A NOVEL TWO-MODE INTEGRAL APPROACH FOR THERMAL ERROR MODELING IN CNC MILLING-TURNING MACHINING CENTER

Abstract : Thermal errors have the largest contribution, as much as about 70%, to the machining inaccuracy of computer-numerical-controlled (CNC) machining centers. The error compensation method so far plays the most popular and effective way to minimize the thermal error. How to accurately and quickly build an applicable thermal error model (TEM) is the kernel work of thermal error compensation. On the basis of some comprehensive machine-learning schemes, past proposed TEMs had impressive merits for dealing with the thermal error modeling of single-function (milling or turning cutting) machine tools with only considering one set of thermal key points
 EXISTING SYSTEM :
 ? In this paper, the existing thermal error modeling methods for machine tools that have been researched and applied in the past ten years are introduced, classified, and summarized. ? considering that the solution of the existing gray prediction model GM(1,N) was not accurate enough, Tien [40] used gray control parameters to develop an improved model GMC(1,N) on the basis of GM(1,N). ? Hunan University devoted to solve the problem of low modeling efficiency of the existing thermal error modeling methods and unsatisfactory model prediction accuracy
 DISADVANTAGE :
 In order to solve these problems, Ma and Jiang used a hybrid particle swarm algorithm to optimize the structure of the BP neural network and to avoid unstable prediction performance. ? In order to solve the problem that the BP neural network cannot adjust independent parameters of new data in thermal error modeling, ? Hunan University devoted to solve the problem of low modeling efficiency of the existing thermal error modeling methods and unsatisfactory model prediction accuracy
 PROPOSED SYSTEM :
 ?? Indonesian scholars Chen and Hsu proposed a time-variant volumetric error model to comprehensively compensate the geometric and thermal errors of the machining center. ? proposed a new gray GMC(1,N) machine tool thermal error prediction method based on the CS algorithm named as CS-GMC(1,N) model ? They proposed a generalized RBF neural network modeling method and applied it to the thermal error modeling of the spindle box of a NC guide rail grinder
 ADVANTAGE :
 ? Thermal error caused by thermal deformation is one of the most significant factors influencing the accuracy of the machine tool. ? Two-step method” to establish the thermal error model of a ball screw. In this method, BP neural network was first used to model the thermal deformation of the screw. ? key feature functions are more difficult to be extracted than the BP neural network, and its generalization performance is wors. Su et al. used the RBF neural network to build a more accurate thermal error model.
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