Abstract
This paper proposes a neurogenetic-based optimization scheme for predicting localized stable cutting parameters in inward turning operation. A set of cutting experiments are performed in inward orthogonal turning operation. The cutting forces, surface roughness, and critical chatter locations are predicted as a function of operating variables including tool overhang length. Radial basis function neural network is employed to develop the generalization models. Optimum cutting parameters are predicted from the model using binary-coded genetic algorithms. Results are illustrated with the data corresponding to four work materials, i.e., EN8 steel, EN24 steel, mild steel, and aluminum operated over a high speed steel tool.
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Rao BC, Shin YC (1999) A comprehensive dynamic cutting force model for chatter prediction in turning. Int J Mach Tools Manuf 39:1631–1654
Chiou RY, Liang SY (1998) Chatter stability of a slender cutting tool in turning with tool wear effect. Int J Mach Tools Manuf 38:315–327
Chandiramani NK, Pothala T (2006) Dynamics of 2-DOF regenerative chatter during turning. J Sound Vib 290:448–464
Chen CK, Tsao TS (2006) A stability analysis of turning a tailstock supported flexible work-piece. Int J Mach Tools Manuf 46:18–25
Berardos PG, Mosialos S, Vosniakos GC (2006) Prediction of workpiece elastic deflections under cutting forces in turning. Robot Comput Integr Manuf 22:505–514
Martínez JC, Ruiz CJ, Guzman AL (2008) Analysis of compliance between the cutting tool and the workpiece on the stability of a turning process. Int J Mach Tools Manuf 48:1054–1062
Azouzi R, Guillot M (1997) Online prediction of surface finish and dimensional deviation in turning using neural network-based sensor fusion International. J Mach Tools Manuf 37:1201–1217
Risbood KA, Dixit US, Sahasrabudhe AD (2003) Prediction of surface finish and dimensional deviation by measuring cutting forces and vibrations in turning process. J Mater Process Technol 132:203–214
Tangjitsitcharoen S, Moriwaki T (2007) Intelligent identification of turning process based on pattern recognition of cutting states. J Mater Process Technol 192:491–496
Dhar NR, Kamruzzaman AM (2006) Effect of minimum quantity lubrication (MQL) on tool wear and surface roughness in turning AISI-4340 steel. J Mater Process Technol 172:299–304
Gaitonde VN, Karnik SR, Davim JP (2008) Selection of optimum MQL and cutting conditions for enhancing machinability in turning of brass. J Mater Process Technol 204:459–464
Sekar M, Srinivas J, Rama Kotaiah K, Yang SH (2008) Stability analysis of turning process with tailstock-supported workpiece. Int J Adv Manuf Technol. doi:10.1007/s00170-008-1764-2
Jiao Y, Lei S, Pei ZJ, Lee ES (2006) Fuzzy adaptive networks in machining process modeling: surface roughness prediction in turning operations. Int J Mach Tools Manuf 44:1643–1651
Abburi NR, Dixit US (2006) A knowledge-based system for the prediction of surface roughness in turning process. Robot Comput Integr Manuf 22:363–372
Dhokia VR, Kumar S, Vichare P, newman ST, Allen RD (2008) Surface roughness prediction model for CNC machining of polypropylene. Proc Inst Mech E Part B J Eng Manuf 222:137–157
Lu C (2008) Study on prediction of surface quality in machining process. J Mater Process Technol 205:439–450
Cardi AA, Firpi HA, Bement MT, Liang SY (2008) Workpiece dynamic analysis and prediction during chatter of turning process. Mech Syst Signal Process 2:1481–1494
Haykin S (2001) Neural networks, a comprehensive foundation. Tsinghua University Press, Beijing
Goldberg D (1989) Genetic algorithms in search-optimization and machine learning. Addison-Wesley, Readings
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Rama Kotaiah, K., Srinivas, J. & Sekar, M. Prediction of optimal stability states in inward-turning operation using neurogenetic algorithms. Int J Adv Manuf Technol 45, 679–689 (2009). https://doi.org/10.1007/s00170-009-2007-x
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DOI: https://doi.org/10.1007/s00170-009-2007-x