Diagnostic expert systems for defects in forged parts

This paper describes the development of a diagnostic expert system that identifies the cause of various manufacturing defects in hot forging and suggests remedies. The patterns of defect generation are various and due to many possible causes. They depend on part designs, tool designs and process conditions. Pertinent factors include part shape, the state of lubrication, location of the performed workpiece on the die and formability of the materials. This paper utilizs the theory of conditional probability to construct a diagnostic expert system that can adapt and learn its diagnostic mechanism through field data. A demonstration program, FORDIA, runs in HyperCard. FORDIA takes the part defect symptoms, uses conditional probability theory to identify possible causes and suggests remedies.