Research Article
AISI 1040 ÇELİĞİNİN İŞLENEBİLİRLİĞİ SIRASINDA OLUŞAN YÜZEY PÜRÜZLÜLÜĞÜ DEĞERLERİNİN FARKLI TAHMİN MODELLERİ İLE ARAŞTIRILMASI
Abstract
Bu araştırmada, 45 HRc sertlik değerine sahip AISI 1040 çeliği torna tezgahında işlenmiştir. Kesme hızı, ilerleme ve talaş derinliği parametreleri üçer seviye olarak belirlenmiştir. Deney listesi Taguchi L9 ortagonal dizilim ile oluşturulmuştur. Deneyler CNC kontrollü tornada gerçekleştirilmiştir. Tornalama işlemi sonunda ortalama yüzey pürüzlülüğü (Ra), off-line olarak elde edilmiştir. Elde edilen Ra değerleri Taguchi, çoklu regresyon modeli, yapay sinir ağı ve bulanık mantık ile modellenmiştir. Bu modeller arasındaki yüzdesel fark belirlenmiştir. Taguchi yaklaşık %86,27, çoklu regresyon modeli yaklaşık %85,85, yapay sinir ağı yaklaşık %78,92 ve bulanık mantık yaklaşık %93,86 doğrulukla test sonuçlarını tahmin etmiştir.
References
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- Mia, M., Dhar, N. R., (2016). Response surface and neural network based predictive models of cutting temperature in hard turning, Journal of advanced research, 7, 6, 1035-1044.
- Hanief, M., Wani, M. F., Charoo, M. S., (2017). Modeling and prediction of cutting forces during the turning of red brass (C23000) using ANN and regression analysis, Engineering science and technology, an international journal, 20, 3, 1220-1226.
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Year 2021,
, 84 - 92, 02.06.2021
Abstract
References
- Zębala, W., Kowalczyk, R., Matras, A., (2015). Analysis and optimization of sintered carbides turning with PCD tools, Procedia Engineering, 100, 283-290.
- Rech, J., Moisan, A., (2003). Surface integrity in finish hard turning of case-hardened steels, International Journal of Machine Tools and Manufacture, 43, 5, 543-550.
- Paturi, U. M. R., Devarasetti, H., Narala, S. K. R., (2018). Application Of Regression And Artificial Neural Network Analysis In Modelling Of Surface Roughness In Hard Turning Of AISI 52100 Steel, Materials Today: Proceedings, 5, 2, 4766-4777.
- Tzeng, C. J., Lin, Y. H., Yang, Y. K., Jeng, M. C., (2009). Optimization of turning operations with multiple performance characteristics using the Taguchi method and Grey relational analysis, Journal of materials processing technology, 209, 6, 2753-2759.
- 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.
- 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.
- Hocheng, H., Tseng, H. C., Hsieh, M. L., Lin, Y. H., (2018). Tool wear monitoring in single-point diamond turning using laser scattering from machined workpiece, Journal of Manufacturing Processes, 31, 405-415.
- Bagherzadeh, A., Budak, E., (2018). Investigation of machinability in turning of difficult-to-cut materials using a new cryogenic cooling approach, Tribology International, 119, 510-520.
- Ataseven, B., (2013). Forecasting by using artificial neural networks, Institute of Social Sciences, 41, 11, 101-115.
- Palanikumar, K., Karunamoorthy, L., Karthikeyan, R., Latha, B., (2006). Optimization of machining parameters in turning GFRP composites using a carbide (K10) tool based on the Taguchi method with fuzzy logics, Metals and materials International, 12, 6, 483.
- Kohli, A., Dixit, U. S., (2005). A neural-network-based methodology for the prediction of surface roughness in a turning process, The International Journal of Advanced Manufacturing Technology, 25, 1-2, 118-129.
- Das, D., Mukherjee, S., Dutt, S., Nayak, B. B., Sahoo, A. K., (2018). High speed turning of EN24 steel-a Taguchi based grey relational approach, Materials Today: Proceedings, 5, 2, 4097-4105.
- Huang, L., Chen, J. C., (2001). A multiple regression model to predict in-process surface roughness in turning operation via accelerometer, Journal of Industrial Technology, 17, 2, 1-8.
- Barzani, M. M., Zalnezhad, E., Sarhan, A. A., Farahany, S., Ramesh, S., (2015). Fuzzy logic based model for predicting surface roughness of machined Al–Si–Cu–Fe die casting alloy using different additives-turning, Measurement, 61, 150-161.
- Dursun, S., (2012). About fuzzy logic paradigm. Batman University Journal of Life Sciences, 1, 2, 347-354.
- Xavior, M. A., Vinayagamoorthy, R., (2014). Fuzzy inference system for prediction during precision turning of Ti-6Al-4V, Procedia Engineering, 97, 308-319.
- Mia, M., Dhar, N. R., (2016). Response surface and neural network based predictive models of cutting temperature in hard turning, Journal of advanced research, 7, 6, 1035-1044.
- Hanief, M., Wani, M. F., Charoo, M. S., (2017). Modeling and prediction of cutting forces during the turning of red brass (C23000) using ANN and regression analysis, Engineering science and technology, an international journal, 20, 3, 1220-1226.
- Wen, J. L., Yang, Y. K., Jeng, M. C., (2009). Optimization of die casting conditions for wear properties of alloy AZ91D components using the Taguchi method and design of experiments analysis, The International Journal of Advanced Manufacturing Technology, 41, 5-6, 430.
- Akkuş, H., Asilturk, İ., (2011). Predicting surface roughness of AISI 4140 steel in hard turning process through artificial neural network, fuzzy logic and regression models, Scientific Research and Essays, 6, 13, 2729-2736.
There are 20 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Mechanical Engineering |
| Journal Section | Mechanical Engineering |
| Authors | |
| Publication Date | June 2, 2021 |
| Submission Date | December 22, 2020 |
| Published in Issue | Year 2021 |