Measuring Data Based Non-linear Error Modeling for Parallel Machine Tool

By converting a nonlinear problem to a linear one by means of the least square fit, a nonlinear error modeling method based on measuring data is presented. Combined with an example, some key items are pointed out during modeling. The simulation results on a parallel machine tool show that the model based on the method is of high accuracy and the error modeling method is correct and reliable. No matter what the error parameter of position and orientation is, the ideal error model would be obtained by means of the method. The accuracy of a parallel machine tool can be raised greatly by using the model to compensate the position and orientation.

Authors

• Xiaoliu Yu
• Mingyang Zhao
• Lijin Fang
• Honggua Wang
• Qiyi Wang

Keywords

• Parallel machines
• Error correction
• Machine tools
• Least squares methods
• Computer errors
• Parameter estimation
• Polynomials
• Automation
• Kinematics
• Calibration
• Nonlinear Model
• Error Model
• Machine Tool
• Simulation Results
• Nonlinear Problem
• Least-squares Fitting
• Nonlinear Method
• Key Items
• Model Parameters
• Accuracy Of Model
• Real-valued
• Variable Positions
• Parameter Identification
• Fitting Method
• Coordinate Transformation
• Coordinate Origin
• Original Parameters
• Accurate Point
• Link Model
• Least Square Fitting Method
• Symmetric Boundary
• Original Boundary