Masters Theses

Date of Award

12-2023

Degree Type

Thesis

Degree Name

Master of Science

Major

Mechanical Engineering

Major Professor

Dr. Tony Schmitz

Committee Members

Dr. Tony Schmitz, Dr. Bradley Jared, Dr. Uday Vaidya

Abstract

Robotic milling offers new opportunities for discrete part manufacturing as an alternative to milling using large conventional machine tools. The advantage of industrial robots is their large work volume, configurability, and comparatively low cost. However, robots are significantly less stiff than conventional machine tools, which can lead to poor surface finish, low machining accuracy, and low material removal rates. The purpose of this research is to predict the geometric errors, or surface location errors, that occur in a robotic mulling tool path, validate these predictions with machining tests, and compensate these errors by tool path modification. Compared with conventional machine tools, robots possess low stiffness, low frequency vibration modes and the presence of these modes causes surface location errors that are nearly independent of spindle speed in the range typically used for machining. Additionally, the robot often exhibits errors relative to the commanded tool path. By developing an understanding of both the dynamics of the robot and its tool path accuracy, predictions were made of the surface location error for a machined part and a compensation algorithm was developed. The accuracy of the predictions and compensation algorithm were verified with a series of experiments. Through this research it was determined that robotic milling is prone to large surface location errors, but it is possible to reduce these through offline compensation.

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