Adaptive dimensional control based on in-cycle geometry monitoring and programming for CNC turning center

This paper presents a method to compensate the dimensional deviation, irrespective of the sources for its components, and to integrate the dimensional verification and dimensional control processes. Nowadays, approach in compensation of dimensional deviation is based on decomposing the deviation. The decomposing criterion is the error source such as positioning errors, thermal deformation, mechanical loads, tool wear, kinematical errors, dynamic force, and motion control. Then, one or even more components are modeled and compensated. On contrary, the proposed method is based on the decomposing of the tool path and consideration of the batch samples. The decomposition criteria ignores the error sources as: (1) speed of variation in space of the total deviation value for the tool path decomposition and (2) the speed of variation in time of the deviation model parameters values for batch samples decomposition. The data from the geometry holistic monitoring are used for both modeling and compensation of systematic component of the total error, also for checking the compliance with technical requirements. Two algorithms for processing of the data provided by geometry monitoring, namely the adaptive–predictive algorithm and adaptive–optimal algorithm, are presented. Nine experimental batches were machined to verify the efficiency of the proposed method using various model structures and processing algorithms. The results of method application have shown a reduction of deterministic and even nondeterministic part of the total error in what concern accuracy and precision. For the entire batch, the level of remanent error is less than 5% for deterministic part, and less than 75% for nondeterministic part. These results are clearly better than the other results reported; moreover, they refer to the whole processing error and entire batch.

[1]  Changqing Liu,et al.  Off-line optimization on NC machining based on virtual machining , 2008 .

[2]  Harry H. Cheng,et al.  Integrated machining error compensation method using OMM data and modified PNN algorithm , 2006 .

[3]  Jun Ni,et al.  Adaptive model estimation of machine-tool thermal errors based on recursive dynamic modeling strategy , 2005 .

[4]  Chi Fai Cheung,et al.  A kinematics and experimental analysis of form error compensation in ultra-precision machining , 2008 .

[5]  Soichi Ibaraki,et al.  Prediction and compensation of machining geometric errors of five-axis machining centers with kinematic errors , 2009 .

[6]  Xincheng Tian,et al.  Dimensional error analysis and its intelligent pre-compensation in cnc grinding , 2008 .

[7]  Guoxiong Zhang,et al.  Error compensation of cylindrical coordinate measuring machines , 2010 .

[8]  Hao Wu,et al.  Application of ACO-BPN to thermal error modeling of NC machine tool , 2010 .

[9]  Jun Ni,et al.  A comprehensive error compensation system for correcting geometric, thermal, and cutting force-induced errors , 1997 .

[10]  Jun Ni,et al.  Dynamic neural network modeling for nonlinear, nonstationary machine tool thermally induced error , 2005 .

[11]  Shaohui Yin,et al.  Profile error compensation in ultra-precision grinding of aspheric surfaces with on-machine measurement , 2010 .

[12]  Can Çoğun,et al.  Computer-based estimation and compensation of diametral errors in CNC turning of cantilever bars , 2011, J. Intell. Manuf..

[13]  J. Jung,et al.  Machining accuracy enhancement by compensating for volumetric errors of a machine tool and on-machine measurement , 2006 .

[14]  V. S. Rao,et al.  Tool deflection compensation in peripheral milling of curved geometries , 2006 .

[15]  Y. Y. Hsu,et al.  A new compensation method for geometry errors of five-axis machine tools , 2007 .

[16]  Arvin Agah,et al.  Machine tool positioning error compensation using artificial neural networks , 2008, Eng. Appl. Artif. Intell..

[17]  W. T. Lei,et al.  NURBS-based fast geometric error compensation for CNC machine tools , 2008 .

[18]  Yang Jianguo,et al.  Thermal error optimization modeling and real-time compensation on a CNC turning center , 2008 .

[19]  Yutaka Yamagata,et al.  A study on optimal compensation cutting for an aspheric surface using the Taguchi method , 2010 .

[20]  Tae Jo Ko,et al.  On-machine measurement using a noncontact sensor based on a CAD model , 2007 .

[21]  Samir Mekid,et al.  Beyond intelligent manufacturing: A new generation of flexible intelligent NC machines , 2009 .

[22]  C. Greg Jensen,et al.  Flexible in-process inspection through direct control , 2006 .

[23]  Jianbin Xue,et al.  Deformation prediction and error compensation in multilayer milling processes for thin-walled parts , 2009 .

[24]  Shih-Ming Wang,et al.  A new high-efficiency error compensation system for CNC multi-axis machine tools , 2006 .

[25]  Robert Schmitt,et al.  Geometric error measurement and compensation of machines : an update , 2008 .

[26]  M. Kamali Nejad,et al.  Simulation of the geometrical defects of manufacturing , 2009 .

[27]  George K. Knopf,et al.  Integrated Inspection and Machining for Maximum Conformance to Design Tolerances , 2004 .