Comparison of 2D and 3D investigations of non-metallic inclusions in metal samples by different analytical methods

IN

DEGREE PROJECT TECHNOLOGY, FIRST CYCLE, 15 CREDITS

STOCKHOLM SWEDEN 2019,

Comparison of 2D and 3D

investigations of non-metallic inclusions in metal samples by different analytical methods

ANDREAS FLYCKT

KTH ROYAL INSTITUTE OF TECHNOLOGY

SCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT

Abstract

The objective of this research is to make a comparison between 2D- and 3D-investigations of non- metallic inclusions (NMIs) in metal samples by different analytical methods. NMIs are undesired particles that degrade the quality of the steel through affecting the mechanical properties. It’s therefor of great importance that NMIs are carefully examined, and the correct investigation method is used depending on what the objective is.

The different parameters that were used in this comparison was composition, location, morphology, number and size.

The first step in this research was to complete a literature review on the different analytical methods. The examined 2D-methods were ASPEX and INCA Feature, which are automated. The examined 3D-method was electrolytic extraction followed by further examination by SEM, which is a manual examination. The second step was to make an experimental comparison between INCA Feature and electrolytic extraction.

It was found that ASPEX and INCA Feature performs well in all parameters except location and morphology, and they are also time-efficient methods. Electrolytic extraction performed well in all parameters but is a time-consuming method.

The 2D-methods performs well in the parameters: composition, number and size, and they are also time-efficient. The 3D-method electrolytic extraction is the best when there is a need for a more precise understanding of all the parameters but it’s time-consuming.

Sammanfattning

Målet med denna undersökning är att göra en jämförelse mellan 2D- och 3D-undersökningar av icke- metalliska inneslutningar (IMIs) i metallprover genom olika analytiska metoder. IMIs är oönskade partiklar som försämrar kvalitén av stålet genom att påverkar de mekaniska egenskaperna. Detta ger oss förståelse att det är otroligt viktigt att noggrant undersöka IMIs och även använda korrekt metod beroende på vad målet är.

De olika parametrarna som användes i denna jämförelsen var sammansättning, position, morfologi, antal och storlek.

Första steget i denna undersökning var att genomföra en litteraturstudie av de olika analytiska metoderna. De 2D-metoder som undersöktes var ASPEX och INCA Feature, dessa är automatiska.

Den 3D-metod som undersöktes var elektrolytisk extraktion där resultatet undersöktes med ett SEM.

Denna metod är manuell. Det andra steget var att genomföra en jämförelse mellan INCA Feature och elektrolytisk extraktion genom experiment.

Resultatet blev att ASPEX och INCA Feature fungerade väl i alla parametrar förutom position och morfologi och att de även är tidseffektiva metoder. Elektrolytisk extraktion fungerar väl i alla parametrar men är en tidsineffektiv metod.

2D-metoderna fungerar bra när det gäller parametrarna sammansättning, antal och storlek, de är även tideffektiva. 3D-metoden elektrolytisk extraktion är den som fungerar bäst när man behöver få en mer detaljerad och noggrann förståelse för de olika parametrarna men den är tidsineffektiv.

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Table of content

1. Introduction ... 1

1.1 Non-metallic inclusions and their impact on steel properties ... 1

1.2 Analytical methods for evaluation in non-metallic inclusions ... 2

1.3 Aims of study ... 4

2. Method and experimental ... 6

2.1 Method literature review ... 6

2.2 Materials and procedures ... 6

2.3 Investigation of non-metallic inclusions ... 6

3. Results ... 9

3.1 Experimental results ... 9

3.2 Literature review results ... 12

3.3 Characterisation of non-metallic inclusions by INCA and EE ... 16

4. Discussion ... 24

4.1 Environmental, ethical and social aspects ... 24

4.2 Final discussion ... 24

5. Conclusions ... 26

6. Future work ... 28

7. Acknowledgement ... 29

8. References ... 30

Appendix A: Detailed summary of literature review ... 31

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1. Introduction

1.1 Non-metallic inclusions and their impact on steel properties

Non-metallic inclusions (NMI) are undesired products that are naturally occurring in the

production of steel and in all the following manufacturing and treatment processes that involves liquid steel. The NMI are composed by glass-ceramic phases that are encapsulated in the steel matrix and they are a combination of a chemical compound of metal, like iron, aluminium, manganese etc, and a non-metal, like oxygen, nitrogen, sulphur etc [1].

There are two ways of classifying NMI. The first one focuses on their origin which can be endogenous or exogenous. Endogenous inclusions come from within the steel and are created through precipitation in the liquid phase due to a loss of solubility of the chemical in the steel.

Exogenous inclusions are a side-effect when non-metallic materials are boxed in by slag, refractory fragments or from processes for protecting the steel [2].

The other way to classify NMI are by their chemical composition, they can synthetically be classified as oxides, sulphides and nitrides. The oxides are formed by the deoxidizing components that are added to the steel to eliminate the content of oxygen. Typical oxides are FeO, Al2O3 and SiO2. The nitrides are mostly formed by TiN and some other common nitrides are AiN and VN.

The sulphides often come from the calcium treatment of the steel that is being applied to change the oxide inclusions. Some common examples of sulphides are FeS, MnS and CaS. These are the three most common ones but there are also carbides (Fe3C), phosphides (Fe3P) and complex compositions of the earlier mentioned like oxy-sulphides (MnS. MnO) etc [1], [2].

NMIs are undesired particles that impacts the properties and can degrade the quality in steels although in some cases they can be a necessity to achieve certain properties. The properties that usually are affected by the NMIs are tensile strength, deformability or ductility, toughness, fatigue strength, fracture toughness, corrosion resistance, weldability, polishability, and machinability. Metallurgists and materials scientists put great effort into examining the total number, morphology, size distribution, spatial distribution and chemical composition of the NMIs [3]. It is common knowledge that inclusions can never be entirely removed from the steels. They can only be controlled and in the end the process of handling inclusions come down to modifying the quantity, size, shape, distribution and compositions to achieve better mechanical properties.

There are different ways that inclusions affect the mechanical properties. One way is through void nucleation where the inclusion acts as a point where the voids grows and can lastly end in a fracture in the steel. The fracture toughness of steel has been shown to have a connection to the inclusion volume fraction and the inclusion spacing. It has been shown that a decrease in the volume fraction of inclusions and an increase in inclusion spacing results in significant

improvements in fracture toughness. The machinability of steel is impaired by oxide inclusions like for example spinels where some of them has an index of deformability near zero which results in that they cannot be worked. Poor polishability, reduced corrosion resistance and inferior surface appearance is linked with large exogenous inclusions [4]. MnS inclusions has been shown to be correlated with a decrease in mechanical properties especially after rolling

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because these inclusions get elongated in that process which leads to weakening in the

transverse direction [5]. Besides impacts on the steels properties NMIs can also cause problems during processing like nozzle clogging and break-outs during casting and it can also affect the surface quality of steel. The presence of the inclusions doesn’t have to be so high for the impact to be noticeable, only 0.01-0.02% [1], [6]. This gives an understanding that it is of grave

importance to have control over the NMIs when producing high-performance steel.

1.2 Analytical methods for evaluation in non-metallic inclusions

Full control over the final steel product goes hand in hand with the control over the NMIs, without this control the manufacturer cannot guarantee the quality of the steel. To get the critical control needed over the NMIs there exist different methods to examine the steels, there exists both 2D- and 3D investigations methods. 2D-methods are often used at the manufacturer’s in off-line studies to investigate different kinds of optimisation and the 3D-methods are often used off-line in Research and Development or at universities. In this paper the focus is on two automated 2D-methods called ASPEX and INCA Feature and a 3D-method called electrolytic extraction.

ASPEX

The 2D-method called ASPEX is a computer-controlled scanning electron microscope (SEM) that is constructed to perform an automated particle analysis of a chosen surface. The ASPEX system is integrated with, as mentioned, a SEM and an Energy Dispersive Spectroscopy (EDS) platform to address the micro-scale visualizations where the EDS platform handles the analysis of the

elemental composition of the point of interest for example an inclusion [7]. Before the analysis the sample is prepared and rigorously polished. The analysis is frame-based, first an area is chosen and then divided into numerous small fields. When the fields are divided an electron beam is positioned on one of the fields. Here the microscope makes a quick search of the chosen field with large search steps were the intensity of the back-scatter electrons are recorded and sent to a computer. Once a specific threshold of the intensity of the back-scatter electrons is reached, indicating for example an inclusion, the search step size is reduced to start more precise measures. Now the program searches after the centre of the inclusion to measure it more precisely [3]. After locating the centre an approach called Rotating Chord Algorithm (RCA) is used. RCA draws 16 chords through the centre with an interval of 11° and based on the size combination of the 16 chords, the shape and the size of the inclusion can be determined. After this the electron beam is placed in the centre again to attain the rest of the information, when the wanted information is attain it continues to scan the field for more inclusions. When the entire field is scanned for inclusions the electron beam moves over to another field and goes through the same process there until all chosen fields are analysed. The information that ASPEX can detect is area, size, shape, number, location, rough morphology and elemental composition of the inclusions in steel samples. The system is calibrated in the beginning where the user sets a minimum particle size, which determines what sizes of inclusions or other types of anomalies that will be analysed. The usual minimum particles size that is chosen is 1 µm, which means that all inclusions under 1 µm will be excluded [3], [7].

3 INCA

The second 2D-method is INCA Feature which also is an automated analysis method that is connected to a SEM and an EDS platform. First, the metal sample that is going to be examined is very well polished. This system works almost in the same way as the earlier mentioned ASPEX system. INCA Feature also uses an electron backscatter detector to distinguish between the metal matrix and the inclusions. The operator choses a surface that shall be investigated and the magnification, then the program starts to analyse. INCA Feature scans the sample step by step until it finds a particle based on their geometrical and chemical composition. When the particle is found it instantly gets classified either as a rectangle, circle, line, point etc., the program then divides the area into a series of small fields which are separately analysed. To calculate the area and the size INCA Feature uses a two-stage algorithm [8], [9], [10]. Other parameters that are calculated are length, width, aspect ratio, chemical composition (both at-% and wt-% of each inclusion found) and the location, in coordinates, in the sample [11]. Aspect ratio of an inclusion is calculated with the maximum length divided by the maximum width as seen in Figure 1.

EE + SEM

The 3D-method is called electrolytic extraction and is the gentlest extraction method that exists.

The way this method works is that first the steel sample is polished with a sandpaper or an electrical rasp, so the surface of the sample is clean. The metal sample is then placed in an electrolyte just below the surface. A current is run through the metal sample to accelerate the process. The acceleration can be up to 1000 times in comparison to what it would take without the current. As the current is run through the metal sample the metal matrix starts to dissolve.

As the metal matrix breaks down the non-metallic inclusions and the clusters are unaffected and lands at the bottom, see Figure 2 for an illustration of the process of electrolytic extraction [12].

Figure 1 Illustration of calculation of aspect ratio

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The anode and the cathode are connected to a potentiostat which has a coulombmeter, voltmeter and an amperemeter. The coulombmeter is used as the indicator of when the extraction should stop. This limit varies and can be from as little as 20 coulombs and in some cases up to several thousand coulombs. When the wanted number of coulombs are reached the current is disconnected and the electrolyte with all the extraction is filtered through a film filter which has a vacuum pump connected to it. The film filter is usually made of polycarbonate and can have different pore sizes from 0.05 all the way up to 10 µm. The metal sample is put in a methanol ultrasonic bath, so all the extracted particles can be obtained. This is then put through the same film filter as the electrolyte, so these inclusions and cluster are caught on the film filter as well. After this the film filter is removed and dried. A piece of the film filter is then cut, and the sample is then examined in a SEM for the analytical part of this method [13].

1.3 Aims of study

In this study there are three goals that are wished to be attained:

1. Find the advantages and disadvantages based on the parameters: composition, location, morphology, number and size of 2D and 3D investigation methods for analysis of non- metallic inclusions in steel samples based on a literature review. Find out if there are any pattern in usage around the world of the different methods based on the literature review.

The different investigation methods examined are the automated 2D-methods ASPEX and INCA Feature and the 3D-method electrolytic extraction as were discussed above in the previous chapter.

Figure 2 Dissolvement of metal matrix and extraction of inclusion and clusters

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2. Perform a 3D investigation, an electrolytic extraction, on the provided steel samples and use a SEM to examine the extracted non-metallic inclusions. Compare this with data from INCA Feature on the same metal samples.

3. Make a comparison based on the parameters composition, location, morphology, number and size between the results from the literature review and the experimental part.

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2. Method and experimental

2.1 Method literature review

45 articles were examined in the literature review, see Appendix A for more intricate details regarding each article. 15 articles where chosen on each method to get a fair result. One of the tasks at hand was to determine if there were any pattern of where in the world the different methods were used.

2.2 Materials and procedures

The investigated industrial steel samples are 43-1 and 43-2 which were acquired from a company. The first one is taken from liquid steel in ladle before calcium treatment and the second is after calcium treatment. The composition of the samples was obtained on-line with OES (Optical Emission Spectrometry) analysis at the manufacturer and are given in Table 1:

Table 1: Composition of steel samples 43-1 and 43-2 (wt-%)

Steel sample C Si Mn S Cr Ni Al Ca

43-1 0.161 0.008 0.402 0.0022 0.050 0.046 0.048 <0.0003 43-2 0.161 0.012 0.407 0.0020 0.051 0.046 0.055 0.0049

The investigation methods used on our steel samples were 2D-investigation by INCA Feature and 3D-investigation by electrolytic extraction.

2.3 Investigation of non-metallic inclusions

INCA Feature

The INCA Feature investigation of the steel sample were performed by a company. Before scanned and analysed the samples were polished. The analysed area of sample 43-1 was 466 mm² and it found 1039 features and of those 916 were inclusions. In sample 43-2 the area was 488 mm² and 353 features were found and of those 288 were inclusions. The minimum size of inclusions that were analysed were 2,78 µm. INCA Feature measures over 20 different

parameters and did also perform a composition analysis of different elements. The ones that were used in this report were: number, area, type of inclusion, aspect ratio and composition.

INCA Feature swept over the steel samples and distinguished between darker and lighter areas. If the density of the inclusion is lower than the metal matrix, the inclusions looks darker that the metal matrix in INCA Feature. If the density is higher in the inclusions, then the inclusions are brighter and the background is darker.

7 Electrolytic Extraction + SEM

The electrolytic extraction and the analysis with the SEM were performed in KTHs laboratories. It was done on both samples, 43-1 and 43-2. The first part, the electrolytic extraction, is done to separate the inclusions from the metal matrix. The following are the steps that were performed:

1. First all the equipment was cleaned rigorously with water, then with distilled water and lastly methanol.

2. The sample was polished with a sandpaper to get a clear contact surface. Important to note is that the sandpaper can release silicon carbides so this needs to be considered when examining the result in the SEM later.

3. Measurements of size and weight were made of the steel sample, so the amount that was dissolved can later be measured after the process.

4. The sample was placed in acetone and then in an ultrasonic bath to remove the contaminants on the outer layer and after that the sample was dried.

5. The sample was placed in benzene and then again in an ultrasonic bath to get the sample even cleaner and after that the sample was dried.

6. A parafilm was placed around the sample so the dissolution of the metal matrix came from the same side that was examined during the company’s INCA Feature investigation.

7. After the parafilm was placed around the sample it was placed in a beaker filled with 250 ml 10% AA (methanol based). A current was run through so the extraction was accelerated, see schematic of the process in Figure 3 a).

8. The extraction was continued until the coulombmeter reaches 500 or 1000 coulombs and was then stopped.

9. The sample was rigorously cleaned and measured.

10. The solution in the beaker was then filtered through a film filter with pores with a diameter of 0.4 µm that was connected to a vacuum pump. This part of the experiment can be seen in Figure 3 b).

11. The film filter is then removed and dried, so it later can be analysed with SEM

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The next step was the analysis of the film filter that was acquired from the electrolytic extraction.

This step was made with a SEM in the following way:

1. The film filter has a circular shape but it was cut up in to a smaller and more manageable size, like piece of a cake, so it would be easier to examine. An illustration is found in Figure 4.

This part of the filter was then pasted on an Al holder by using C-tape.

2. The used magnification was x1000 with the SEM and at a lot of different inclusions were found and pictures were taken.

3. When examining the sample, the grey area was ignored, see Figure 4, because the inclusions captured on the film filter may have been removed when the sample was cut.

4. Pictures were taken on the top, middle and the bottom of the sample, this was done to get pictures that were as representative as possible of the entire sample.

5. After these pictures were taken, pictures of selected inclusions for a more in-deep analysis were taken to get a better sense of what kind of inclusions that were present on the film filter.

.

Figure 3 a) Illustration of EE process and b) photograph from the film filtering part of the experiment

Figure 4 Illustration of the film filter that was examined

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3. Results

3.1 Experimental results

During this entire process all parameters were recorded which can be seen in Table 2. Length dissolved, ldis,was calculated with (3.1):

𝐥_𝐝𝐢𝐬= ^{𝐕𝐝𝐢𝐬}

𝐀 =

∆𝐖 𝛒𝐌𝐞𝐭𝐚𝐥

𝐀 (3.1) ldis = length dissolved

Vdis = Volume dissolved A =Area

ΔW = Difference in weight

ρMetal = Density of our metal sample

The density, ρMetal, was chosen to have the same value as iron (Fe) which is 0.0078g/mm³. The reason behind this was that the metal sample was a very low alloyed steel, so an assumption could be made that the density was approximately the same as iron.

Table 2: Summary of all parameters of Electrolytic extraction (EE)

During the whole extraction process time, coulombs, ampere and volt were measured as mentioned earlier. This was measured because it is important the see that the process is stable otherwise the results may be wrong and misleading. To prove that the process was stable diagrams were made with coulombs vs time, this can be seen in Figure 5. Figure 5 a) shows the sample 43-1 and 3 b) shows sample 43-2.

Sample Size of sample (mm)

Dissolved weight (g)

ldis

(µm)

Electr- olyte

Charge (C)

Current (mA)

Voltage (V)

Time of extraction (min)

43-1 13.12 x 8.91 x 10.2

0.1124 123 10%

AA

500 66-70 4.4-4.5 128

43-2 9.32 x 8.81 x 10.19

0.1976 309 10%

AA

1000 51-66 3.9-4.2 301

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Figure 5 a) Stability of EE of sample 43-1 and b) stability of EE of sample 43-2

It is clear from Figure 5 a) and b) that the extraction process was stable because there are no noteworthy fluctuations seen in the diagrams and the lines are almost linear. The two other parameters, ampere and volt, were also stable. In the extraction of sample 43-1 the ampere varied between 66-70 mA and the voltage between 4.4-4-5 V. Sample 43-2 had a bit more fluctuations, the ampere varied between 51-66 mA and the voltage between 3.9-4.2 V. The larger fluctuations in the latter sample can be a result of that the extraction process of sample 43-2 took 301 minutes in comparison to sample 43-1 128 minutes.

It's important to understand how deep our different methods analyses into the steel samples.

INCA Feature was the automated 2D-method that was used. A theoretical average depth, which corresponds to the average diameter of the measured inclusions, 𝑑̅̅̅, can be calculated with the _𝐴 De Hoff Method which uses (3.2):

𝑁_𝑉= ^𝑁𝐴

𝑑_𝐴

̅̅̅̅ (3.2) Where NV is the numbers of particles per unit and NA is the numbers of particles per unit area.

When applied on our data the analysed depth was about 3.5 µm in sample 43-1 and about 5.5 µm in sample 43-2. This gives an understanding that some inclusions sizes can be misinterpreted due to the limitation of analysed depth of this method. Some inclusions are larger and go beyond the limit of 3.5 and 5.5 µm. An illustration of how INCA Feature analyses can be seen in Figure 6 a).

The electrolytic extractions depth was 123 µm in sample 43-1 and 309 µm in sample 43-2. It’s clear that EE can analyse deeper, up to as much as 56 times the depth of INCA in our experiment.

An illustration of how EE dissolves the metal matrix can be seen in Figure 6 b).

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Figure 6 a) Illustration of how deep INCA Feature analyses a metal sample and b) illustration of metal sample and how deep inside the metal sample EE reach

Another important statement is that size must be defined to be able to fully understand the results of these experiments and gathered data. When it comes to size distribution it is important to state that size is ECD. It stands for Equivalent Circle Diameter, which corresponds to a circle having the same area as the inclusion area. ECD of an inclusion is calculated with the following formula [11]:

𝐸𝐶𝐷 = √^{4×𝐴𝑟𝑒𝑎}

𝜋 (3.3)

The last statement is that when the metal sample is cut it can paint a wrongful picture of what is really going on, especially if there are clusters. A cluster can be seen in Figure 7, were the lines symbolises a couple of different ways it can be cut. It is then clear that one inclusion, a cluster, can be misinterpreted as up to four separate inclusions.

Figure 7 Different ways a cluster can be misinterpret as several inclusion

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3.2 Literature review results

The 2D-methods ASPEX and INCA Feature were first examined. The results in Figure 8 shows that ASPEX is mostly used in Asia but also in North and South America and that there is no use in Europe. INCA Feature is mostly used in Europe but have significant use in Asia and there is also some in North America.

The 3D-method electrolytic extraction was also examined. It’s by far mostly used in Asia but there is significant use in Europe and a little in North America. The results can be seen in Figure 9.

There is a clear pattern that Asia is in a leading position when it comes to inclusion investigations.

Asia (China, Japan, South Korea) is very dominant in both ASPEX and EE but Europe (Finland, France, Italy, Sweden) trump them in INCA Feature. It is quite interesting that Europe didn’t have

Figure 8 Illustration of the usage of ASPEX and INCA Feature by continents

Figure 9 Illustration of the usage of EE by continents

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a single use of ASPEX in this study. Africa, Oceania and Australia aren’t even represented here.

The reason that they are not represented may be because that the ten biggest steel manufacturers are from either Asia or Europe, so it isn’t that strange that the Research and Development is mainly in Asia and Europe [5]. It’s important to have in mind that this is a small literature review with only 15 articles on each method, but it gives a good indicator of what the reality might be.

The other approach was to see if there were any correlations between use of the different methods and universities/companies. Table 3 shows a summary of all the universities/companies who used what method and during what year according to this study. The same conclusion can be drawn here, Asia and Europe are dominant when it comes the all the methods. The

universities that are represented the most are University of Science and Technology Beijing (China), Missouri University of Science & Technology (USA), Tohoku University (Japan) and KTH Royal Institute of Technology (Sweden).

o University of Science and Technology Beijing and Missouri University of Science &

Technology are the biggest users of ASPEX

o University of Science and Technology Beijing are by far the biggest users of INCA Feature o University of Science and Technology Beijing, Tohoku University and KTH Royal Institute of

Technology are the biggest users of Electrolytic extraction

There is no specific company that stands out when it comes to the use of the different methods due to the understandable limitations of published articles from companies. The only pattern that is noticeable is that most of the companies that participate in the articles are mostly Chinese companies and some Swedish and Japanese companies. Table 3 show which company/university that uses which technique, what year and from which reference. The reference list that is connected to Table 3 can be found in Appendix A.

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Table 3: Summary of usage of different methods in different companies/universities.

Method Country Company/University Years References

ASPEX Brazil

China

South Korea USA

- Federal University of Rio Grande do Sul (UFRGS)

- Baoshan Iron & Steel Co., Ltd.

Stainless Steel Business - Central South University

- Continuous Casting Department, CISDI Engineering Co. Ltd.

- Heibei Shougang Qian’an Iron &

Steel CO., LTD.

- Qian’an Steelmaking Co. Ltd., Shougang Group

- Shougang Research Institute of Technology

- Technology and Quality Departments of Shougang Qiangan

- University of Science and Technology Beijing

- Hanyang University - Colorado School of Mines - Missouri University of Science &

Technology - SSAB Muscatine

- University of Connecticut

2018

2016 2013 2011 2015 2011 2015 2016 2011- 2016

2013 2011 2008- 2015 2011 2018

15

6 16 9 5, 11 9 5, 11 6

1, 4, 6, 9, 13, 16

1 14 2, 7, 8, 9, 10 9 12

INCA Canada

China

Finland

- McMaster University - Western University

- Institute of Metal Research, Chinese Academy of Sciences - University of Science and

Technology Beijing

- Xining Special Steel Group, Co., Ltd.

- Aalto University

- Helsinki University of Technology

2013 2013 2013 2015- 2018 2018

2012 2006

31 31 28 3, 17, 18, 19, 20 18, 19

23 24 23

15 France

Italy

South Korea Sweden

USA

- Metso Paper, Inc

- CP2M, Faculté des Sciences et Techniques St Jérôme

- Equipe MecaSurf, ENSAM - Laboratoire de Physicochimie de

Surface UMR CNRS 7045, ENSCP

- Parma Spray Italia S.r.l.,

- University of Modena and Reggio Emilia

- Research Institute of Industrial Science and Technology

KTH Royal Institute of Technology - Ovako

- Sandvik Coromant - Swerea KIMAB - Uppsala University

- Carnegie Mellon University

2012

2008 2008 2008

2012 2012

2004 2009- 2015 2015 2015- 2016 2015 2015- 2016 2011

30 30 30

29 29

21 25, 27 27 26, 27 27 26, 27 22

EE China

Finland Japan

- Handan Iron and Steel Group Co.

Ltd.,

- Northeastern University - University of Science and

Technology Beijing

- Xining Special Steel Group, Co., Ltd.

- University of Oulu

- Furukawa Techno Material Co.

Ltd.

- Kanagawa Academy of Science and Technology

- Nagoya University

- National Institute of Technology, Akita College

- Nippon Yakin Kogyo Co., Ltd.

- NKK Corporation

2015 2017 2014- 2018 2018 2016 2017 1996 1996 2017 2011 1996 2004- 2017

37 40

17, 18, 36, 37

18 32 33 38 38 33 39, 42 38

33, 41, 43, 44

16 South

Korea

Sweden USA

- Tohoku University - Toyohashi University of

Technology

Pohang University of Science and Technology (POSTECH)

- KTH Royal Institute of Technology - Carnegie Mellon University

1996 2004

2011- 2016 2011

38 21

34, 35, 39, 41, 42, 45 22

3.3 Characterisation of non-metallic inclusions by INCA and EE

Composition

Based on the data from INCA Feature there were two different types of inclusion, oxides and sulphides. INCA Feature also analysed the inclusions a bit more intricate and found four different inclusions inside these two types. The inclusions that were analysed inside the group oxides were spinel (MgO*Al2O3), Al2O3 and CaO-Al2O3 and the group sulphides consisted of CaS and MnS. The pores and unclassified objects were also analysed by INCA Feature but those were excluded.

The obtained data from the INCA Feature analysis were analysed and can be seen in Figure 10 which is an illustration of how the types of inclusions changes between before and after the Ca- treatment.

Figure 10 Illustration of how the different types of inclusions changed before and after the Ca-treatment

The Ca-treatment affected the inclusions and the balance between oxides and sulphides. The oxides decreased from 99% of the total amount of inclusions to 68% and the sulphides increased from 1% to 32%. A closer look shows that the Spinels and Al2O3-inclusions decreased with more than 50% after the Ca-treatment. There was a little increase when it comes to the inclusions

37

17 30

13 32

38 1

32

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Before Ca-treatment After Ca-treatment Spinel (MgO*Al2O3) Al2O3 CaAl2O3 Sulphides: CaS + MnS

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CaO-Al2O3. The sulphides CaS and MnS on the other hand increased very much. It is expected after a Ca-treatment that the inclusions with Ca increases.

It is worth mentioning that INCA Feature chooses a main component and classify the inclusion after that. This means that there are other elements present in the inclusions which is excluded when it comes to the classification process.

The composition results from EE were similar to the results from INCA with the difference that inclusions < 2,8µm were able to be analysed. In Figure 11 a comparison of composition can be seen between the two samples. Both methods have approximately the same result when it comes to composition with some anomalies. One factor that must be considered is that a larger amount of inclusions was analysed with INCA Feature so there is a lot of more data from INCA than from EE. In 43-2: Mn% vs Size in Figure 11 there are no traces of Mn in the inclusions according to EE, this might be because fewer inclusions were analysed with EE. One other thing that must be mentioned is that some inclusions were removed from the result due to Fe+C>90%, meaning part of metal matrix, or the supposed inclusion was something else for example a slag particle.

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Figure 11 Graphs showing comparison of composition of examined inclusions. Mg, Al, Si, Ca and Mn were analysed in samples 43-1 and 43-2 with INCA Feature and EE

19

The EE method also resulted in mapping of the inclusions in sample 43-2, which is when chosen inclusions composition are more closely examined. In Figure 12 mapping of three inclusions can be seen. In this way the inclusions can easily be characterized. The inclusions in Figure 12 are CaAl2O3 a), CaS b) and some sort of cluster or fused inclusions that consists of CaS and Al2O3 c).

Mapping is a very good tool when it comes to identifying and characterize what kind of composition the inclusions have. It is clear from Figure 12 that there are a lot of different elements present besides the main components. For example, in Figure 12 a), which is classified as CaAl2O3,there is evidence that both sulphur and magnesium is present. In Figure 12 b) and c) there is also evidence of all elements presence in some amount, even if it is small.

Location

INCA Feature automatically scans the surface of the steel sample when doing so it locates where each and every one of the different inclusions are located. The coordinates of the location of the inclusions are obtained and saved in an excel file. This method gives a good estimation of were the inclusions are in the steel samples, but only the coordinates in the sample. There is no

information if the inclusions are present in the grain boundaries or where in the grains they exist.

In electrolytic extraction there is a way to find the location quite precise. If observations only are made on the film filter the parameter location can only partly be obtained. The only factor that can be obtained in terms of location is how deep the inclusions may have been located. This is achieved through ldis which gives the information of how much was extracted which in turn gives the potential depth were an inclusion might have been located. On the other hand, it is possible to look at the surface of the metal sample from which the inclusions were extracted. Here it’s possible to find where the inclusions are located for example if they are located in the grains or the grain boundaries.

Figure 12 Mapping of three different inclusions

20 Morphology

INCA Feature did only present some data regarding the morphology. One parameter that was found in the data was something called shape. This is something that INCA Feature labels based on the particle’s geometrical composition. Some of the different shapes are rectangle, circle, line and point. This gives some kind of understanding of what kind of morphology that was present.

The aspect ratio, which is length divided by width, can also give some sort of indication of the morphology. Though the only real conclusion that can be drawn from this, is if the inclusion is uniform, same length and width, with an aspect ratio of 1 or if the inclusion is elongated with an aspect ratio >5. These two factors can only give us an indication of what the inclusion’s

morphology might be. The conclusion is that no precise conclusions can be drawn about the morphology of the inclusions from this data only weak assumptions and guesses.

With the EE method the morphology was studied with help from a SEM both before and after the Ca-treatment and pictures were taken of chosen inclusions.

Figure 13 shows four different inclusions found from the electrolytic extraction of sample 43-1.

There is clear evidence that there are a lot of different shapes of inclusions present, there are irregular (Figure 13 a)), two irregular that has been fused together (Figure 13 b)) so they almost create a cluster and lastly two clusters that consists of at least eight individual inclusions that has been fused together (Figure 13 c) and d)).

Figure 14 shows four different pictures taken by SEM of the electrolytic extraction that was performed on sample 43-2. From the pictures it can clearly be stated that all of the inclusions

Figure 13 Morphology of different inclusions in sample 43-1 before Ca-treatment

21

have a spherical shape and maybe three spheres that has been fused together one in Figure 14 b) and two Figure 14 d). The spherical shape comes from the Ca-treatment.

Number

INCA Feature can automatically count the objects that are found. In total, INCA Feature found 1039 objects before the Ca-treatment and of those the program identified 916 to be inclusions the rest were identified as pores or defined as “unclassified”. After the Ca-treatment 353 objects were found and of those 288 were categorised as inclusions. As was mentioned above this was the count of inclusions on the surface of 43-1 and 43-2 with an area of 466 mm² and 488 mm². In the EE process the inclusions gets caught on the film filter which then gets examined with a manually operated SEM. There exists a method where a lot of pictures are taken in a series of the top and bottom of the film filter to come up with a number. When using EE to calculate number, the amount of extracted weight is often decided by the cleanness of the steel. In this experiment at least 1 g of the sample would have been needed to be extracted, because of the cleanness of the steel. This process was not done, so no number of inclusions were concluded by the EE method in this study.

Size

INCA Feature is a swift tool to analyse size. Figure 15 a) and b) shows the size (ECD) distribution of the inclusions regarding oxides and sulphides in the two samples 43-1 in a) and 43-2 in b). The intervals are between 2-4, 4-6 and >6 µm. The interval >6µm were chosen to include the rest of the inclusions. There was a total of 916 inclusions in sample 43-1 and of those 904 were oxides and 12 were sulphides. Sample 43-2 had 288 inclusions and of those 282 were oxides and 6 were sulphides. There were only 4 inclusions over 10 µm in sample 43-1 and all of them were oxides.

Figure 14 Morphology of different inclusions in sample 43-2 after Ca-treatment

22

In sample 43-2 there were only 16 inclusions over 10 µm and all of them were oxides except for one that was a sulphide.

Approximately a third of the inclusions disappeared after the Ca-treatment. The inclusions between 2-4 µm decreased with 75% from 672 to 162 inclusions and strangely enough the inclusions between >6 µm increased with more than 100% from 21 to 48 inclusions.

Unfortunately, no statistically relevant conclusions about the varying size could be drawn from the results from EE. This was due to the number of measured particles were too small. In the limited number of inclusions which were found the size varied between 3-14 µm in sample 43-1 and 0.8-9.5 µm in sample 43-2.

Figure 16 contains all inclusions from INCA and EE with morphology of NMI vs size (ECD) with sample 43-1 in Figure 16 a) and sample 43-2 in Figure 16 b). As can be seen there is a lot more data collected from INCA than EE. The aspect ratio decreased as I should after a Ca-treatment.

The INCA results shows that INCA Feature has not the ability to go below 2,8 µm in size. The red dots from EE shows that it has got that ability. This means that INCA Feature misses a lot of inclusions due to its limitations when it comes to size at the used SEM magnification.

Figure 15 a) Showing size (ECD) distribution in sample 43-1 from INCA Feature and b) showing size (ECD)

distribution in sample 43-2 from INCA Feature

Figure 16 Diagram show aspect ratio vs size (ECD) for INCA Feature and EE a) shows sample 43-1 and b) shows sample 43-2

23

Some features that INCA and EE discovered were excluded due to various reasons. One is that the composition of the inclusion was >90% Fe+C, meaning it’s probably a part of the metal matrix and not an inclusion. Another is when inspected with the SEM after EE, it can be seen that the inclusion is just dust or a particle from the polishing before the EE.

There is one more aspect regarding size that is connected to composition which is worth mentioning, that is the effect of the metal matrix. The effect of the metal matrix is when part of the metal matrix is accidentally scanned when INCA Feature tries to analyse the inclusion to determine its composition and size. This leads to that this part, that accidentally got scanned, gets categorized as the inclusion which in turn leads to that the inclusion’s size might get

misinterpreted. This is where the connection between composition and size in INCA Feature gets important to understand. If there is a large amount of Fe in the inclusion this can suggest that the size might have been misinterpreted. This gives the possibility to put the percentage of Fe

against size (ECD) to see if there are any proof that the metal matrix has an effect on the result.

Figure 17 shows the effect of the metal matrix for sample 43-1 in Figure 17 a) and sample 43-2 in Figure 17 b). The blue points are all the inclusions that were analysed and the red line is the mean of all the inclusions. Figure 17 shows that there is evidence that there are large amounts of Fe in the smallest inclusions that INCA Feature could analyse. This might indicate that these are misinterpreted and the size of these inclusions should be smaller than what INCA Feature says.

Figure 17 also shows that when the size gets larger the effect of the metal matrix decreases, the red line shows this and this might suggest that the larger inclusions are more correctly calculated when it comes to size.

Figure 17 Effect of metal matrix

24

4. Discussion

4.1 Environmental, ethical and social aspects

The use of the mentioned analysis methods has made a positive environmental impact historically due to a deeper understanding of the steel products but has also the potential for more positive effects on our environment. The investigation methods for NMIs during the steelmaking process have pushed the industry to optimize their production technology and the final quality of the steel. This have resulted in a decrease in energy and material consumption, improved quality of the steel which has led to more sustainable steel without defects.

The ethical aspect of this paper is more of a dilemma. These investigation methods can lead to innovations that have the potential of having a positive effect on both environmental and social aspects. The dilemma here might be if a company comes up with something revolutionary thanks to these methods that will give them a huge advantage in comparison to the other companies.

Would the company then share this intellectual property with the rest of the industry or keep it for themselves to make a profit?

From a social point of view the effects have also been positive. The automatic analysis methods of NMIs have resulted in improvements in some working conditions of the operators so routine analysis of NMIs have been avoided. The precise analysis of NMIs by using electrolytic extraction have improved our knowledge about metallurgical processes, the behaviours of NMIs and the steel quality. All these positive effects have led to that the industry promotes the development of high-level specialists within the analysis area of metallurgy.

There are two types of approaches that will contribute to the final comparison in this paper. The first one is based on the experimental results and the other one is based on the literature review.

One important note in this comparison is that the ASPEX method wasn’t experimentally examined in this study, it was only examined through the literature review.

4.2 Final discussion

The first approach is based on the literature review. 45 articles were examined in the literature review, see Appendix A for more intricate details regarding each article. 15 articles were chosen on each method to get a fair result.

ASPEX did analyse composition, size and number in almost all of the paper where it was used.

One interesting point is that more than half of the papers mentioned that ASPEX roughly estimated the morphology of the inclusions.

INCA Feature was mostly used to obtain composition and size, there were some mentions of number, morphology and one mention of location.

EE was always used to analyse the morphology and quite often with a focus on clusters, Composition and size was also analysed and a few mentions of number but never location.

The second approach is the experimental between the 2D-method INCA and the 3D-method EE.

Both INCA and EE can analyse composition, but sometimes strange elements are analysed such as indium, erbium, hafnium, francium etc. An advantage with EE is that points of interest can be

25

chosen by the operator so one inclusion can be analysed from many points to get a better understanding of the composition.

Location can be obtained with EE but only if the metal sample is investigated with a SEM, it’s impossible to obtain the location from the film filter. INCA scans the sample and every time an inclusion is found, according to the program, the location in the metal sample is registered. One disadvantage is that INCA cannot analyse as deep as EE potentially can. Morphology can roughly be obtained through INCA Feature but only a sense of the shape. EE is excellent at analysing the morphology because it extracts the inclusions which later can be looked at in a SEM. Clusters of inclusions can also be analysed which is impossible with INCA. INCA is superior when it comes to number in comparison to EE in a time-consuming manner. INCA analysed 916 inclusions and 288 inclusions in samples 43-1 and 43-2. In 43-1 and 43-2 2% respectively 5% of the features that were characterized as inclusions contain >90% Fe+C which means it is not an inclusion. This means that INCA can misinterpret up to 5% of the inclusions which is quite a high percentage. EE cannot analyse number in an effective way. If used to analyse number many photographs must be taken of the film filter and the manually counted. This in an ineffective and tedious procedure that can easily result in miscalculation.

EE is superior when it comes to size, because INCA cannot analyse sizes < 2,8 µm according to the data that was received from the company. This means that all inclusions < 2,8 µm won’t be registered which can lead to a huge loss of information. Calculating the size with the EE method is on the other hand tedious because all sizes are measured manually.

The two approaches give almost the same pictures of what advantages and disadvantages that each method has but with some small differences.

All the methods work well regarding composition of the inclusions. The 2D-methods are superior when it comes to calculating number and size fast. With exception for inclusions < 2,8µm, then EE is the best method. Location is the only parameter that has been hard to estimate because it is rarely mentioned in paper and articles. Morphology is best analysed by EE because it is possible to get a 3D view of the inclusions and investigate clusters. In some cases, ASPEX was used to roughly estimate morphology but it cannot compare to the EE method. Table 4 shows a compilation of advantages and disadvantages of 2D- and 3D-methods regarding the parameters:

composition, location, morphology, number and size. The different symbols are: advantages = +, disadvantages = -, small advantages = (+) and small disadvantage = (-).

Table 4: Compilation of advantages and disadvantages of 2D and 3D-methods

Parameters 2D – ASPEX 2D – INCA

Feature

3D – EE + SEM

Composition + + +

Location - - -(+)

Morphology -(+) -(+) +

Number + +(-) +

Size + +(-) +

26

5. Conclusions

A literature review on ASPEX, INCA Feature and EE with 15 articles on each was done along with an experimental part with EE and INCA Feature. This laid the foundation for investigating non- metallic inclusions in metal samples with 2D- and 3D-analytical methods and finding the advantages and disadvantages of each. These are the conclusions that could be drawn:

• Asia is dominant when it comes to the use of ASPEX and EE, but Europe is the biggest user of INCA Feature.

• The biggest ASPEX users are University of Science and Technology Beijing and Missouri University of Science & Technology.

• University of Science and Technology Beijing is the biggest INCA Feature user.

• University of Science and Technology Beijing, Tohoku University and KTH Royal Institute of Technology are the biggest users of Electrolytic extraction.

• No conclusions can be drawn about specific companies who uses ASPEX, INCA Feature and EE but they are mostly Chinese companies.

• The 3D-method EE is the only one that can examine clusters and really understand the morphology of the inclusions. If the purpose of the investigation is understanding what kind of morphology exists this method is necessary.

• The 2D-method INCA Feature data that was examined had up to 5% inaccuracy regarding distinguishing between inclusions and pores/unclassified.

• The 2D-methods are superior if the purpose of the examination is to obtain number, size and composition in a swift way. The EE process can take over 5 hours before a viable sample that can be examined can be obtained.

• The 3D-method EE is superior if the purpose is to get an understanding of inclusions deeper inside of the metal sample. The 2D-methods are limited when it comes to the depth that they can penetrate the metal matrix to investigate the inclusions. EE can investigate much deeper inside the metal sample. During the experiments a depth of 309 µm was achieved with EE.

27

• All methods work well to examine the composition of the inclusions. Both 2D- and 3D- methods often find small amounts of strange elements such as francium etc. One major advantage of EE is that after the extraction when the film filter is examined by a SEM the operator can manually chose which part or parts of the inclusion that shall be analysed.

Mapping information is also available with the SEM, this makes it much easier to understand the inclusions composition and especially clusters. The advantage of the 2D-method is that the analysis is much faster.

• Both the 3D-method and the 2D-methods are used off-line for Research & Development but the 3D-method is more time-consuming than the 2D-method.

• One interesting aspect is the effect of the metal matrix on size (ECD) with INCA Feature. It seems that the smaller the inclusions are the larger amount of Fe they contain and this effect decreases as the size (ECD) increases. This might lead to misinterpretations of the size of the inclusions, especially the small inclusions.

28

6. Future work

The area for investigations of NMIs is exciting and quite unexplored, and there is a lot of future work that can be done. The topics that would be most interesting to examine are the following:

• Examine more clean steels and compare to get a more accurate statistical result in the differences between 2D- and 3D-analytical methods. Are the results in this paper consistent with other clean steels?

• Make an experimental comparison with data from ASPEX, INCA Feature and EE. This paper only focused on an experimental comparison between INCA and EE. Would the 2D-methods INCA Feature and ASPEX have the same result or would they differ?

• Investigate the effect of the metal matrix on the inclusions in INCA Feature and the connection between composition and size (ECD). Is the size of the inclusions with the 2D- method INCA Feature misinterpret due to the effect of the metal matrix?

• The effect of the metal matrix might not be the only thing that may lead to misinterpretation of the size (ECD). Does the carbon polishing of the metal sample in INCA Feature also affect the calculation of size?

29

7. Acknowledgement

I wish to express my deepest gratitude to my mentor Andrey Karasev from the Unit of Process at the department of Material Science and Engineering at KTH for providing me with the

opportunity to take a deep dive into his world and expertise. His guidance and personal attention have been a great motivational factor in my research and a necessity for the completion of this project.

.

Stockholm, May 2019

30

8. References

[1] Mapelli, C. (2008). Non-metallic inclusions and clean steel. Metallurgia Italiana. 100.

[2] S. K. Sarna, “Non metal inclusions in steels”, ISPAT GURU, 2015-01-08.

http://ispatguru.com/non-metal-inclusions-in-steels/ [Retrieved: 2019-02-24]

[3} Y. Ren, Y. Wang, S. Li, L. Zhang, X. Zuo, S. N. Lekakh and K. Peaslee: The Minerals, Metals &

Materials Society and ASM International 2014, DOI: 10.1007/s11663-014-0042-y

[4] V. Singh. (2009). Inclusion modification in steel castings using automated inclusion analysis.

[5] M. Jiang, Z. Hu, X. Wang and J. Pak: ISIJ Int., 53 (2013), 1386-1391

[6] V. Thapliyal, A. Kumar, D. Robertson and J. Smith: ISIJ Int., 55 (2015), 190-199

[7] W. Yang, L. Zhang, X. Wang, Y. Ren, X. Liu and Q. Shan. ISIJ Int., 53, No 8 (2013), 1401-1410 [8] A. Roiko, H. Hänninen and H. Vuorikari: International Journal of Fatigue 41 (2012), 158-167 [9] INCA Energy, ”INCA”, Corex, 2018 [Online] Available: https://www.corex.co.uk/electron- microscopy-facility/PDF/11-INCAEnergy-Brochure.pdf [Retrieved: 2019-04-12]

[10] S.S. Chaeikar. (2013). Examination of inclusion size distributions in duplex stainless steel using electrolytic extraction.

[11] T. Gram and A. Vickerfält. (2015). Characterisation of non-metallic inclusions according to morphology and composition.

[12] D. Janis, A. Karasev and P. G. Jönsson: ISIJ Int. 55, No. 10 (2015), 2173-2181 [13] T. Sipola, T. Alatarvas, T. Fabritius and P. Perämäki: ISIJ Int. 56 No. 8, 1445-1451

31

Appendix A: Detailed summary of literature review

References visited: 2019-03-30

[Nr of Ref.] = Number of references, you will find the references at the bottom of this appendix [Nr of Ref.],

[Ref.], Year, Author

Cou ntry

Met hod

Steel Inclusi

on

Comment

[1]

[https://www.jsta ge.jst.go.jp/article /isijinternational/5 3/8/53_1386/_pdf /-char/ja], 2013, Min JIANG¹, Zhiyong HU², Xinhua WANG² and Jong-Jin PAK¹

Kor ea¹ and Chin a²

ASP EX

Zr–Al Deoxidiz ed Low Carbon Steel 1. Zr High:

0,072%

2. Zr Medium:

0,0085%

3. Zr Low:

0,0008%

1. ZrO2

2.

ZrO2– TiOx

with a layer of (SiO2– MnO–

Al2O3– (MnS)) and MnS 3.

MgO–

Al2O3– SiO2– MnO

Size:

1. 70% 1-5µm 2. 90% 1-5µm 3. 70% 1-5µm Number density:

1, 2: 100/mm² 3. 347/mm²

[2]

[https://www.jsta ge.jst.go.jp/article /isijinternational/5 5/1/55_190/_pdf/

-char/ja], 2015, Vivek THAPLIYAL, Abhishek KUMAR, David ROBERTSON and Jeffrey SMITH

USA ASP

EX

Si–Mn Killed Steels (with FeTi addition) 1. Before addition 2. After addition

1. Mn–

Si–Al–O based oxide inclusio ns 2. Ti3O5

1.

Size: 1-3µm Density number:

1500/mm2 Morphology:

Spherical 2.

Density number:

1000/mm2, Size: most were 1-3,5µm

32 [3]

[https://www.jsta ge.jst.go.jp/article /isijinternational/a dvpub/0/advpub_I SIJINT-2018- 509/_pdf/- char/ja], 2018, Jinlong LU, Yunpeng WANG, Qiming WANG, Huijing CHENG and Guoguang CHENG

Chin a

INC A

Medium Carbon Non- Quenche d and Tempere d Steel (NQT) S1:

Lower Zr, Higher Al and O S2: Zr Killed steel, Lower Al and O (C, Si, Mn, P, S, V and N are similar in two sample steels)

MnS inclusio ns, Al2O3

S1:

Average Size:

0,97µm

Density number:

768,3/mm2 Morphology:

point-like and some are short rod-like S2:

Average Size:

1.16µm

Density number:

466.4/mm2 Morphology:

point-like and some are short rod-like

[4]

[https://www.jsta ge.jst.go.jp/article /isijinternational/5 5/1/55_126/_pdf/

-char/ja], 2015, Guangwei YANG and Xinhua WANG

Chin a

ASP EX

Low Carbon Aluminiu m Killed steel (LCAK steel) with ultra-low sulphur content (0.04–

0.065%

Al, 9 ppm S) (With Calcium Addition)

CaO–

Al2O3

with differe nt amoun ts of CaS (after 20 min of additio n)

Size:

Most 1-10µm, but some >10µm Density number:

7/mm²

Composition:

43% Al2O3, 28%

CaOand 18% CaS Morphology:

Spherical

33 [5]

[https://www.jsta ge.jst.go.jp/article /isijinternational/5 5/10/55_ISIJINT- 2015-064/_pdf/- char/ja], 2015, Dongwei ZHAO, Haibo LI, Chunlin BAO and Jian YANG

Chin a

ASP EX

Aluminiu m killed steel with various calcium aluminat es

Al2O3– CaS based inclusio ns

They examine 5 different steps so it’s too much information to be discussed here

[6]

[https://www.jsta ge.jst.go.jp/article /isijinternational/5 6/4/56_ISIJINT- 2015-694/_pdf/- char/ja], 2016, Shusen LI, Lifeng ZHANG, Ying REN Wen FANG, Wen YANG, Shijie SHAO, Jun YANG and Weidong MAO

Chin a

ASP EX

Silicon killed Stainless Steels

Al2O3, SiO2, CaO and MnO

From cast start- 200min (2 ladle changes) Size:

1.5-2-5µm Composition:

30→35% SiO2

10→20% Al2O3

10→20% CaO 40→20% MnO Density number:

40/mm²

[7]

[https://www.rese archgate.net/publi cation/318311563 _AN_SEMEDS_STA TISTICAL_STUDY_

OF_THE_EFFECT_

OF_MINI-

MILL_PRACTICES_

ON_THE_INCLUSI ON_POPULATION _IN_LIQUID_STEEL ], 2015, Obinna Abada, Ronald J O’Malley, Mark Harris and Simon Lekakh

USA ASP

EX

Aluminiu m killed steel

CaS and MgO inclusio ns

Size:

1-100µm Morphology:

Spinel

Density number:

35-130/mm²

34 [8]

[https://apps.dtic.

mil/dtic/tr/fulltext /u2/a504465.pdf], 2008, D.C. Van Aken

USA ASP

EX

High mangane se and high aluminiu m austeniti c steel (age hardene d, one is calcium treated and one is not)

Inclusio ns are sulphid es and oxides of high Mn concen tration

Size:

1-175µm, the graph just had 0- 40µm as interval

[9]

[http://web.mst.e du/~lekakhs/webp age%20Lekakh/Ar ticles/met%20tran s%20lifeng.pdf], 2014, YING REN, YUFENG WANG, SHUSEN LI, LIFENG ZHANG, XIANGJUN ZUO, SIMON N.

LEKAKH, and KENT PEASLEE

Chin a, USA

ASP EX, Acid Extr acti on

Aluminiu m killed medium carbon steel

Al2O3- MgO, SiO2, Al2O3, CaS, MnS, Na-K, Al2O3- SiO2, Al2O3- CaO

Morphology:

Spinel single or cluster,

Spherical, irregularly shaped slag Average Size:

2.5-55.2µm Density number:

0.001-2/mm²

[10]

[http://scholarsmi ne.mst.edu/cgi/vi ewcontent.cgi?arti cle=6422&context

=masters_theses], 2009, Vintee Singh

USA ASP

EX

Medium- carbon steel

MnO, TiO2

Al2O3, MnSiO3

, MnS, CA and CaS, MnS- CaS

Size:

1-7µm Fractions of inclusions:

0.0002-0.0016 Morphology:

Non-spherical, spherical

35 [11]

[https://www.scie ncedirect.com/sci ence/article/pii/S1 006706X15301357 ], 2015, Hai-bo LI, Dong-wei ZHAO, Guo-sen ZHU, Chun-lin BAO, Jian YANG

Chin a

ASP EX

J55 Steel MnS and CaO- Al2O3- CaS compo site inclusio n

Size:

1<x<110µm Morphology:

String (MnS), globular (CaO-..) Density number:

1-5/mm² (MnS) 15-32/mm² (CaO-..) [12]

[https://www.scie ncedirect.com/sci ence/article/pii/S0 264127517310948 ], 2018, Yu Sun, Rainer J. Hebert, Mark Aindow

USA ASP

EX

17-4PH Stainless Steel (ASTM A564 or AMS 5643)

Oxide inclusio ns that contain O, Si, Cr, Mn, Fe, Cu and Al

Morphology:

Equi-axed inclusion, irregularly- shaped inclusion, spherical Size:

1-32µm [13]

[https://www.scie ncedirect.com/sci ence/article/pii/S1 006706X1530114X ], 2015, Wen YANG, Ying ZHANG, Li-Feng ZHANG, Hao-Jian DUAN, Li WANG

Chin a

ASP EX

Ti- stabilize d Ultra- low Carbon Steel

Al2O3, Al-Ti-O inclusio ns and TiN

Size:

1-30µm Morphology:

Spherical, Cluster, crystal

[14]

[https://www.scie ncedirect.com/sci ence/article/pii/S1 87770581100289X ], 2011, A.B.

Nissan, K.O.

Findley, A.S.

Hering

USA ASP

EX

1045 Inductio n hardene d steel alloy

MnS, MnCaS, MnAIS, Al2O3, Al2O3- MgO

Measured endurance levels and only had µm² regarding the inclusions, no other data

36 [15]

[https://www.scie ncedirect.com/sci ence/article/pii/S2 23878541730786X ], 2018, Pedro Cunha Alves, Vinicius Cardoso da Rocha, Julio Anibal Morales Pereria, Wagner Viana Bielefeldt, Antônio Cezar Faria Vilela

Braz il

ASP EX

SAE 1055 Carbon Steel

CaO, SiO2, Al2O3, ZrO2

Size:

1->15µm

Density number:

0.5-15/mm²

[16]

[https://www.scie ncedirect.com/sci ence/article/pii/S1 006706X13600400 ], 2013, Shu-Hao CHEN, Xin-Hua WANG, Xiao-Fei HE, Wan-Jun WANG, Min Jiang

Chin a

ASP EX

72 grade tire cord steel

MnS, MnO, SiO2, Al2O3

Density number:

17-20/mm² (sulphide inclusions) 7-9.5/mm² (oxide inclusions)

[17]

[https://www.jsta ge.jst.go.jp/article /isijinternational/5 8/10/58_ISIJINT- 2018-072/_pdf/- char/ja], 2018, Shijian LI,

Guoguang CHENG, Zhiqi MIAO, Weixing DAI, Lie CHEN and Zhiquan LIU

Chin a

INC A, Elec trol ytic Extr acti on (AA type Elec trol ytic soul utio n)

G20CrNi 2Mo Carburiz ed Bearing Steel during

MgO–

Al2O3

(Low MgO), Al2O3- based CaO–

(MgO)–

Al2O3, CaO–

Al2O3

Size:

1->10µm

Density number:

0,04-4.73/mm² Morphology:

Irregular, Spherical, globular