Authors: Leonarda Carnimeo, Michele Dassisti
Addresses: Dip. di Elettrotecnica ed Elettronica, Politecnico di Bari Via Orabona, 4, 70125 Bari, Italy. ' Dip. di Ingegneria Meccanica e Gestionale, Politecnico di Bari, Viale Japigia 182, 70126 Bari, Italy
Abstract: The continuous detection and correction of unnatural process behaviours, owing to special causes of variations, is a basic task in manufacturing to keep any process stable and predictable, as well as to improve it continuously. In this paper, a contribution to increase quality process control from an innovative point of view is presented, which consists in designing Cellular Neural Networks (CNNs) to behave as associative memories for recognising unnatural behaviours. As an example, a test case is developed by considering abnormal cyclic behaviours given by sinusoidal signals. For this purpose, a CNN is synthesised for an associative memory, to recognise these unnatural situations. A robustness analysis of the synthesised network is then developed in the presence of input noise. The performances of the designed circuit have been illustrated in detail.
Keywords: statistical process control; SPC; cellular neural networks; CNNs; recognition process; associative memories; quality control; sinusoidal signals; continuous innovation.
International Journal of Technology Management, 2007 Vol.37 No.1/2, pp.86 - 103
Published online: 23 Dec 2006 *Full-text access for editors Access for subscribers Purchase this article Comment on this article