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Automatic grain size determination and classification of iron carbides with neural nets
Author(s) -
Schmitter Ernst Dieter
Publication year - 1995
Publication title -
steel research
Language(s) - English
Resource type - Journals
eISSN - 1869-344X
pISSN - 0177-4832
DOI - 10.1002/srin.199501153
Subject(s) - automation , quality assurance , computer science , preprocessor , artificial neural network , software , image processing , artificial intelligence , data mining , engineering , image (mathematics) , mechanical engineering , operations management , external quality assessment , programming language
Under the aspect of quality assurance material testing is getting more and more important. As a consequence there is a strong demand for automation and objective procedures. This article concentrates on the inspection of polished steel specimens using image processing and intelligent software techniques. Nowadays computer aided image processing of microstructures is widely used, but final analysis and quality grading are based on the experience of the testing personnel. Using intelligent software techniques (soft computing), especially neural nets, allows complex decision processes to be done on a computer in a reproducible manner. Of course there are certain premises: suitable restriction of the application domain, careful image preprocessing and feature extraction, critical surveillance of the training process. The article focuses on two neural net applications: grain size determination and classification of iron carbides. To learn the ability to do complex decisions in a reproducible manner from a suitable example data set – at least in a restricted domain – is the main advantage of a neural net solution.