Abstract
In present global economy manufacturing systems be supposed to be more responsive to progressively changing, developing customer demands and diminishing product life cycles. Reconfigurable manufacturing system (RMS) is measured as an advanced manufacturing system well resourced to offer preferred functionality and capacity when exactly it needed. RMS is capable of achieving beneficial characteristics of both Flexible Manufacturing system (FMS) and dedicated manufacturing system (DMS). Reconfigurable machines (RM) are designed after formation of part family considering manufacturing operation need to perform on job. The learning of RMS is in incubation period hence; cost effective implementation of RMS in real life depends upon best set of part family formation. In this paper part family is formed for variety of gears and eccentric pin having variations in their geometry and manufacturing process. Part family is formed by using operation sequence dependent BMIM (by passing moves and idle machines) similarity coefficient and Average linkage clustering (ALC) method is applied to group part in family. Dendogram formed for part family shows percentage of similarity between parts; so the families of parts processing with use of RMS gets benefits of similarity in expressions of reduction in set up time on hobbing machines, cost related with designing fixtures and tools.
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