Abstract
Abstract Non-metallic inclusions, especially large or clustered inclusions, in steel are usually harmful. Thus, the microscopic analysis of test specimens is an important part of the quality control. This steel purity analysis produces a large amount of individual inclusion information for each test specimen. The interpretation of the results is laborious and the comparison of larger product groups practically impossible. The purpose of this study was to develop an easy-to-use tool for automatic interpretation of the SEM analysis to differentiate clustered and large inclusions information from the manifold individual inclusion information. Because of the large variety of the potential users, the tool needs to be applicable for any steel grade and application, both for liquid and final product specimen, to analyse automatically steel specimen inclusions, especially inclusion clusters, based on the INCA Feature program produced data from SEM/EDS. The developed tool can be used to improve the controlling of the steel purity or for automatic production of new inclusion cluster features that can be utilised further in quality prediction models, for example.
Highlights
Non-metallic inclusions, especially large or clustered inclusions, in steel are usually harmful
This steel purity analysis produces a large amount of individual inclusion information for each test specimen
Because of the large variety of the potential users, the tool needs to be applicable for any steel grade and application, both for liquid and final product specimen, to analyse automatically steel specimen inclusions, especially inclusion clusters, based on the INCA Feature program produced data from scanning electron microscopy/energy dispersive spectroscopy (SEM/EDS)
Summary
Abstract: Non-metallic inclusions, especially large or clustered inclusions, in steel are usually harmful. The purpose of this study was to develop an easy-to-use tool for automatic interpretation of the SEM analysis to differentiate clustered and large inclusions information from the manifold individual inclusion information. Because of the large variety of the potential users, the tool needs to be applicable for any steel grade and application, both for liquid and final product specimen, to analyse automatically steel specimen inclusions, especially inclusion clusters, based on the INCA Feature program produced data from SEM/EDS. An automatic Oxford INCA feature analysis program produces more than 15 features (including information about the location, size, area, aspect ratio, direction and chemical composition of the inclusion) for each inclusion that it differentiates from a scanning electron microscopy/energy dispersive spectroscopy (SEM/EDS) data of a steel specimen. The contribution of this paper is a description of a novel tool for automated finding and analysing steel specimen inclusion clusters based on INCA Feature program produced inclusion data from SEM/EDS. Tween the accuracy of the modelling of the inclusions and the processing time for the analysis
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