A CASE-BASED EXPERT SYSTEM FOR MANUFACTURING APPLICATIONS

THE OBJECTIVE IS TO DEVELOP ARTIFICIAL INTELLIGENCE (AI) FOR INCREASING MANUFACTURING CAPABILITIES. A CASE-BASED REASONING (CBR) APPROACH TO KNOWLEDGE ELICITATION IS PROPOSED AS A SENSITIVE AND EFFECTIVE STRATEGY TO DERIVE AND REPRESENT THE CRITICAL KNOWLEDGE. PHASE I WILL DEMONSTRATE THE FEASIBILITY OF USING A CBR APPROACH TO DEVELOPING AN AI KNOWLDGE-BASED SYSTEM TO IMPROVE MANUFACTURING. CASE-BASED REASONING METHODS WERE DEVELOPED FOR DOMAINS WHERE DIRECT DATA COLLECTION WAS TIME-CONSUMING, EXPENSIVE, AND OFTEN NOT FEASIBLE,AND WHERE FORMAL MODELS WERE HARD TO APPLY BECAUSE OF MISSING DATA AS WELL AS THE COMPLEXITY OF THE PROBLEM. CBR RELIES UPON ESTRAPOLATION FROM EXISTING POINTS; IT CAN OVERCOME PROBLEMS OF MISSING DATA WHILE LEAVING A CLEAR RATIONAL FOR THE BASIS OF THE ESTIMATES GENERATED. THEREFORE, A CBR APPROACH WOULD HAVE THE ADVANTAGE OF OVERCOMING THE LIMITATIONS OF ALTERNATIVE KNOWLEDGE-ENGINEERING APPROACHES WHILE INCREASING THE EFFECTIVENESS OF KNOWLEDGE-BASED SYSTEMS.