scholarly journals A tool for finding inclusion clusters in steel SEM specimens

2020 ◽  
Vol 10 (1) ◽  
pp. 642-648
Author(s):  
Anna-Mari Wartiainen ◽  
Markus Harju ◽  
Satu Tamminen ◽  
Leena Määttä ◽  
Tuomas Alatarvas ◽  
...  

AbstractNon-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.

1992 ◽  
Author(s):  
D. D. Murphy ◽  
W. M. Thomas ◽  
W. M. Evanco ◽  
W. W. Agresti

2011 ◽  
Vol 52-54 ◽  
pp. 674-679
Author(s):  
Chun Sheng Wang ◽  
Min Wu ◽  
Qi Lei

Based on some features in lead-zinc sintering process (LZSP), such as large time delay and strong non-linearity, an intelligent integrated method for quality prediction based on back-propagation neural network (BPNN) and improved grey system (IGS) is presented. First, the compositions of agglomerate are predicted by BPNN and IGS models. Then, a recursive entropy algorithm for the weighting coefficients is devised from the viewpoint of the information theory and an intelligent integrated prediction model (IIPM) is established. The compositions of sinter agglomerate are predicted by integrating the two prediction models. Application results show that the IIPM has higher prediction precision than that of single model and the proposed intelligent integrated method settles the modeling problem of the quality in the LZSP.


2019 ◽  
Vol 946 ◽  
pp. 47-52
Author(s):  
A.V. Ryabov

The paper presents theoretical and experimental studies of the formation processes of boron nitride, aluminium nitride, aluminium oxide and manganese sulphide inclusions in a free-cutting steel. Fact Sage software was used to model the behaviour of non-metallic inclusions. Formation temperatures and the amount of key inclusions in steel were calculated. Formation order of inclusions is as follows: aluminium oxide > boron nitride > manganese sulphide > aluminium nitride. The object of study was the A45AR grade steel in 1.1–1.2 kg ingots. It was melted in an induction furnace, and aluminium, nitrided ferrosilicon and ferroboron were added after deoxidation before tapping. Quality estimation included chemical composition, macro-and microstructure, the character and shape of non-metallic inclusions. The finished metal contained fine and uniformly distributed inclusions of boron nitride. Qualitative and quantitative analysis of boron nitrides distribution in metal matrix showed that they were present both as individual and complex compounds, mostly of spherical shape. The size of BN inclusions varied from 0.18 to 6.52 μm. The amount of boron added to steel did not affect the size of MnS non-metallic inclusions.


2019 ◽  
Author(s):  
Miguel Cuartas ◽  
Estela Ruiz ◽  
Diego Ferreño ◽  
Jesús Setién ◽  
Valentín Arroyo ◽  
...  

1978 ◽  
Vol 23 (1) ◽  
pp. 200-201 ◽  
Author(s):  
F. B. Pickering

Author(s):  
C. G. Nicholson

SummaryIt is shown that petrographic methods, using the microscope and micro-refractometer, can be a valuable aid in the identification of non-metallic inclusions in steel, even when their size is only a small fraction of a millimetre. Special ways of preparing the samples are given, and some of the methods used in examination are indicated. Natural chromite, corundum, merwinite, mutlite, quartz, rhodonite, spincls, and zircon are taken as examples of the minerals found.


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