Automated Classification and Analysis of Non-metallic Inclusion Data Sets
- PDF / 3,174,880 Bytes
- 12 Pages / 593.972 x 792 pts Page_size
- 60 Downloads / 200 Views
Automated Classification and Analysis of Non-metallic Inclusion Data Sets MOHAMMAD ABDULSALAM, TONGSHENG ZHANG, JIA TAN, and BRYAN A. WEBLER The aim of this study is to utilize principal component analysis (PCA), clustering methods, and correlation analysis to condense and examine large, multivariate data sets produced from automated analysis of non-metallic inclusions. Non-metallic inclusions play a major role in defining the properties of steel and their examination has been greatly aided by automated analysis in scanning electron microscopes equipped with energy dispersive X-ray spectroscopy. The methods were applied to analyze inclusions on two sets of samples: two laboratory-scale samples and four industrial samples from a near-finished 4140 alloy steel components with varying machinability. The laboratory samples had well-defined inclusions chemistries, composed of MgO-Al2O3-CaO, spinel (MgO-Al2O3), and calcium aluminate inclusions. The industrial samples contained MnS inclusions as well as (Ca,Mn)S + calcium aluminate oxide inclusions. PCA could be used to reduce inclusion chemistry variables to a 2D plot, which revealed inclusion chemistry groupings in the samples. Clustering methods were used to automatically classify inclusion chemistry measurements into groups, i.e., no user-defined rules were required. https://doi.org/10.1007/s11663-018-1276-x Ó The Minerals, Metals & Materials Society and ASM International 2018
I.
INTRODUCTION
NON-METALLIC inclusions are an inevitable product of chemical reactions occurring during steel processing and they play an important role in defining the properties of steel.[1] If not controlled, they can often reduce ductility, fatigue resistance, and toughness of steels.[2–7] There has been a significant amount of research on inclusion control during liquid steel processing[8] with intentional efforts to control inclusion populations referred to as ‘‘inclusion engineering’’ with the resulting product called ‘‘clean steel.’’ An enabling technology for inclusion engineering efforts on both the laboratory scale and the production scale is automated analysis in a scanning electron microscope equipped with energy dispersive X-ray spectroscopy (SEM/EDS).[9] While one of several analysis and quantification methods,[10] automated SEM/ EDS can measure hundreds or thousands of individual inclusions per sample and obtain chemistry, size, shape, MOHAMMAD ABDULSALAM, TONGSHENG ZHANG, and BRYAN A. WEBLER are with the Materials Science and Engineering Department, Center for Iron and Steelmaking Research, Carnegie Mellon University, Pittsburgh, PA, 15213. Contact e-mail: [email protected] JIA TAN is with Nucor Castrip Arkansas LLC, Blytheville, AR, 72315 Manuscript submitted September 01, 2017.
METALLURGICAL AND MATERIALS TRANSACTIONS B
and spatial distribution information in times on the order of hours or less. Several recent studies have advanced in back-scattered electron (BSE) imaging and EDS measurement parameters,[11,12] to improve the speed and accuracy of the method. Automated analy
Data Loading...
