Feature-based memory association for group technology

Abstract

This paper presents a new approach for group technology (GT) part family formation and multiple-application set formation using feature-based memory association performed by neural networks. The drawbacks of two GT approaches, production flow analysis (PFA) and coding and classification systems (CCS) are discussed. CCS is useful for similar part retrieval and new part assignment but not adequate for forming machine cells. PFA can form part families and machine cells simultaneously; however, routeing sheets are required and PFA does not provide specific methods for part information retrieval. These drawbacks are rooted in the fact that CCS depends on the relationships between parts and features and PFA relies on the relationships between parts and machines. The presented approach emphasizes the relationships between parts, features and machines together and incorporates memory association into family and cell formation. The feature-based memory association networks (FBMAN) system contains three sub-systems...