Skip to main content

Feature-based memory association for group technology

Buy Article:

$63.00 plus tax (Refund Policy)


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. The FBMAN-I system uses autoassociative memory and relationships between features and parts to cluster parts into families. It also overcomes the problems caused by exceptional parts and the presentation order of seed parts. The FBMAN-II system uses heteroassociative memory and relationship between parts features and machines to form machine cells without routeing sheets being provided. The FBMAN-III system, extended from the FBMAN-II system, can form multiple-application sets at the same time. A case study shows that the FBMAN-III system can also perform part information retrieval, similar part retrieval and new part assignment with autoassociative memory.

Document Type: Research Article


Publication date: 1998-06-01

More about this publication?
  • Access Key
  • Free ContentFree content
  • Partial Free ContentPartial Free content
  • New ContentNew content
  • Open Access ContentOpen access content
  • Partial Open Access ContentPartial Open access content
  • Subscribed ContentSubscribed content
  • Partial Subscribed ContentPartial Subscribed content
  • Free Trial ContentFree trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more