Abstract
In spite of advantaged machining technology, there are still some vibrations and heating problems of drill machines during drilling work pieces. This experimental and simulation investigation is focused on drilling condition of drill column machines system’s performance using neural network based approach for plastic material and different feed rates under different working speeds. Firstly, the system is tested with plastic material for different drilling speeds and feed rates. Moreover, different regions of the system were measured with vibration measuring system. Secondly, the experimentally measured vibration and acceleration parameters were predicted with two types of neural network predictors. The result were improved that neural networks can be used as predictor such systems in real time applications during drilling process.
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References
Quintana G, Ciurana J (2011) Chatter in machining processes: a review. Int J Mach Tools Manuf 51:363–376
Altintas Y, Budak E (1995) Analytical prediction of stability lobes in milling. CIRP Ann Manuf Technol 44:357–362
Schmitz T, Davies M, Kennedy M (2001) Tool point frequency response prediction for high speed machining by RCSA. J Manuf Sci Eng 123(4):700–707
Zhang J, Schmitz T, Zhoa W, Lu B (2011) Receptance coupling for tool point dynamics prediction on machine tools. Chin J Mech Eng 24:1–6
Erturk A, Özgüven HN, Budak E (2006) Analytical modeling of spindle–tool dynamics on machine tools using Timoshenko beam model and receptance coupling for the prediction of tool point FRF. Int J Mach Tools Manuf 46:1901–1912
Pedrammehr S, Farrokhi H, Khani Sheykh Rajab A, Pakzad S, Mahboubkhah M, Ettefagh MM, Sadeghi MH (2012) Modal analysis of the milling machine structure through FEM and experimental test. Adv Mater Res 383–390:6717–6721
Wu Z, Xu C, Zhang J, Yu D, Feng P (2010) Modal and harmonic response analysis and evaluation of machine tools. In: International conference on digital manufacturing & automation, pp 928–935
Eski İ (2012) Vibration analysis of drilling machine using proposed artificial neural network predictors. J Mech Sci Technol 26(10):3037–3046
Esim E, Yıldırım Ş (2016) Drilling performance analysis of drill column machine using proposed neural networks. Neural Comput Appl 1–12. doi:10.1007/s00521-016-2322-8
Rao SS (2004) Mechanical vibrations, 4th edn. Pearson Education Inc., Upper Saddle River
Eski İ, Erkaya S, Savaş S, Yildirim Ş (2011) Fault detection on robot manipulators using artificial neural networks. Robot Comput Integr Manuf 27:115–123
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Yıldırım, Ş., Esim, E. (2018). Design of Neural Network Predictor for Vibration Analysis of a Drill Column Machine During Drilling Plastic Work-Pieces. In: Dede, M., İtik, M., Lovasz, EC., Kiper, G. (eds) Mechanisms, Transmissions and Applications. IFToMM 2017. Mechanisms and Machine Science, vol 52. Springer, Cham. https://doi.org/10.1007/978-3-319-60702-3_28
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DOI: https://doi.org/10.1007/978-3-319-60702-3_28
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