Application of connectionist learning methods to manufacturing process monitoring

It is demonstrated that connectionist learning networks can monitor manufacturing processes to determine causal relationships with an accuracy competitive with that of conventional statistical techniques. Moreover, the network operates online, in realtime, and with substantial savings in computational complexity as compared with conventional CIM techniques. Two approaches are compared. One employs standard procedures to find correlations between sensor measurements and quality. The sensor data from the production line are collected over a period of time, and correlations are made offline at infrequent intervals using analyses such as linear regression. The second approach is to estimate the correlations incrementally, as the data are collected, online and in real-time. The estimates are updated incrementally using connectionist learning procedures. Simulation results are presented for a fluorescent bulb manufacturing line