Machine-part family formation with the adaptive resonance theory paradigm

Abstract

The ARTI neural network paradigm employs a heuristic where new vectors arc compared with group representative vectors for classification. ARTI is adapted for the cell formation problem by reordering input vectors and by using a better representative vector. This is validated with both test cases studied in literaure as well as synthetic matrices. Algoriihmns for effective use of ARTI are proposed. This approach is observed to produce sufficiently accurate results and is therefore promising in both speed and functionality. For the automatic generation of an optimal family formation solution a decision support system can be integrated with ARTI.