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The research about micromachining accuracy control based on fuzzy evaluation algorithm
Author(s) -
Qiao Jian,
Lu Qinghua,
Zhang Wenbao,
Xue Xiangyao
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4745
Subject(s) - machining , surface micromachining , fuzzy logic , computer science , nonlinear system , algorithm , field (mathematics) , fuzzy control system , artificial neural network , control engineering , artificial intelligence , engineering , mechanical engineering , mathematics , medicine , physics , alternative medicine , pathology , quantum mechanics , fabrication , pure mathematics
Summary The calculation method of nonlinear artificial neural network based on fuzzy evaluation algorithm is introduced. This method is used to evaluate the precision of micro machined machining and to find the influence factors of the precision of micromachining. The traditional machining precision control mainly adopts the linear control strategy, but the micromachining involves the mechanical characteristics of the micro field, and many linear machine adding precision control methods cannot effectively control the precision of the micromachining. Using fuzzy evaluation method, large data cluster training method can be used to fit the control curve of micromachining accuracy. The nonlinear mapping of the fuzzy evaluation algorithm is realized by the large sample training method, and the objective and effective parameter evaluation and precision control of the machining precision in the micro machined field are realized. It is proved that the fuzzy evaluation algorithm can accurately evaluate the precision of micro machined machining. Learning through large data samples can effectively improve the topology practicability and rationality of the algorithm.