Parametric modeling of edge effects for polishing tool influence functions

Area of Science:

• Materials Science and Engineering
• Manufacturing Processes
• Computational Modeling

Background:

• Accurate modeling of the tool influence function (TIF) is crucial for precision computer-controlled polishing.
• Existing linear Preston's models struggle with nonlinear material removal at workpiece edges.
• Predicting edge TIF is challenging due to complex removal dynamics.

Purpose of the Study:

• To develop a novel parametric model for predicting the edge TIF in computer-controlled polishing.
• To overcome limitations of linear models in edge polishing scenarios.
• To enhance the accuracy of material removal predictions at workpiece boundaries.

Main Methods:

• Developed a new 5-parameter parametric model.
• Fitted the model to measured data from polishing experiments.
• Validated the model for both spinning and orbiting polishing tools at edges.

Main Results:

• The proposed parametric model accurately predicts the edge TIF.
• The model effectively captures nonlinear material removal behavior at workpiece edges.
• Achieved high prediction accuracy for various edge polishing conditions.

Conclusions:

• The new parametric model provides a robust solution for edge TIF prediction in computer-controlled polishing.
• This advancement enables more precise manufacturing of components with complex geometries.
• Improved edge TIF modeling contributes to enhanced surface finish and dimensional accuracy.