Prediction of Temperature in Vacuum Arc Remelting in the Presence of Strong Disturbances

Abstract

A system for predictive, noninvasive temperature measurement in a broad class of manufacturing processes is presented. The system employs a comprehensive, three-tier signal processing algorithm to achieve high accuracy and reliability in the presence of strong disturbances. Temperature estimates are generated by ‘unstructured, shallow knowledge’ based algorithms. The estimation process employs a set of robust features obtained by signal processing that involves constitutive models representing ‘deep knowledge’ about the process dealt with. The application of these models is combined with ‘structured, shallow knowledge’ based techniques. Results obtained in application to the investment casting of titanium, which employs the vacuum arc remelting (VAR) process, are presented.