Artificial-neural-networks-based surface roughness Pokayoke system for end-milling operations

Surface roughness is an important indicator of the quality of machined parts. Commonly, the off-line, manual technique of direct measurement is utilized to assess surface roughness and part quality, which is found to be very time-consuming and costly. For that reason, the neural network-based surface roughness Pokayoke (NN-SRPo) system is developed to keep the surface roughness within a desired value in an in-process manner. Both the surface roughness prediction and machining parameters control are performed online during the machining process. A testing experiment demonstrated the efficacy of this NN-SRPo system.

[1]  Weiguo Gong,et al.  Monitoring of Tool Wear States in Turning Based on Wavelet Analysis , 1997 .

[2]  E. Liasi,et al.  Surface Finish Enhancement in a Turning Operation via Adaptive STR Control of the Depth of Cut , 2003 .

[3]  Xiaoli Li.  Real-Time Prediction of Workpiece Errors for a CNC Turning Centre, Part 2. Modelling and Estimation of Thermally Induced Errors , 2001 .

[4]  Xiaoli Li,et al.  Real-Time Prediction of Workpiece Errors for a CNC Turning Centre, Part 3. Cutting Force Estimation Using Current Sensors , 2001 .

[5]  Shuting Lei,et al.  Fuzzy adaptive networks in machining process modeling: surface roughness prediction for turning operations , 2004 .

[6]  Xiaoli Li.  Real-Time Prediction of Workpiece Errors for a CNC Turning Centre, Part 4. Cutting-Force-Induced Errors , 2001 .

[7]  Hun-Keun Chang,et al.  In-process surface roughness prediction using displacement signals from spindle motion , 2007 .

[8]  Joseph C. Chen,et al.  Fuzzy-nets based approach to using an accelerometer for an in-process surface roughness prediction system in milling operations , 2000, Int. J. Comput. Integr. Manuf..

[9]  Xiaoli Li.  Real-Time Prediction of Workpiece Errors for a CNC Turning Centre, Part 1. Measurement and Identification , 2001 .

[10]  Yung C. Shin,et al.  Robust Tool Wear Estimation With Radial Basis Function Neural Networks , 1995 .

[11]  S. Li,et al.  Tool Condition Monitoring in Machining by Fuzzy Neural Networks , 1996 .

[12]  Fathy Ismail,et al.  Surface topography characterization in finish milling , 1994 .

[13]  Jacob Chen,et al.  An In-Process Neural Network-Based Surface Roughness Prediction (INN-SRP) System Using a Dynamometer in End Milling Operations , 2003 .

[14]  Paul P. Lin,et al.  On-Line Free Form Surface Measurement Via a Fuzzy-Logic Controlled Scanning Probe , 1999 .

[15]  Uday S. Dixit,et al.  Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process , 2003 .

[16]  Joseph C. Chen,et al.  A fuzzy-nets in-process (FNIP) system for tool-breakage monitoring in end-milling operations , 1997 .