Abstract
The paper outlines three applications of case-based learning in sheet metal manufacturing by utilization of artificial intelligence, each based on different concepts. One topic is the synthesis with feature processing for the process outline on a CNC bending machine. Statistics on fail and success influence weights of rules in an expert system. Another example is the classification of sheet metal parts. Real objects and their components are represented by a frame like data structure, which is embedded in a semantic network. Grouping of work pieces is done by a decision method of Bayes. This method is also used for part diagnosis to avoid possible defects of tools and machines.
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