A STUDY ON PREDICTION OF SURFACE ROUGHNESS AND CUTTING TOOL TEMPERATURE AFTER TURNING FOR S235JR STEEL
Abstract
In machining technologies, the most important criterion taken into consideration when evaluating the product quality is seen as the surface roughness. In the consideration of production quality and cost, tool wear is one of the factors that directly affect the cost of production. In the machining process, the most important parameters affecting the surface roughness and tool temperature are the cutting depth, speed and feed rate of rotation. In order to obtain the best surface quality and to keep the cost at the optimum level, the most suitable processing parameters should be selected by taking into consideration the effect of these parameters on each other. In this study, it is aimed that to prediction of surface roughness (Ra.) and tool temperature (°C) values for turning which has an important position in machining. For this purpose, Artificial Neural Networks (ANN) method and Multi Linear Regression Model (MLRM) were used separately. The data obtained from ANN, Regression Model were compared with the actual test data, and the results were examined. According to the obtained results, it is seen that the ANN method has more successful results than Regression model in surface roughness and tool temperature estimation.
References
- Abouelatta, O. B., & Madl, J. (2001). Surface roughness prediction based on cutting parameters and tool vibrations in turning operations. Journal of materials processing technology, 118(1-3), 269-277.
- Akkuş, H. (2010). Tornalama işlemlerinde yüzey pürüzlülüğünün istatistiksel ve yapay zeka yöntemleriyle tahmin edilmesi (Doctoral dissertation, Selçuk Üniversitesi Fen Bilimleri Enstitüsü).
- Bilgic, H. H., Sen, M. A., & Kalyoncu, M. (2016). Tuning of LQR controller for an experimental inverted pendulum system based on The Bees Algorithm. Journal of Vibroengineering, 18(6), 3684-3694.
- Bilgic, H. H., Yağlı, H., Koç, A., & Yapıcı, A. (2016). Deneysel bir organik rankine çevriminde yapay sinir ağları (Ysa) yardımıyla güç tahmini. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi, 4(1), 7-17.
- Childs, T. (2000). Metal Machining Theory and Applications. Materials Technology, 416.
- Çakır, M., Oral, M., & Aydın, A. (2011) Karanca Koloni Optimizasyon Algoritmaları ile Risk Faktörlerine Bağlı Optimum Hastane Yerleşim Noktasının Bulunması. Engineering Sciences, 6(1), 195-208.
- Dahbi, S., Ezzine, L., & El Moussami, H. (2017). Modeling of cutting performances in turning process using artificial neural networks. International Journal of Engineering Business Management, 9, 1847979017718988.
- Dorigo, M., & Di Caro, G. (1999). Ant colony optimization: A new metaheuristic, evolutionary computation. CEC 99. Proceedings of the 1999 Congress On, 2.
- Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine learning, 3(2), 95-99.
- Guvenc, M. A., Cakir, M., Mistikoglu, S. (2019). Experimental Study on Optimization of Cutting Parameters by Using Taguchi Method for Tool Vibration and Surface Roughness in Dry Turning of AA6013. 10th International Symposium on Intelligent Manufacturing and Service Systems. 1032-1040
- Jurkovic, Z., Cukor, G., Brezocnik, M., & Brajkovic, T. (2018). A comparison of machine learning methods for cutting parameters prediction in high speed turning process. Journal of Intelligent Manufacturing, 29(8), 1683-1693.
- Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471.
- Lu, C. (2008). Study on prediction of surface quality in machining process. Journal of materials processing technology, 205(1-3), 439-450.
- Markopoulos, A. P., Manolakos, D. E., & Vaxevanidis, N. M. (2008). Artificial neural network models for the prediction of surface roughness in electrical discharge machining. Journal of Intelligent Manufacturing, 19(3), 283–292.
- Mert, I., & Arat, H. T. (2014). Prediction of heat transfer coefficients by ANN for aluminum & steel material. International Journal, 5(2), 2305-1493.
- Mia, M., & Dhar, N. R. (2019). Prediction and optimization by using SVR, RSM and GA in hard turning of tempered AISI 1060 steel under effective cooling condition. Neural Computing and Applications, 31(7), 2349-2370.
- Öztürk, O., Kalyoncu, M., & Ünüvar, A. (2018). Multi objective optimization of cutting parameters in a single pass turning operation using the bees algorithm. 1st International Conference on Advances in Mechanical and Mechatronics Engineering.
- Preacher, K. J., & Rucker, D. (2003). A primer on interaction effects in multiple linear regression. Retrieved November, 10, 2003.
- Raj, P. P., Perumal, A. E., & Ramu, P. (2012). Prediction of surface roughness and delamination in end milling of GFRP using mathematical model and ANN.
- Singh, D., & Rao, P. V. (2007). A surface roughness prediction model for hard turning process. The International Journal of Advanced Manufacturing Technology, 32(11-12), 1115-1124.
- Wenden, A. L. (1981a). Machining Fundamentals and Recent Advances. (J. P. Davim, Ed.) (Vol. 3). Springer.
- Wenden, A. L. (1981b). Two Neural Network Programming Assignments Using Arrays. In SIGCSE ’91 Proceedings of the twenty-second SIGCSE technical symposium on Computer science education (Vol. 3, pp. 43–47).
- Zadeh, L. A., & Jose, S. (1975). The Concept of a Linguistic Variable II. Electrical Engineering, 357, 301–357.
- Zain, A. M., Haron, H., & Sharif, S. (2010). Prediction of surface roughness in the end milling machining using Artificial Neural Network. Expert Systems with Applications, 37(2), 1755–1768.
S235JR Çeliği için Tornalama İşlemi Sonrası Yüzey Pürüzlülüğü ve Kesici Takım Uç Sıcaklığının Tahmini Üzerine Bir Çalışma
Abstract
Talaşlı üretim
teknolojilerinde, ürün kalitesi değerlendirilirken dikkate alınan en önemli
kıstas yüzey pürüzlüğü olarak görülmektedir. Üretim kalitesi ve maliyet dikkate
alınması durumunda ise takım aşınması, üretim maliyetini doğrudan etkileyen
etkenler arasında öne çıkmaktadır. Talaşlı imalat sürecinde, yüzey pürüzlüğü ve
takım sıcaklığını etkileyen parametrelerin en önemlileri; kesme derinliği,
devir sayısı ve ilerleme hızıdır. En iyi yüzey kalitesini elde etme ve aynı
zamanda maliyeti optimum seviyede tutabilmek için bu parametrelerin
birbirlerini etkileme durumları dikkate alınarak en uygun işleme parametreleri
seçilmelidir. Bu çalışmada; talaşlı üretimde önemli bir konuma sahip olan
tornalama için yüzey pürüzlülüğü (Ra/Aritmetik Ortalama Sapma) ve işleme
sonrası takım uç sıcaklığı (°C) değerlerinin tahmin edilmesi amaçlanmıştır.
Bunun için Yapay Sinir Ağları (YSA) yöntemi ve Çoklu Lineer Regresyon Modeli
(ÇLRM) ayrı ayrı kullanılmıştır. Geliştirilen YSA ve Regresyon Modelinden elde
edilen veriler ile gerçek test verileri karşılaştırılmış ve sonuçlar
irdelenmiştir. Elde edilen sonuçlara göre yüzey pürüzlüğü ve takım sıcaklığı
tahmininde; YSA yönteminin, Regresyon modeline göre daha başarılı sonuçlar
verdiği görülmüştür.
References
- Abouelatta, O. B., & Madl, J. (2001). Surface roughness prediction based on cutting parameters and tool vibrations in turning operations. Journal of materials processing technology, 118(1-3), 269-277.
- Akkuş, H. (2010). Tornalama işlemlerinde yüzey pürüzlülüğünün istatistiksel ve yapay zeka yöntemleriyle tahmin edilmesi (Doctoral dissertation, Selçuk Üniversitesi Fen Bilimleri Enstitüsü).
- Bilgic, H. H., Sen, M. A., & Kalyoncu, M. (2016). Tuning of LQR controller for an experimental inverted pendulum system based on The Bees Algorithm. Journal of Vibroengineering, 18(6), 3684-3694.
- Bilgic, H. H., Yağlı, H., Koç, A., & Yapıcı, A. (2016). Deneysel bir organik rankine çevriminde yapay sinir ağları (Ysa) yardımıyla güç tahmini. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi, 4(1), 7-17.
- Childs, T. (2000). Metal Machining Theory and Applications. Materials Technology, 416.
- Çakır, M., Oral, M., & Aydın, A. (2011) Karanca Koloni Optimizasyon Algoritmaları ile Risk Faktörlerine Bağlı Optimum Hastane Yerleşim Noktasının Bulunması. Engineering Sciences, 6(1), 195-208.
- Dahbi, S., Ezzine, L., & El Moussami, H. (2017). Modeling of cutting performances in turning process using artificial neural networks. International Journal of Engineering Business Management, 9, 1847979017718988.
- Dorigo, M., & Di Caro, G. (1999). Ant colony optimization: A new metaheuristic, evolutionary computation. CEC 99. Proceedings of the 1999 Congress On, 2.
- Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine learning, 3(2), 95-99.
- Guvenc, M. A., Cakir, M., Mistikoglu, S. (2019). Experimental Study on Optimization of Cutting Parameters by Using Taguchi Method for Tool Vibration and Surface Roughness in Dry Turning of AA6013. 10th International Symposium on Intelligent Manufacturing and Service Systems. 1032-1040
- Jurkovic, Z., Cukor, G., Brezocnik, M., & Brajkovic, T. (2018). A comparison of machine learning methods for cutting parameters prediction in high speed turning process. Journal of Intelligent Manufacturing, 29(8), 1683-1693.
- Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471.
- Lu, C. (2008). Study on prediction of surface quality in machining process. Journal of materials processing technology, 205(1-3), 439-450.
- Markopoulos, A. P., Manolakos, D. E., & Vaxevanidis, N. M. (2008). Artificial neural network models for the prediction of surface roughness in electrical discharge machining. Journal of Intelligent Manufacturing, 19(3), 283–292.
- Mert, I., & Arat, H. T. (2014). Prediction of heat transfer coefficients by ANN for aluminum & steel material. International Journal, 5(2), 2305-1493.
- Mia, M., & Dhar, N. R. (2019). Prediction and optimization by using SVR, RSM and GA in hard turning of tempered AISI 1060 steel under effective cooling condition. Neural Computing and Applications, 31(7), 2349-2370.
- Öztürk, O., Kalyoncu, M., & Ünüvar, A. (2018). Multi objective optimization of cutting parameters in a single pass turning operation using the bees algorithm. 1st International Conference on Advances in Mechanical and Mechatronics Engineering.
- Preacher, K. J., & Rucker, D. (2003). A primer on interaction effects in multiple linear regression. Retrieved November, 10, 2003.
- Raj, P. P., Perumal, A. E., & Ramu, P. (2012). Prediction of surface roughness and delamination in end milling of GFRP using mathematical model and ANN.
- Singh, D., & Rao, P. V. (2007). A surface roughness prediction model for hard turning process. The International Journal of Advanced Manufacturing Technology, 32(11-12), 1115-1124.
- Wenden, A. L. (1981a). Machining Fundamentals and Recent Advances. (J. P. Davim, Ed.) (Vol. 3). Springer.
- Wenden, A. L. (1981b). Two Neural Network Programming Assignments Using Arrays. In SIGCSE ’91 Proceedings of the twenty-second SIGCSE technical symposium on Computer science education (Vol. 3, pp. 43–47).
- Zadeh, L. A., & Jose, S. (1975). The Concept of a Linguistic Variable II. Electrical Engineering, 357, 301–357.
- Zain, A. M., Haron, H., & Sharif, S. (2010). Prediction of surface roughness in the end milling machining using Artificial Neural Network. Expert Systems with Applications, 37(2), 1755–1768.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Research Article |
| Authors | |
| Publication Date | December 30, 2019 |
| Submission Date | September 25, 2019 |
| Acceptance Date | November 1, 2019 |
| Published in Issue | Year 2019 KONJES_c7_Special_Issue |
