Dynamic grouping of parts in flexible manufacturing systems – A self-organizing neural networks approach

Dynamic grouping of parts in flexible manufacturing systems – A self-organizing neural networks approach

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Article ID: iaor19981789
Country: Netherlands
Volume: 84
Issue: 1
Start Page Number: 192
End Page Number: 212
Publication Date: Jul 1995
Journal: European Journal of Operational Research
Authors: ,
Keywords: production: FMS
Abstract:

Artificial Intelligence (AI) has recently been recognized as a worthwhile tool for supporting manufacturing operations. This paper reviews AI-related approaches to Group Technology (GT) and presents the Self-Organizing Map (SOM) network, a special type of neural networks, as an intelligent tool for grouping parts and machines. SOM can learn from complex, multi-dimensional data and transform them into visually decipherable clusters. What sets this technique apart from others in GT is that SOM offers the flexibility of choosing from multiple grouping alternatives. SOM can be used in a dynamic situation where quick response to changes in part designs, process plans, or manufacturing conditions is essential, and thus it can be more easily integrated into a Flexible Manufacturing System. The paper proposes a framework of an intelligent system that integrates the neural networks approach and a knowledge-based system to provide decision supporting functions.

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