Process optimization of the serial-parallel hybrid polishing machine tool based on artificial neural network and genetic algorithm

A kind of serial-parallel hybrid polishing machine tool based on the elastic polishing theory is developed and applied to finish mould surface with using bound abrasives. It mainly consists of parallel mechanism of three dimensional moving platform, serial rotational mechanism of two degrees of freedom and the elastic polishing tool system. The active compliant control and passive conformity of polishing tool are provided by a pneumatic servo system and a spring, respectively. Considering the contradiction between the machining quality and efficiency, the optimization model of process parameters is found according to different machining requirements, namely single objective optimization and multi-objective optimization, which provide a choice of parameters as a basis for the operators in practice. Many polishing experiments are conducted to collect the data samples. The genetic algorithm integrated with artificial neural network is used for researching for the optimal process parameters in term of the various optimization objectives. This research also lays the foundation for further establishing polishing expert system.

[1]  Jung-Hwan Ahn,et al.  Development of a sensor information integrated expert system for optimizing die polishing , 2001 .

[2]  F. Xi,et al.  Modeling surface roughness in the stone polishing process , 2005 .

[3]  Angelos P. Markopoulos,et al.  Artificial neural network models for the prediction of surface roughness in electrical discharge machining , 2008, J. Intell. Manuf..

[4]  Fengfeng Xi,et al.  Modeling and control of automated polishing/deburring process using a dual-purpose compliant toolhead , 2008 .

[5]  Engelbert Westkämper,et al.  Computer-assisted manufacturing process optimization with neural networks , 1998, J. Intell. Manuf..

[6]  Christian Brecher,et al.  NURBS Based Ultra-Precision Free-Form Machining , 2006 .

[7]  Behnam Malakooti,et al.  An interactive multi-objective artificial neural network approach for machine setup optimization , 2000, J. Intell. Manuf..

[8]  L. Guvenç,et al.  An overview of robot-assisted die and mold polishing with emphasis on process modeling , 1997 .

[9]  Hon-Yuen Tam,et al.  An investigation of material removal in polishing with fixed abrasives , 2002 .

[10]  Gianpaolo Savio,et al.  A surface roughness predictive model in deterministic polishing of ground glass moulds , 2009 .

[11]  F. Erzincanli,et al.  Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm , 2006 .

[12]  Yoshihiro Kita,et al.  New polishing technology of free form surface by GC , 2007 .

[13]  T. Huang,et al.  Conceptual design and dimensional synthesis for a 3-DOF module of the TriVariant-a novel 5-DOF reconfigurable hybrid robot , 2005, IEEE Transactions on Robotics.

[14]  Min Cheol Lee,et al.  Intelligently automated polishing for high quality surface formation of sculptured die , 2002 .

[15]  Juan J. Marquez,et al.  Process modeling for robotic polishing , 2005 .

[16]  George-Christopher Vosniakos,et al.  Predicting surface roughness in machining: a review , 2003 .

[17]  Mitsuo Gen,et al.  Adaptive genetic algorithm for advanced planning in manufacturing supply chain , 2006, J. Intell. Manuf..

[18]  Tung-Hsu Hou,et al.  Parameters optimization of a nano-particle wet milling process using the Taguchi method, response surface method and genetic algorithm , 2007 .

[19]  Fusaomi Nagata,et al.  CAD/CAM-based position/force controller for a mold polishing robot , 2007 .

[20]  Norhashimah Morad,et al.  Genetic algorithms in integrated process planning and scheduling , 1999, J. Intell. Manuf..

[21]  George Q. Huang,et al.  Case-based polishing process planning with Fuzzy Set Theory , 2010, 2008 IEEE International Conference on Industrial Engineering and Engineering Management.

[22]  George-Christopher Vosniakos,et al.  Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments , 2002 .

[23]  Yung C. Shin,et al.  Modeling of Complex Manufacturing Processes by Hierarchical Fuzzy Basis Function Networks With Application to Grinding Processes , 2004 .

[24]  C. Czenkusch,et al.  Modelling and optimization of grinding processes , 1998, J. Intell. Manuf..

[25]  Yanwen Li.  Theoretical study on a non-transmission high efficient parallel camber grinding machine , 2002 .

[26]  Tomonari Furukawa,et al.  Automated polishing of an unknown three-dimensional surface , 1996 .

[27]  Christophe Tournier,et al.  Automatic polishing process of plastic injection molds on a 5-axis milling center , 2009, 1003.5062.

[28]  M Yu,et al.  Study on the dynamic characteristics of a virtual-axis hybrid polishing machine tool by flexible multibody dynamics , 2004 .

[29]  Davood Afshari,et al.  Creep feed grinding optimization by an integrated GA-NN system , 2010, J. Intell. Manuf..

[30]  Qian Li,et al.  Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method , 2007 .

[31]  Ming-June Tsai,et al.  Efficient automatic polishing process with a new compliant abrasive tool , 2006 .