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2003•中国机械工程学报Requires access

MODELING AND COMPENSATION TECHNIQUE FOR THE GEOMETRIC ERRORS OF FIVE－AXIS CNC MACHINE TOOLS

WangShuxin, YunJintian, ZhangZhifei, LiuYouwu, ZhangQing

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Abstract

One of the important trends in precision machining is the development of real-time error compensation technique.The error compensation for multi-axis CNC machine tools is very difficult and attractive.The modeling for the geometric error of five-axis CNC machine tools based on multi-body systems is proposed.And the key technique of the compensation-identifying geometric error parameters-is developed.The simulation of cutting workpiece to verify the modeling based on the multi-body systems is also considered.

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What this paper is about

One of the important trends in precision machining is the development of real-time error compensation technique.The error compensation for multi-axis CNC machine tools is very difficult and attractive.The modeling for the geometric error of five-axis CNC machine tools based on multi-body systems is proposed.And the key technique of the compensation-identifying geometric error parameters-is developed.The simulation of cutting workpiece to verify the modeling based on the multi-body systems is also considered.

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Available abstract

One of the important trends in precision machining is the development of real-time error compensation technique.The error compensation for multi-axis CNC machine tools is very difficult and attractive.The modeling for the geometric error of five-axis CNC machine tools based on multi-body systems is proposed.And the key technique of the compensation-identifying geometric error parameters-is developed.The simulation of cutting workpiece to verify the modeling based on the multi-body systems is also considered.

Key concepts: Compensation (psychology), Numerical control, Machining, Machine tool, Computer science, Geometric modeling, Key (lock), Control engineering

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MODELING AND COMPENSATION TECHNIQUE FOR THE GEOMETRIC ERRORS OF FIVE－AXIS CNC MACHINE TOOLS — Research Paper | ScholarLens