Automatic assembly sequence generation (ASG) is important for efficient manufacturing and concurrent engineering. There are two main difficulties with developing tools for ASG: (i) the combinatorics makes solutions by blind search intractable and (ii) criteria for optimal assembly sequences are difficult to formalize. Earlier work of the PI investigated the use of case-based reasoning techniques to simultaneously address both problems with encouraging results especially concerning the extensibility to covering large sets of problems. The practical objective of this project is to verify these results on a larger prototype, and thus demonstrate the usefulness of case-based assembly sequence generation for practical applications. The theoretical objectives of this project are to investigate new methods for adaptive learning and case matching which will form part of the new prototype. The result of this work is a fully implemented case-based reasoning system for a large class of assembly devices. The conceptual results involved in building such a system should prove useful to research in intelligent design and manufacturing systems and in case-based reasoning field.
