A survey on artificial intelligence technologies in modeling of High Speed end-milling processes

High Speed Machining centers (HSM) are considered as complicated industrial instruments. Finishing is a critical process in production procedure which is carried on by these machines. Among many types of cutters, ball-nose cutters are the preferred cutters to do these kinds of operations since they have extensive operating cutting edges and appropriate geometry. The main aim of the researches on cutting process is to understand its nature better and to use this knowledge to improve the quality of the product. To achieve this goal, it is necessary to have a descriptive reference model on the process using experiments' data. Increasing demands for better surface-finishing and concurrently the development of the available measurement instruments and modeling techniques make the methods and approaches to be novel. Present paper is a survey on the lack of literature on the state-of-the-art modeling paradigms of milling processes, mainly on ball-nose cutters for surface finishing.

[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 .