Abstract

Since the advent of robots, many tasks that were originally performed by humans have now been tasked to industrial robots. From a manufacturing standpoint, robots have primarily been used in pick-and-place or other non-machining operations that require high repeatability. However, with the increasing availability of CAD/CAM software and the development of high-precision metrology, comes the opportunity to integrate robots into a wider variety of manufacturing processes through the use of feedback control. One such machining operation that is being explored is precision grinding of metal parts. Most other work in this area has focused on force regulation to improve grind quality; however, this paper takes a different approach. In this work, an Iterative Learning Control (ILC) algorithm is implemented to correct the geometric error directly by altering the toolpath trajectory. Specifically, in this framework, a conservative initial cutting trajectory is implemented using a 6-DoF robotic grinding system, and the resulting part geometry is measured via a high-precision laser scanner. Based on the resultant geometric error, the toolpath is corrected and then rerun on the part. This process is then repeated iteratively until sufficient accuracy is achieved. Due to the inability to replace material in overground regions, the controller is designed with an emphasis on reducing overshoot which cannot be corrected. The controller is experimentally validated by grinding an elliptical pocket which meets FAA specifications for corrosion removal in aircraft. The results showed that within seven iterations the entire error surface could be brought to a tolerance of ±0.150 mm for the given geometry.

Department(s)

Mechanical and Aerospace Engineering

Keywords and Phrases

Iterative learning control; Precision machining; Robotic grinding; Spatial feedback control

International Standard Serial Number (ISSN)

2213-8463

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Elsevier, All rights reserved.

Publication Date

01 Oct 2024

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