A survey on artificial intelligence technologies in modeling of High Speed end-milling processes
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Li Xiang | Er Meng Joo | San Lin | Huang Sheng | Lim Beng Siong | Jose Thomas Thayil. Tijo | Zhou Junhong | Amin J. Torabi | Zhai Lianyin | Phua Si Jie | Jose Thomas Thayil Tijo | A. Torabi | L. B. Siong | Huang Sheng | Li Xiang | Zhai Lianyin | Z. Junhong | San-An Lin
[1] René David,et al. Petri nets for modeling of dynamic systems: A survey , 1994, Autom..
[2] XiaoQi Chen,et al. An experimental study of tool wear and cutting force variation in the end milling of Inconel 718 with coated carbide inserts , 2006 .
[3] Jan C. Aurich,et al. 3D Finite Element Modelling of Segmented Chip Formation , 2006 .
[4] James A. Stori,et al. A Bayesian network approach to root cause diagnosis of process variations , 2005 .
[5] Z. Kasirolvalad,et al. An intelligent modeling system to improve the machining process quality in CNC machine tools using adaptive fuzzy Petri nets , 2006 .
[6] Roger Smith,et al. Fuzzy Petri nets with neural networks to model products quality from a CNC-milling machining centre , 1996, IEEE Trans. Syst. Man Cybern. Part A.
[7] David K. Aspinwall,et al. Experimental Evaluation of Cutter Orientation When Ball Nose End Milling Inconel 718 , 2000 .
[8] Fikri Dweiri,et al. Fuzzy surface roughness modeling of CNC down milling of Alumic-79 , 2003 .
[9] Sohyung Cho,et al. Modeling tool wear progression by using mixed effects modeling technique when end-milling AISI 4340 steel , 2008 .
[10] H. Metin Ertunc,et al. Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system , 2009 .
[11] Concha Bielza,et al. A Bayesian network model for surface roughness prediction in the machining process , 2008, Int. J. Syst. Sci..
[12] M. Ortiz,et al. Modelling and simulation of high-speed machining , 1995 .
[13] Joseph C. Chen,et al. Development of a fuzzy-nets-based in-process surface roughness adaptive control system in turning operations , 2006, Expert Syst. Appl..
[14] Bor-Tsuen Lin,et al. Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm , 2009, Expert Syst. Appl..
[15] A. M. Bassiuny,et al. Flute breakage detection during end milling using Hilbert–Huang transform and smoothed nonlinear energy operator , 2007 .
[16] Ship-Peng Lo,et al. An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling , 2003 .
[17] Todd Andrew Stephenson,et al. An Introduction to Bayesian Network Theory and Usage , 2000 .
[18] Lieh-Dai Yang,et al. Fuzzy-nets-based in-process surface roughness adaptive control system in end-milling operations , 2006 .
[19] Cuneyt Oysu,et al. Drill wear monitoring using cutting force signals , 2004 .
[20] Muammer Nalbant,et al. Comparison of regression and artificial neural network models for surface roughness prediction with the cutting parameters in CNC turning , 2007 .
[21] Ying Tang,et al. HSM strategy study for hardened die and mold steels manufacturing based on the mechanical and thermal load reduction strategy , 2008 .
[22] John S. Strenkowski,et al. A finite element analysis of orthogonal rubber cutting , 2006 .