Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

A Knowledge-Based Inspection Workstation

Published

Author(s)

Elena R. Messina, John A. Horst, Thomas R. Kramer, Hui-Min Huang, Tsung-Ming Tsai, E Amatucci

Abstract

We are building an inspection workstation development environment to use as a testbed for understanding what types of knowledge, e.g., data, algorithms, and processes, can increase the productivity of inspection operations. Inspection can be more efficient through reducing the need for fixturing, integrating the generation of process plans and their execution within the controller, and reducing the errors or data losses that occur by translating the models to different formats. Initial configuration of inspection systems can be less costly through the use of open architectures that are constructed from components. Key elements of our work include in situ feature-based planning, vision-driven part pose estimation, and software methods to facilitate construction of manufacturing controllers. These provide a rich environment in which to study the categories of knowledge that are useful in intelligent control of inspection workstations. This paper describes our vision, approach, and preliminary results.
Proceedings Title
Proceedings of Intelligence in Automation & Robotics Symp.
Conference Dates
October 31-November 3, 1999
Conference Location
Bethesda, MD
Conference Title
Intelligence in Automation & Robotics Symp.

Keywords

Knowledge Engineering, Robotics & Intelligent Systems, knowledge-based, inspection systems, process planning, feature-based

Citation

Messina, E. , Horst, J. , Kramer, T. , Huang, H. , Tsai, T. and Amatucci, E. (1999), A Knowledge-Based Inspection Workstation, Proceedings of Intelligence in Automation & Robotics Symp., Bethesda, MD, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=820644 (Accessed August 1, 2021)
Created November 3, 1999, Updated February 17, 2017