Manufacturing process monitoring using neural networks

Effective automatic control in manufacturing processes depends on a properly designed and implemented computerized monitoring system. In this paper, a monitoring system designed for identifying both periodic and aperiodic process signals using neural networks is reported. Digital signal processing techniques are first used to convert collected manufacturing signals into frequency domain. Then a neural network-based program is used to identify these signals by examining their characteristic frequencies. Implementation of neural networks in program logic and the system's computational properties are discussed. The promising results demonstrated by application examples show that the neural network-based system seems to have a good potential in automatic manufacturing process control.