Using Linguistic Data Summarization in the study of creep data for the design of new steels

A procedure for the design of new creep resistant ferritic steels that involves a large systematic search of combinations of parameters using a neural network model, was proposed in a paper published few years ago. In the present work we study the effectiveness of the Linguistic Data Summarization technique to be used as a tool to discover a credible and useful creep behavior in a way that it can be used as a guide in the mentioned search. Experiments are performed similar to those discussed in the paper mentioned in order to make an effective comparison of the behavior of the creep. We propose the use of an indicator that measures the degree of representativeness of the linguistic terms for the summarizer in our experiments context. As a result, the effectiveness of the Linguistic Data Summarization to discover hidden creep behavior stored in creep data and the usefulness of the representativeness indicator was confirmed.