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Accuracy Degradation Analysis for Industrial Robot Systems

[+] Author Affiliations
Guixiu Qiao, Brian A. Weiss

National Institute of Standards and Technology, Gaithersburg, MD

Paper No. MSEC2017-2782, pp. V003T04A006; 9 pages
doi:10.1115/MSEC2017-2782
From:
  • ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing
  • Volume 3: Manufacturing Equipment and Systems
  • Los Angeles, California, USA, June 4–8, 2017
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5074-9

abstract

As robot systems become increasingly prevalent in manufacturing environments, the need for improved accuracy continues to grow. Recent accuracy improvements have greatly enhanced automotive and aerospace manufacturing capabilities, including high-precision assembly, two-sided drilling and fastening, material removal, automated fiber placement, and in-process inspection. The accuracy requirement of those applications is primarily a function of two main criteria: (1) The pose accuracy (position and orientation accuracy) of a robot system’s tool center position (TCP), and (2) the ability of a robot system’s TCP to remain in position or on-path when loads are applied. The degradation of a robot system’s tool center accuracy can lead to a decrease in manufacturing quality and production efficiency. Given the high output rate of production lines, it is critical to develop technologies to verify and validate robot systems’ health assessment techniques, particularly the accuracy degradation. In this paper, the National Institute of Standards and Technology’s (NIST) effort to develop the measurement science to support the monitoring, diagnostics, and prognostics (collectively known as prognostics and health management (PHM)) of robot accuracy degradation is presented. This discussion includes the modeling and algorithm development for the test method, the advanced sensor development to measure 7-D information (time, X, Y, Z, roll, pitch, and yaw), and algorithms to analyze the data.

Topics: Robots

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