Integrated Genetic Programming and Genetic Algorithm Approach to Predict Surface Roughness

References

• Lou, S. Development of Four In-process Surface Recognition Systems to Predict Surface Roughness in End Milling. Ph.D. thesis, Iowa State University, Iowa, 1997
   Google Scholar
• Lou , S.M. , Chen , C.J. and Li , M.C. 1998 . Surface roughness prediction technique for CNC end-milling . J. Ind. Tech. , 15 ( 1 ) : 1 – 6 .
   Google Scholar
• Huang , L. and Chen , J.C. 2001 . A multiple regression model to predict in-process surface roughness in turning operation via accelerometer . J. Ind. Tech. , 17 ( 2 ) : 1 – 8 .
   Google Scholar
• Abouelatta , O.B. and Mádl , J. 2001 . Surface roughness prediction based on cutting parameters and tool vibrations in turning operations . J. Mat. Proc. Tech. , 118 ( 1–3 ) : 269 – 277 .
   Web of Science ®Google Scholar
• Fuh , K.H. and Wu , C.F. 1995 . A proposed statistical model for surface quality prediction in end-milling of Al Alloy . Internat. J. Machine Tools and Manufacture , 35 ( 8 ) : 1187 – 1200 .
   Web of Science ®Google Scholar
• Chen , J.C. and Lou , M.S. 2000 . Fuzzy-nets based approach using an accelerometer for in-process surface roughness prediction system in milling operations . J. Computer Integrated Manufacturing Systems , 13 ( 4 ) : 358 – 368 .
   Web of Science ®Google Scholar
• Ali , Y. and Zhang , L. 1999 . Surface roughness prediction of ground components using a fuzzy logic approach . J. Mat. Proc. Tech. , 89–90 ( 0 ) : 561 – 568 .
   Google Scholar
• Chen , J. 2000 . “ Neural networks and neural-fuzzy approaches in an in-process surface roughness recognition system for end milling operations ” . In Computational Intelligence in Manufacturing Handbook Edited by: Wang , J. and Kusiak , A. Washington : CRC Press .
   Google Scholar
• Suresh , P.V.S. , Venkateswara Rao , P. and Deshmukh , S.G. 2002 . A genetic algorithmic approach for optimization of surface roughness prediction model . Internat. J. Machine Tools and Manufacture , 42 ( 6 ) : 675 – 680 .
   Web of Science ®Google Scholar
• Bäck , T. , Hammel , U. and Schwefel , H.-P. 1997 . Evolutionary computation: Comments on the history and current state . IEEE Transaction on Evolutionary Computation , 1 ( 1 ) : 3 – 17 .
   Google Scholar
• Koza , J.R. 1994 . Genetic Programming II Massachusetts : The MIT Press .
   Google Scholar
• Koza , J.R. 1999 . Genetic Programming III San Francisco, CA : Morgan Kaufmann .
   Google Scholar
• Holland , J. 1992 . Adaptation in Natural and Arificial Systems Massachusetts : The MIT Press .
   Google Scholar
• Gen , M. and Cheng , R. 1997 . Genetic Alghoritms and Engineering Design Canada : John Wiley & Sons .
   Google Scholar
• Mitchell , T.M. 1997 . Machine Learning McGraw-Hill .
   Google Scholar
• Brezocnik , M. and Balic , J. 2001 . A genetic-based approach to simulation of self-organizing assembly . Robotics and Computer Integrated Manufacturing , 17 ( 1–2 ) : 113 – 120 .
   Google Scholar
• Brezocnik , M. , Balic , J. and Kuzman , K. 2002 . Genetic programming approach to determining of metal materials properties . J. Intelligent Manufacturing , 13 ( 1 ) : 5 – 17 .
   Google Scholar
• Dobnik-Dubrovski , P. and Brezocnik , M. 2002 . Using genetic programming to predict the macroporosity of woven cotton fabrics . Textile Research Journal , 72 ( 3 ) : 187 – 194 .
   Google Scholar
• Kalpakjian , S. and Schmid , S.R. 2001 . Manufacturing Engineering and Technology New Jersey : Prentice Hall .
   Google Scholar
• Dong , Y.J. , Young-Gu , C. , Hong-Gil , K. and Hsiao , A. 1996 . Study of the correlation between surface roughness and cutting vibrations to develop an on-line roughness measuring technique in hard turning . Internat. J. Machine Tools and Manufacture , 36 ( 4 ) : 453 – 464 .
   Google Scholar