Neural Network System for Manufacturing Assembly Line Inspection

Abstract

Assembly line inspection is currently performed for General Motor's clutch drivers by means of a vision system. When the part is changed, the system must be reprogrammed, which takes time and is expensive. A new system has been developed and demonstrated in the Computer Science and Engineering Department at Wright State University that permits an operator to teach the system what is to be considered good and bad without any need for computer reprogramming. The machine is shown good parts and flawed parts. In the latter case, the type of flaw is entered in the computer. Preprocessing is used to provide position and rotation invariance. A feedforward network is then trained to provide the correct output. The system is shown to perform reliably and has been modified to cope with more difficult inspection systems in which back lighting may not be used.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1990
Accession Number
ADA578297

Entities

People

  • Alastair D. Mcaulay
  • Devert Wicker
  • Paul Danset

Organizations

  • Wright Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Assembly Lines
  • Change Detection
  • Computer Science
  • Computers
  • Demonstrations
  • Engineering
  • Extraction
  • Feature Extraction
  • Inspection
  • Learning
  • Neural Networks
  • Pattern Recognition
  • Recognition
  • Rotation
  • Signal Processing
  • Training

Readers

  • Computational Modeling and Simulation
  • Computer Science.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks