DEVELOPING MEASUREMENT SCIENCE TO VERIFY AND VALIDATE THE IDENTIFICATION OF ROBOT WORKCELL DEGRADATION

Published: June 14, 2019

Author(s)

Brian A. Weiss

Abstract

Robot systems have become more prevalent in manufacturing operations as the technology has become more accessible to a wider range of manufacturers, especially small to medium-sized organizations. Although these technologies have become more affordable, easier to integrate, and greater in functional capable, these advanced systems increase workcell complexity leading to the presence of more fault and failure modes. Given increasing manufacturing competitiveness, maximizing asset availability, and maintaining desired quality and productivity targets have become essential. The National Institute of Standards and Technology (NIST) is developing measurement science (e.g., test methods, performance metrics, reference data sets) to monitor the degradation within a manufacturing workcell that includes a six-degree-of-freedom robot arm. Numerous components of the workcell influence the accuracy of the robot's tool center position. Identifying the component(s) responsible for process degradation prior to the process performing out of specification will provide manufacturers with advanced intelligence to maintain or maximize their performance targets and asset availability. NIST's research in robot workcell health fills a gap to promote workcell component health identification and develops methods and tools to verify and validate this approach. This paper presents the overall research plan and the efforts to date in developing appropriate test methods, identifying key sources of workcell degradation, and presenting baseline performance data that is leveraged for health assessment.
Proceedings Title: Proceedings of the ASME 2019 14th International Manufacturing Science and Engineering Conference (MSEC2019)
Conference Dates: June 10-14, 2019
Conference Location: Erie, PA
Pub Type: Conferences

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Keywords

condition monitoring, degradation, diagnostics, industrial robot systems, kinematics, manufacturing processes, manufacturing systems, prognostics, testbed, use cases, workcell
Created June 14, 2019, Updated February 26, 2019