How to cite this paper
Malagi, R., Barreto, R & Chougula, S. (2022). Neural network based model for estimating cutting force during machining of Ti6Al4V alloy.Journal of Future Sustainability, 2(1), 23-32.
Refrences
Alaneme George, U., & Mbadike Elvis, M. (2019). Modelling of the mechanical properties of concrete with cement ratio partially replaced by aluminium waste and sawdust ash using artificial neural network. SN Applied Sciences, 1(11), 1-18.
Balasubramanian, A. N., Yadav, N., & Tiwari, A. (2021). Analysis of cutting forces in helical ball end milling process using machine learning. Materials Today: Proceedings, 46, 9275-9280.
Benardos, P. G., & Vosniakos, G. C. (2007). Optimizing feedforward artificial neural network architecture. Engineering applications of artificial intelligence, 20(3), 365-382.
Caggiano, A. (2018). Tool wear prediction in Ti-6Al-4V machining through multiple sensor monitoring and PCA features pattern recognition. Sensors, 18(3), 823.
Detak, Y. P., Syarif, J., & Ramli, R. (2010). Prediction of mechanical properties of Ti-6Al-4V using neural network. In Advanced Materials Research (Vol. 89, pp. 443-448). Trans Tech Publications Ltd.
Graupe, D. (2013). Principles of artificial neural networks (Vol. 7). World Scientific.
Jamil, M., He, N., Zhao, W., Khan, A. M., & Gupta, M. K. (2021). Tribological and Machinability Performance of Hybrid Al2O3-MWCNTs MQL for Milling Ti-6Al-4V.
Kadirgama, K., & Abou-El-Hossein, K. A. (2006). Prediction of cutting force model by using neural network. Journal of applied sciences, 6(1), 31-34.
Kaya, B., Oysu, C., & Ertunc, H. M. (2011). Force-torque based on-line tool wear estimation system for CNC milling of Inconel 718 using neural networks. Advances in Engineering Software, 42(3), 76-84.
Ma, X., He, X., & Tu, Z. C. (2021). Prediction of fatigue–crack growth with neural network-based increment learning scheme. Engineering Fracture Mechanics, 241, 107402.
Madić, M. J., & Radovanović, M. R. (2011). Optimal selection of ANN training and architectural parameters using Taguchi method: A case study. FME Transactions, 39(2), 79-86.
Malagi, R. R., Chougula, S. R., & Shetty, R. J. (2018). Prediction of cutting force in turning of Ti-6Al-4V under minimum quantity lubrication (MQL) using response surface model and fuzzy logic model. International Journal of Mechanical and Production Engineering and Research and Development, 8, 263-274.
Rachmatullah, M. I. C., Santoso, J., & Surendro, K. (2020). A novel approach in determining neural networks architecture to classify data with large number of attributes. IEEE Access, 8, 204728-204743.
Salur, E., Kuntoğlu, M., Aslan, A., & Pimenov, D. Y. (2021). The effects of MQL and dry environments on tool wear, cutting temperature, and power consumption during end milling of AISI 1040 steel. Metals, 11(11), 1674.
Shokrani, A., & Newman, S. T. (2019). A new cutting tool design for cryogenic machining of Ti–6Al–4V titanium alloy. Materials, 12(3), 477.
Wang, J., Li, Y., Zhao, R., & Gao, R. X. (2020). Physics guided neural network for machining tool wear prediction. Journal of Manufacturing Systems, 57, 298-310.
Wu, Y. C., & Feng, J. W. (2018). Development and application of artificial neural network. Wireless Personal Communications, 102(2), 1645-1656.
Balasubramanian, A. N., Yadav, N., & Tiwari, A. (2021). Analysis of cutting forces in helical ball end milling process using machine learning. Materials Today: Proceedings, 46, 9275-9280.
Benardos, P. G., & Vosniakos, G. C. (2007). Optimizing feedforward artificial neural network architecture. Engineering applications of artificial intelligence, 20(3), 365-382.
Caggiano, A. (2018). Tool wear prediction in Ti-6Al-4V machining through multiple sensor monitoring and PCA features pattern recognition. Sensors, 18(3), 823.
Detak, Y. P., Syarif, J., & Ramli, R. (2010). Prediction of mechanical properties of Ti-6Al-4V using neural network. In Advanced Materials Research (Vol. 89, pp. 443-448). Trans Tech Publications Ltd.
Graupe, D. (2013). Principles of artificial neural networks (Vol. 7). World Scientific.
Jamil, M., He, N., Zhao, W., Khan, A. M., & Gupta, M. K. (2021). Tribological and Machinability Performance of Hybrid Al2O3-MWCNTs MQL for Milling Ti-6Al-4V.
Kadirgama, K., & Abou-El-Hossein, K. A. (2006). Prediction of cutting force model by using neural network. Journal of applied sciences, 6(1), 31-34.
Kaya, B., Oysu, C., & Ertunc, H. M. (2011). Force-torque based on-line tool wear estimation system for CNC milling of Inconel 718 using neural networks. Advances in Engineering Software, 42(3), 76-84.
Ma, X., He, X., & Tu, Z. C. (2021). Prediction of fatigue–crack growth with neural network-based increment learning scheme. Engineering Fracture Mechanics, 241, 107402.
Madić, M. J., & Radovanović, M. R. (2011). Optimal selection of ANN training and architectural parameters using Taguchi method: A case study. FME Transactions, 39(2), 79-86.
Malagi, R. R., Chougula, S. R., & Shetty, R. J. (2018). Prediction of cutting force in turning of Ti-6Al-4V under minimum quantity lubrication (MQL) using response surface model and fuzzy logic model. International Journal of Mechanical and Production Engineering and Research and Development, 8, 263-274.
Rachmatullah, M. I. C., Santoso, J., & Surendro, K. (2020). A novel approach in determining neural networks architecture to classify data with large number of attributes. IEEE Access, 8, 204728-204743.
Salur, E., Kuntoğlu, M., Aslan, A., & Pimenov, D. Y. (2021). The effects of MQL and dry environments on tool wear, cutting temperature, and power consumption during end milling of AISI 1040 steel. Metals, 11(11), 1674.
Shokrani, A., & Newman, S. T. (2019). A new cutting tool design for cryogenic machining of Ti–6Al–4V titanium alloy. Materials, 12(3), 477.
Wang, J., Li, Y., Zhao, R., & Gao, R. X. (2020). Physics guided neural network for machining tool wear prediction. Journal of Manufacturing Systems, 57, 298-310.
Wu, Y. C., & Feng, J. W. (2018). Development and application of artificial neural network. Wireless Personal Communications, 102(2), 1645-1656.