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

  • Authors:

  • Guilian Wang;Yiqiang Wang;Ji Zhao;Guiliang Chen

  • Affiliations:

  • College of Mechanical Engineering, Jilin University, Changchun, China 130025 and College of Mechanical Engineering, Jiamusi University, Jiamusi, China 154007;College of Mechanical Engineering, Jilin University, Changchun, China 130025 and Ningbo Institute of Technology, Zhejiang University, Ningbo, China 315100;College of Mechanical Engineering, Jilin University, Changchun, China 130025;College of Mechanical Engineering, Jilin University, Changchun, China 130025

  • Venue:
  • Journal of Intelligent Manufacturing
  • Year:
  • 2012

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Abstract

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.