Neural network system for manufacturing assembly line inspection

A new assembly line inspection system that permits an operator to teach the system what is to be considered good and bad without any need for computer reprogramming is developed and demonstrated. The feasibility of using neural networks combined with a simple feature extraction algorithm to make visual inspection systems which learn is demonstrated. The demonstration system can separate round parts in the class of problems which have all of the required information in a circular band concentric to the center of the part and which have visually detectable features. The machine is shown to have 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