A systematic methodology for automatic machining operation clustering is an important component in Reconfigurable Machining System (RmS) 1 design. The task of designing a RmS is performed by a System Configurator, of which the Decision Support System for Machine Selection is one major component. Machining operation clustering is the first step in machine selection. One important form of clustering identifies sets of operations that have the potential to be machined in parallel. Such paraUelism-based operation clusters as we refer to them, must satisfy a set of constraints in order to be processed simultaneously on a gang spindle head. Minimum feature spacing is one major mechanical constraint due to bearing size limitation that must be considered. This paper first proposes a model for developing a Decision Support System for Machine Selection, then presents a patterning algorithm for obtaining identical parallelism-based clusters that satisfy the minimum feature spacing constraint where • applicable. The algorithm obtains identical clusters by first searching for translational vectors and then extracting the appropriate end-points. The algorithm automatically obtains all alternative solutions so that finding identical patterns on different faces of the part or on different parts within the target family can be implemented. This strategy maximizes the usage of identical spindles and/or machines. The cost associated with redesigning, testing, and reconfiguration is significantly reduced.
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