Cluster Detection of Non‐Metallic Inclusions in 42CrMo4 Steel

Abstract

Recent progress in steel casting shows significant reduction of non‐metallic inclusions (NMIs). Unfortunately, total NMI volume reduction due to refining can surprisingly lead to NMI size enlargement or cluster formation. This effect decreases the fatigue endurance limit of steel, which is sensitive not only to the presence of large NMIs, but also to clusters of small inclusions, which are usually overlooked in standard metallography inspection. In the present paper, the authors aim to investigate clustering properties of NMIs in a dataset of 42CrMo4 steel, which are observed on fracture surfaces after fatigue tests and from inclusion patterns in polished cross‐sections after metallographic inspection. The authors use both data types for estimating critical distances as main cluster size parameters and find excellent agreement between the results of both methods. The clusters are determined by a classical statistical method of cluster analysis, called “agglomerative hierarchical clustering”. The authors show by means of methods of spatial point pattern analysis that their method yields clusters that are more plausible than clusters resulting from the well‐known Murakami “thumbnail rule”.