A case-based system for process planning

TL;DR: A prototypical knowledge-based system that uses the high level, dynamic memory structures of MOPs, meta-MOPs and TOPs to rearrange its knowledge according to its experiences and to predict and avoid errors is designed and used in the system.

Abstract: Process planning is the phase of manufacturing that is concerned with the selection, and sequencing of manufacturing operations necessary to transform an initial stock material into a finished part. It is a tedious operation that requires highly skilled and experienced personnel of which there is currently great shortage. To disseminate the costly expertise and to reduce process planning times, which tend to add high costs to small batch production, many automated computer systems have been introduced and applied. All these systems, though, require the supervision of an experienced human being and fail to capture the knowledge and reasoning behind process planning decisions. The causes of the lack of an efficient automatic system for process planning are the unique characteristics of the domain. In contrast to other areas where knowledge-based and expert systems have been applied, process planning demands the solution of several different planning problems before a final solution is achieved. Every change introduced to the previous world model creates a completely new world model with its own, new constraints and preconditions that nedd to be satisfied. This, in turn, affects the applicability of the knowledge to the new model. To provide solutions to the above problems we have designed a prototypical knowledge-based system that uses the high level, dynamic memory structures of MOPs (Memory Organization Packages), meta-MOPs and TOPs (Thematic Organization Packets) to rearrange its knowledge according to its experiences and to predict and avoid errors. To model the dynamic, sharable knowledge and the constantly changing world model a knowledge-representation language named TOLTEC has been designed and used in the system. Furthermore, we propose a novel planning method based on abstract plans and constraints that can solve planning problems in uncertain domains given incomplete information.