AISI 1040 çeliğinin tornalanması sonucu oluşan yüzey pürüzlülük değerlerinin RSM ve YSA ile araştırılması

Yıl 2020, , 186 - 197, 23.03.2020

Harun Akkuş

https://doi.org/10.35193/bseufbd.635997

Öz

Bu çalışmada,
Design Expert programında yüzey yanıt metodu (RSM) Box-Behnken tasarımına göre
deney listesi oluşturulmuştur. Oluşturulan deney listesine uygun olarak AISI
1040 çeliğinin tornalaması sonucu yüzey pürüzlülük değerleri elde edilmiştir.
Elde edilen yüzey pürüzlülük değerleri ile RSM modeli ve yapay sinir ağı (YSA)
modeli oluşturulmuştur. RSM modeli ile ikinci dereceden regresyon denklemi,
varyans analizi (ANOVA) parametre etkileşimlerinin yüzey pürüzlülüğüne etkisi
iki boyutlu kontur grafiği ve üç boyutlu yanıt grafiği, optimum kesme
parametreleri incelenmiştir. Matlab R2013a programı ile YSA modeli
oluşturulmuştur. RSM ve YSA modellerinin tahmin sonuçlarının doğruluğunu
araştırmak için üç tane test deneyi belirlenmiştir. Test deneyleri
gerçekleştirilmiştir. Daha sonra deneysel Ra, RSM tahmini Ra ve YSA tahmini Ra
değerleri kıyaslanmıştır. Bu kıyaslama sonucu RSM modelinin yaklaşık %90
doğrulukla test sonucunu tahmin ettiği belirlenmiştir.

Anahtar Kelimeler

Tornalama, Yüzey pürüzlülüğü, RSM, YSA

Kaynakça

• 1. Agrawal, A., Goel, S., Rashid, W. B., & Price, M. (2015). Prediction of surface roughness during hard turning of AISI 4340 steel (69 HRC). Applied Soft Computing, 30, 279-286.
• 2. Zhang, P., Liu, Z. (2016). Modeling and prediction for 3D surface topography in finish turning with conventional and wiper inserts. Measurement, 94, 37-45.
• 3. Khorasani, A., Yazdi, M. R. S. (2017). Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation. The International Journal of Advanced Manufacturing Technology, 93(1-4), 141-151.
• 4. Butola, R., Ali, P., Khanna, V. (2017). Effecton Surface Properties of mild steel during dry turning & wet turning on lathe. Materials Today: Proceedings, 4(8), 7892-7902.
• 5. Debnath, S., Reddy, M. M., Yi, Q. S. (2016). Influence of cutting fluid conditions and cutting parameters on surface roughness and tool wear in turning process using Taguchi method. Measurement, 78, 111-119.
• 6. Thamizhmanii, S., Saparudin, S., Hasan, S. (2007). Analyses of surface roughness by turning process using Taguchi method. Journal of Achievements in Materials and Manufacturing Engineering, 20(1-2), 503-506.
• 7. Meddour, I., Yallese, M. A., Bensouilah, H., Khellaf, A., & Elbah, M. (2018). Prediction of surface roughness and cutting forces using RSM, ANN, and NSGA-II in finish turning of AISI 4140 hardened steel with mixed ceramic tool. The International Journal of Advanced Manufacturing Technology, 1-19.
• 8. Selvaraj, D. P. (2018). Optimization of surface roughness of duplex staınless steel in dry turning operation using Taguchi technique. Materials Physics and Mechanics, 40, 63-70.
• 9. Moganapriya, C., Rajasekar, R., Ponappa, K., Venkatesh, R., Jerome, S. (2018). Influence of coating material and cutting parameters on surface roughness and material removal rate in turning process using Taguchi method. Materials Today: Proceedings, 5(2), 8532-8538.
• 10. Koç, B., Kaymak-Ertekin, F. (2010). Response surface methodology and food processing applications. GIDA-Journal of Food, 35(1), 63-70.
• 11. Lin, W. S., Lee, B. Y., Wu, C. L. (2001). Modeling the surface roughness and cutting force for turning. Journal of Materials Processing Technology, 108(3), 286-293.
• 12. Li, X. (2002). A brief review: acoustic emission method for tool wear monitoring during turning. International Journal of Machine Tools and Manufacture, 42(2), 157-165.
• 13. Labidi, A., Tebassi, H., Belhadi, S., Khettabi, R., & Yallese, M. A. (2018). Cutting Conditions Modeling and Optimization in Hard Turning Using RSM, ANN and Desirability Function. Journal of Failure Analysis and Prevention, 18(4), 1017-1033.
• 14. Panda, A., Sahoo, A. K., Rout, A. K., Kumar, R., Das, R. K. (2018). Investigation of Flank Wear in Hard Turning of AISI 52100 Grade Steel Using Multilayer Coated Carbide and Mixed Ceramic Inserts. Procedia Manufacturing, 20, 365-371.
• 15. Tasdemir, Ş., Neşeli, S., & Yaldız, S. (2009). Prediction of surface roughness on turning with Artificial Neural Network. Journal of Engineering and Architecture Faculty of Eskişehir Osmangazi University, 22(9), 65-75.
• 16. Kumar, R., Chauhan, S. (2015). Study on surface roughness measurement for turning of Al 7075/10/SiCp and Al 7075 hybrid composites by using Response Surface Methodology (RSM) and Artificial Neural Networking (ANN). Measurement, 65, 166-180.
• 17. Asiltürk, I., Çunkaş, M. (2011). Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method. Expert Systems with Applications, 38(5), 5826-5832.
• 18. Siddique, R. A., Dilwar, F., Nayeem, R. K. (2018). Experimental ınvestıgation of the effect of cutting parameters on cutting temperature usıng RSM and ANN in turning AISI1040. GSJ, 6(8).