Neural Network System for Manufacturing Assembly Line Inspection

reportActive / Technical Report | Accession Number: ADA578297 | Open PDF

Abstract:

Assembly line inspection is currently performed for General Motors 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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