Abstract
This article presents an approach for the simulation of machining operations through Artificial Intelligence, which guarantees an automatic learning of the distinctive features in the processes of metal cutting. In the research, an Artificial Neural Network was designed, which establishes the relationships between the parameters of cutting regime and the technological indexes of machining, based on the information generated in real experimentation. For the conception of suitable cutting strategies, the following magnitudes were considered for the input of the model: lubrication regime, cutting speed, feed rate and machining time; which determined the behavior of the cutting forces in the turning of the AISI 316L steel, in order to obtain the cutting powers that define the specific energy consumption. Several designs were considered according to the features of Multi-Layer Perceptron architecture and the selected model was evaluated according to the mean square error and the regression coefficient R2, reflecting high precision in the approximation. The deviation for the error made in the estimation of the cutting force values represents approximately 2% of the average value. These results showed a good level of reliability in the prediction of energy consumption under various machining conditions, in order to adopt relevant saving measures.
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References
Grzesik, W.: Advanced Machining Processes of Metallic Materials: Theory, Modelling and Applications. Elsevier, New York City (2008). http://www.elsevier.com/wps
Kara, S., Li, W.: Unit process energy consumption models for material removal processes. CIRP Ann. Manuf. Technol. 60, 37–40 (2011)
Dornfeld, A.: Moving towards green and sustainable manufacturing. Int. J. Precis. Eng. Manuf. Green Technol. 1, 63–66 (2014)
Arsecularatne, J., Zhang, L., Montross, C.: Wear and tool life of tungsten carbide, PCBN and PCD cutting tools. Int. J. Mach. Tools Manuf. 46(2), 482–491 (2006)
Mandal, S., Sivaprasad, P., Venugopal, S., Murthy, K.: Constitutive flow behaviour of austenitic stainless steel under hot deformation: artificial neural network modelling to understand, evaluate and predict. Modell. Simul. Mater. Sci. Eng. 14, 1053–1070 (2006)
Çiçek, A., Kivak, T., Samtas, G., Çay, Y.: Modelling of thrust forces in drilling of AISI 316 stainless steel using artificial neural network and multiple regression analysis. J. Mech. Eng. 58, 492–498 (2012)
Ahilan, C., Kumanan, S., Sivakumaran, N., Dhas, J.: Modeling and prediction of machining quality in CNC turning process using intelligent hybrid decision making tools. Appl. Soft Comput. 13(3), 1543–1551 (2013)
Koyee, R., Heisel, U., Eisseler, R., Schmauder, S.: Modeling and optimization of turning duplex stainless steels. J. Manuf. Process. 16, 451–467 (2014)
Phate, M., Toney, S.: Formulation of artificial neural network based model for the dry machining of ferrous and non-ferrous materials used in Indian small scale industries. Int. J. Mater. Sci. Eng. 4(3), 145–160 (2016)
Mia, M., et al.: Effect of time-controlled MQL pulsing on surface roughness in hard turning by statistical analysis and artificial neural network. Int. J. Adv. Manuf. Technol. 91, 3211–3223 (2017)
Xie, J., Liu, F., Qiu, H.: An integrated model for predicting the specific energy consumption of manufacturing processes. Int. J. Adv. Manuf. 85, 1339–1346 (2016)
Paluszek, M., Thomas, S.: MATLAB Machine Learning. Apress, New Jersey (2017)
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Curra-Sosa, DA., Pérez-Rodríguez, R., Del-Risco-Alfonso, R. (2018). Predictive Model for Specific Energy Consumption in the Turning of AISI 316L Steel. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2018. Lecture Notes in Computer Science(), vol 11047. Springer, Cham. https://doi.org/10.1007/978-3-030-01132-1_6
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DOI: https://doi.org/10.1007/978-3-030-01132-1_6
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