Intelligent control of complex materials processes

Abstract

A blackboard based intelligent control system has been developed for a family of complex non-equilibrium materials processes. The system is being tested in the laboratory for control of a particular high risk, high value-added step in the manufacture of carbon-carbon composites. The system uses knowledge based methods in several fundamental ways to fill gaps left by control theory and process models. Most notable of these are (1) inferring from indirect measurements and history the process state at multiple, changing levels of abstraction, (2) anticipating problems and planning actions to reach goal (end of process) states, (3) selecting, executing and interpreting approximate models to predict process progression and (4) changing control objectives as the physical situation changes. The system has been demonstrated to substantially reduce processing time.