., Mohagheghian, N. (2017). Experimental study and empirical analysis on effect of ultrasonic vibration during rotary turning of aluminum 7075 aerospace alloy. Journal of Manufacturing Processes, 26, 1-12.
8. 8. Bouacha, K., Yallese, M. A., Mabrouki, T., Rigal, J. F. (2010). Statistical analysis of surface roughness and cutting forces using response surface methodology in hard turning of AISI 52100 bearing steel with CBN tool. International Journal of Refractory Metals and Hard Materials, 28(3), 349-361.

9. 9. Yadav, R. N. (2017). A hybrid approach of Taguchi-Response surface methodology for modeling and optimization of duplex turning process. Measurement, 100, 131-138.
10. 10. Koyee, R. D., Heisel, U., Eisseler, R., Schmauder, S. (2014). Modeling and optimization of turning duplex stainless steels. Journal of Manufacturing Processes, 16(4), 451-467.
11. 11. Tazehkandi, A. H., Pilehvarian, F., Davoodi, B. (2014). Experimental investigation on removing cutting fluid from turning of Inconel 725 with coated carbide tools. Journal of Cleaner Production, 80, 271-281.
12. 12. Lalwani, D. I., Mehta, N. K., Jain, P. K. (2008). Experimental investigations of cutting parameters influence on cutting forces and surface roughness in finish hard turning of MDN250 steel. Journal of materials processing technology, 206(1-3), 167-179.
13. 13. Asiltürk, I., Neşeli, S., Ince, 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.
14. 14. Chabbi, A., Yallese, M. A., Meddour, I., Nouioua, M., Mabrouki, T., Girardin, F. (2017). Predictive modeling and multi-response optimization of technological parameters in turning of Polyoxymethylene polymer (POM C) using RSM and desirability function. Measurement, 95, 99-115.
15. 15. Mitsubishi Carbide, Guide to turning inserts. Japan, 2019.
16. 16. STN ISO 3685, Tool-Life Testing with Single-Point Turning Tools, Turkey, 1999.
17. 17. Rao R.V., Advanced Modeling and Optimization of Manufacturing Processes, Springer, London, 2011.
18. 18. Myres, R. H., Montgomery D. C. and Anderson-Cook C. M., Response Surface Methodology, Wiley, New York, 2009.
19. 19. Sönmez, F., Başak, H., Baday, Ş. (2016). Analysis of Burnishing Process with Response Surface Method. Gazi University Science Journal: PART:C ‘Design and Technology’, 4(4), 275-283. 20. Montgomery D. C., Design and Analysis of Experiments, Wiley, New York, 2004.
20. 21. Vinayagamoorthy, R. (2017). Parametric optimization studies on drilling of sandwich composites using the Box–Behnken design. Materials and Manufacturing Processes, 32(6), 645-653. 22. Goud, V., Ramasamy, A., Das, A., Kalyanasundaram, D. (2019). Box-Behnken technique based multi-parametric optimization of electrostatic spray coating in the manufacturing of thermoplastic composites. Materials and Manufacturing Processes, 34(14), 1638-1645.
21. 23. Kandananond, K. (2010). Using the response surface method to optimize the turning process of AISI 12L14 steel. Advances in Mechanical Engineering, 2, 362406.
22. 24. Nouioua, M., Yallese, M.A., Khettabi, R., Belhadi, S., Bouhalais, M. L., Girardin, F. (2017). Investigation of the performance of the MQL, dry, and wet turning by response surface methodology (RSM) and artificial neural network (ANN). The International Journal of Advanced Manufacturing Technology, 93(5-8), 2485-2504.
23. 25. Camposeco-Negrete, C. (2013). Optimization of cutting parameters for minimizing energy consumption in turning of AISI 6061 T6 using Taguchi methodology and ANOVA. Journal of Cleaner Production, 53, 195-203.
24. 26. Das, A., Patel, S. K., Hotta, T. K., Biswal, B. B. (2019). Statistical analysis of different machining characteristics of EN-24 alloy steel during dry hard turning with multilayer coated cermet inserts. Measurement, 134, 123-141.