evaluation of 25 mhz ultrasonic testing for detection of

IN THE FIELD OF TECHNOLOGYDEGREE PROJECT MATERIALS DESIGN AND ENGINEERINGAND THE MAIN FIELD OF STUDYMATERIALS SCIENCE AND ENGINEERING,SECOND CYCLE, 30 CREDITS

, STOCKHOLM SWEDEN 2021

Evaluation of 25 MHz Ultrasonic Testing for Detection of Non-Metallic Inclusions in Steel

HENRIETTA ISAKSSON

KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT

i

Abstract

Quantification of inclusions is important since it is correlated to the steel’s fatigue

properties. One method that could be further developed for detection of inclusions in steel

is ultrasonic testing (UST). The aim of this study is to investigate what type of inclusions, in

terms of size, morphology and chemical composition, that can be detected with 25 MHz UST,

and what type of inclusion that cannot be detected. This was done by firstly scanning 74

steel samples with 25 MHz UST, and then fatigue test the same samples until fracture. The

inclusion that caused the fracture was then analysed with microscopy and compared with

the results from the 25 MHz UST. It was found that Mn-Mg-sulphides, Ca-sulphides, oxy

sulphides and complex oxides are difficult to detect with 25 MHz UST. Globular oxides can be

detected with 25 MHz UST, at least down to an area of 8300 µm2 and if they are not too

fragmented due to rolling. The results indicate that oxy sulphide stringers can be detected

with 25 MHz UST if the inclusion have oxides in direct contact with the steel matrix, rather

than oxides encapsulated by sulphides.

Keywords: Ultrasonic Testing; High Frequency Ultrasonic Testing; Quantification of

inclusions; Fatigue Testing

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Sammanfattning

Kvantifiering av inneslutningar är viktigt då det korrelerar med stålets

utmattningsegenskaper. En metod som kan utvecklas för detektering av inneslutningar i stål

är ultraljudstestning (UT). Syftet med den här studien är att undersöka vilken typ av

inneslutningar, med avseende på storlek, morfologi och kemisk sammansättning, som kan

detekteras med 25 MHz UT, och vilken typ av inneslutningar som inte kan detekteras. Detta

gjordes genom att först skanna 74 stålprover med 25 MHz UT, och sedan utmattningstesta

samma prover tills de gick till brott. Inneslutningen som orsakade brottet analyserades

sedan med mikroskopi och jämfördes med resultaten från 25 MHz UT. Det visade sig att Mn-

Mg-sulfider, Ca-sulfider, oxisulfider och komplexa oxider är svåra att upptäcka med 25 MHz

UT. Globulära oxider kan upptäckas, åtminstone ner till en area på 8300 µm2 och om den

inte har blivit för fragmenterad av valsning. Resultaten indikerar att oxisulfider kan

detekteras om oxiderna är i direkt kontakt med stålmatrisen, i stället för att oxiderna är

inkapslade av sulfider.

Nyckelord: Ultraljudstestning; Högfrekvent ultraljudstestning; Kvantifiering av

inneslutningar; Utmattningstestning

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Acknowledgement

I would like to acknowledge all the people that made my thesis work possible, and everyone

that made it a joy to write this thesis at Group R&D at Ovako Hofors.

First of all, I would like to thank Patrik Ölund, Head of Group R&D Ovako Hofors, Göran

Nyström, EVP Group Marketing & Technology Ovako, and Helena Hagman, Head of Talent

and Leadership Development Ovako Group, for giving me the opportunity to write my

master’s thesis at Ovako Group R&D in Hofors. Thank you to Fredrik Lindberg, Product Line

Manager Ovako Hällefors, for recommending the thesis work during the BKW-fair at KTH.

Special thanks to my supervisor, Joakim Fagerlund, Senior R&D Engineer at Ovako Group

R&D, for guidance of the scientific work done for this thesis. I am particularly grateful for the

assistance given by Larsa Fröjd, Senior Technician Group R&D Ovako Hofors, for all the hours

spent with the ultrasonic equipment, discussing things in several dimensions at the same

time. This thesis work would not have at all as fun without Larsa. Assistance provided by

Niclas Granlund, Technician Group R&D Ovako Hofors, regarding sample preparation was

greatly appreciated. Furthermore, I wish to acknowledge the never-ending source of wisdom

provided by Garry Wicks, R&D Engineer Group R&D Ovako Hofors, regarding inclusions and

scanning electron microscopy. Thank you to my supervisor Peter Hedström, Professor at

department Materials Science and Engineering KTH, for guidance regarding scientific

presentation of my thesis work. The curious questions and discussions in the lunchroom

together with Simon Lille, Jan-Erik Andersson, Stefan Akterhag, Erik Claesson, Patrik Holm,

Elias Löthman and Andreas Rindeskär, was particularly appreciated.

I would also like to extend my thanks to LG, the good Samaritan, for rescuing me when my

car broke down, Patrik Ölund’s wife for lending me her cross-country skis, Ove Sandberg for

his patience when I tried to learn cross-country skiing, and Carola Snar & the bootcamp gang

for the enthusiastic and energetic work out sessions 6 in the morning in April weather.

Thanks to Iréne Storm, Petra Hedberg, Arun Thakur and Thomas Björk, for the safari

adventures in Hofors, and the lovely company at Hammarvägen 1. And a very special thanks

to my partner Petter for all the support, which have made it possible for me to accomplish

my studies with my mental health intact.

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Acronyms

UST Ultrasonic Testing

RBF Rotating Bending Fatigue

SEM

EDS

Scanning Electron Microscopy

Energy-Dispersive X-ray Spectroscopy

SAM Scanning Acoustic Microscopy

LOM

FBH

SDH

DAC

DSG

ROI

ECD

Light Optical Microscopy

Flat Bottom Hole

Side Drilled Hole

Distance Amplitude Correction

Distance-Gain-Size

Region of Interest

Equivalent Circle Diameter

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Table of Contents

1 Introduction ........................................................................................................................ 1

1.1 Aim ............................................................................................................................... 1

2 Inclusions, Fatigue and Ultrasonic Testing.......................................................................... 2

2.1 Fatigue Strength and Inclusions .................................................................................. 2

2.2 Rotating Bending Fatigue Testing ................................................................................ 3

2.3 Ultrasonic Testing ........................................................................................................ 5

2.3.1 Scanning acoustic microscopy .............................................................................. 5

2.3.2 Data presentation................................................................................................. 7

2.3.3 Acoustic impedance ............................................................................................. 8

2.3.4 Focal plane............................................................................................................ 8

2.3.5 Amplification and defect sizing ............................................................................ 9

3 Method.............................................................................................................................. 12

3.1 Sample Preparation ................................................................................................... 12

3.2 10 MHz Ultrasonic Testing ......................................................................................... 15

3.3 25 MHz Ultrasonic Testing ......................................................................................... 16

3.4 Rotating Bending Fatigue Testing .............................................................................. 19

3.5 Scanning Electron Microscopy ................................................................................... 19

3.6 Light Optical Microscopy ........................................................................................... 20

3.7 Layer Analysis ............................................................................................................ 20

3.8 Region of Interest in 25 MHz Ultrasonic Testing ....................................................... 22

4 Results ............................................................................................................................... 25

4.1 10 MHz vs 25 MHz Ultrasonic Testing ....................................................................... 25

4.2 Scanning Electron Microscopy & Energy-Dispersive X-ray Spectroscopy ................. 26

4.2.1 Classification of inclusions .................................................................................. 26

4.3 25 MHz Ultrasonic Testing Results ............................................................................ 28

4.4 Detected Inclusions ................................................................................................... 32

4.5 Comparison of Inclusions That Were Detected and Those Who Were Not.............. 44

5 Discussion .......................................................................................................................... 53

5.1 10 MHz vs 25 MHz Ultrasonic Testing ....................................................................... 53

5.2 Comparison of Strategy 1 and Strategy 2 .................................................................. 53

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5.3 Detection of Inclusions .............................................................................................. 53

5.3.1 Manganese-magnesium-sulphides .................................................................... 54

5.3.2 Oxy sulphide stringers ........................................................................................ 54

5.3.3 Globular oxides ................................................................................................... 55

5.3.4 Calcium sulphides ............................................................................................... 55

5.3.5 Complex oxides .................................................................................................. 56

5.3.6 Oxy sulphides ..................................................................................................... 56

5.4 Morphology ............................................................................................................... 56

5.5 Acoustic Impedance................................................................................................... 57

5.6 Sources of Error ......................................................................................................... 57

5.7 Social and Ethical Aspects .......................................................................................... 58

6 Conclusions ....................................................................................................................... 59

7 Recommendations ............................................................................................................ 60

8 References ........................................................................................................................ 61

1

1 Introduction

Ovako is a European manufacturer of high-quality engineering steel with applications in

bearings, powertrains and hydraulic cylinders. Ovako has three production flows: Hofors-

Hällefors and Smedjebacken-Boxholm in Sweden and Imatra in Finland. Hofors-Hällefors

produces high-quality long-steel products with high cleanliness and fatigue strength mainly

for the bearing, automotive and mining industries.

Ovako is continuously improving analysing techniques for quantifying inclusions, so that they

can ensure a high steel quality regarding fatigue strength for their customers. There is an

issue today where the standards available for quantifying inclusions in steel are not

sufficient since today’s clean steel has very small and dispersed inclusions. An example of

this is that not any indication of inclusions has been found with the traditional blue fracture

testing at Ovako for over 30 years, and yet customers still demand this type of test.

One of the methods for quantifying inclusions is fatigue testing, that has the disadvantage of

being time-consuming and needing a lot of samples, since the tested volume per sample is

small. By using ultrasonic testing (UST), a larger volume can be analysed, and it is considered

a fast, cheap, and reliable method for predicting steel’s fatigue properties. An increased

frequency with UST indicates smaller inclusions while a decreased frequency allows for a

larger volume to be tested. UST with 10 MHz is used on a regular basis at Ovako for quality

control, and the company is now looking to develop this technique further and is

investigating UST with higher frequencies.

Ovako is currently evaluating 25 MHz UST and earlier investigations show that 25 MHz UST

can detect artificial holes that are 50 µm in diameter. It is desired to establish the relation

between UST results and true material defects, so that UST with 25 MHz might be used

directly to accurately model the fatigue properties of the steel.

1.1 Aim

The aim of this study is to investigate what type of inclusions, in terms of size, morphology

and chemical composition, that can be detected with 25 MHz UST, and what type of

inclusions that cannot be detected.

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2 Inclusions, Fatigue and Ultrasonic Testing

The material used in this study is ingot casted material, that has been hot rolled into billets.

Bearings are often operating under severe static and cyclic loads in harsh environments and

the bearing steel is therefore characterized by high hardness, excellent fatigue properties

and wear resistance [1]. One important factor for achieving excellent fatigue properties is to

avoid inclusions [2]. The following background covers more about fatigue strength,

inclusions, fatigue testing and UST.

2.1 Fatigue Strength and Inclusions

Fatigue fracture occurs during cyclic loading at loads below the maximum static load and it is

the cause for around 90% of all mechanical service failures [2]. Fatigue starts with crack

initiation in the component or at the surface and continues with propagation of the crack

through the material. Finally, when the crack has grown enough so that the component

cannot withstand the load it ends with a rapid crack growth that causes complete fracture.

Fatigue is critical, firstly since it is difficult to discover initiated internal cracks, and secondly

since the final stage of fracture occurs so rapidly. In worst case, the fatigue failure can cause

complete structure failure that can lead to fatality. A typical starting point for crack initiation

in steel is around an inclusion, where there can be a higher stress concentration, see Figure

1.

Figure 1. Illustration of a material with an applied load in two directions, the red lines illustrate the stress concentration that piles up around a defect, such as a hole or inclusion [3].

Important factors that influence fatigue strength is the shape, size and chemical composition

of the inclusions as well as the inclusion’s adhesion to the matrix and the elastic constants of

the inclusions and the matrix [4]. Some types of inclusions are deformable and will be

elongated during rolling. Inclusions can be elongated enough to become non-damaging

because of the small cross-section areas. However, this also depends on the direction of

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loading, where loading transversal to the rolling direction becomes more critical [5], see

Figure 2.

Figure 2. Illustration of inclusions that have been elongated in the rolling direction, and how the elongated inclusions’ area is more critical for transverse loading than for longitudinal [2].

Fatigue cracks can initiate easily if the adhesion of the inclusion to the matrix is not perfect

[6]. The type of inclusion will also affect the fatigue strength, see Figure 3.

Figure 3. Relative harmful effect based on a harmful index developed with fatigue testing. Harmful index on the y-axis and average inclusion diameter on the x-axis. Four inclusion types are plotted,

where globular oxides show to be most harmful [7].

2.2 Rotating Bending Fatigue Testing

Fatigue testing aims to predict the fatigue life, meaning the number of cycles of a specific

stress amplitude before complete fracture of the sample. Rotating bending fatigue (RBF)

testing is performed with hourglass-shaped samples with a circular cross-section, see Figure

4.

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Figure 4. Illustration of a steel sample used for rotating bending fatigue testing [8].

The samples are bent and rotated at the same time, causing the stresses in the circular

cross-section to alternate between compression and tension. Where the fracture will initiate

depends on which types of inclusions that exists in the sample and where in the sample the

inclusions are located. This is since the stress will vary through the waist of the sample, as

illustrated in Figure 5, where the highest local stress is present at the centre of the sample

and close to the sample surface. The local stress decreases further away from the surface, as

well as further away from the centre of the sample.

Figure 5. A plot that shows how the local stress varies through the material, with the sample’s radius on the y-axis and distance from the centre of the sample on the x-axis. The different coloured lines

represent different local stress amplitudes [9].

After failure it is common that a characteristic circled area, called fisheye, around the

inclusion that initiated the fracture can be observed on the fracture surface, see Figure 6 [1].

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Figure 6. The left image shows a typical fisheye formed around an inclusion. The right image shows a fisheye and inclusion in detail [10].

A disadvantage with using RBF for analysing steel is that only a small volume will be exposed

to high stress, this means that larger inclusions can be present in the waist of the sample

while a smaller inclusion will cause the fracture. The advantage compared to optical

examination on polished transverse sections is that RBF testing generally shows larger

inclusions. This is because clean bearing steel have a low frequency of inclusions, hence the

chance of including the largest inclusion in the sample surface for microscopy is small.

Another issue when examining the size of an inclusion with optical microscopy is that there

is a risk that the most part of the inclusion is hidden under the surface.

2.3 Ultrasonic Testing

Ultrasound refers to sound waves with frequencies higher than 20 kHz. UST is a non-

destructive technique which uses ultrasonic waves that are emitted into the test sample and

the reflected sound waves can be analysed, which can indicate defects such as cracks, voids,

inclusions, and delamination. There is several different equipment that can be used for UST,

and this study focuses on Scanning Acoustic Microscopy (SAM).

2.3.1 Scanning acoustic microscopy

To produce ultrasonic waves the SAM equipment consists of a radio frequency tone-burst

source that generates electrical signals which are transmitted through a circulator to a

transducer with a piezoelectric element. Piezoelectric materials vibrate when electricity is

applied, so the electrical signal is converted to an acoustic signal, i.e. the ultrasonic wave.

The probe consists of the transducer that is placed at the top of a rod and the wave travels

from the top to a lens at the bottom. The lens can have different geometries, which will

focus the ultrasonic wave in different manners. It could for example be planar, cylindrical or

spherical focusing [11] [12]. This thesis work focuses on spherical focusing.

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The probe and the sample are immersed in a liquid medium, usually distilled water, so that

the high frequency sound waves can propagate. When the sound waves encounter a change

in acoustic impedance, such as a material boundary, a part of the sound waves is reflected,

and some continue to propagate through the boundary, this is called transmission. Two

different inspection modes can be used, pulse-echo and through transmission, the latter is

using a receiver on the opposite side. This study focuses on the pulse-echo inspection mode.

Peak amplitude imaging is used where defects result in changes in the amount or strength of

ultrasound reflected, see Figure 7 for illustration, with time on the x-axis and amplitude on

the y-axis.

Figure 7. Left: schematic illustration of the transducer and the principle of the interaction of the sound waves with the sample. Right: the amplitude of the reflected echo presented on a time-axis, where

the red curve is for surface echo, blue is for the defect and green is for the backwall echo [13].

The reflected ultrasonic beam is converted backwards to an electric signal by the

piezoelectrical element in the transducer, and this signal varies in voltage. The electrical

signal at the receiver needs to be amplified, depending on the insertion loss. Transmission

leaks, internal reflections, and reflections from the specimen are other factors that affects

the electric signal. The solution to this is to select the desired reflections by using a

rectangular wave, i.e. a wave that varies between two levels, from a double balanced mixer,

and this is known as the first gate. The double balanced mixer is a frequency mixer that

reduces the input signals, which makes it possible to reduce distortion. The peak detection

technique is used, where a circuit, including a diode and a capacitor, detects the peak of the

electric signal amplitude. By using a second gate within the first gate, the gate noise can be

removed, this is known as the blanking technique. The detected signal is converted from

analogue to digital and stored into a memory. Then again it is converted to an analogue

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signal. The probe scans the specimen and the intensity value for each location can be

displayed on a computer screen in a two-dimensional image [11].

2.3.2 Data presentation

Figure 8 shows illustrations of how the digital waveforms can be presented. A-scans shows

raw waveforms, while B- and C-scan shows acoustical images. B-scan shows a 2D image

where each vertical line of pixels is an individual A-scan presented in amplitude-colour

representation, see Figure 9. It is a cross-sectional display that shows location of interfaces

in the material at various depths. The C-scan shows a 2D image where the colour of each

pixel represents the amplitude within a specific gated depth range. It shows a cross-section

in a plane perpendicular to the directions of the A-scan [14]. This thesis work focuses on C-

scan.

Figure 8. Illustration of how the digital waveforms can be presented with scanning acoustic microscopy [13].

Figure 9. A typical amplitude-colour bar, where blue is lowest amplitude and red is highest amplitude. This is the colour bar used in this thesis work. Another common colour bar is in greyscale.

Advanced numerical processing is used to be able to extract important information in thin

specimens when there is a risk of obtaining interleaving signals. By using data about the

material’s acoustic properties, the interference can be determined. Quantitative

measurements of the sound speed can be obtained by either varying the frequency and

perform serial measurements or by fast Fourier transform of a single broadband pulse. The

lateral resolution is dependent on the width of the acoustic beam. With spherical focusing

8

this is approximately equal to the angular aperture multiplied by the wavelength. The focal

distance of the acoustic lens determines the certain depth that gives the maximal resolution.

Increasing the frequency limits the imaging depth and therefore it is necessary with

repeated scanning with focus at different depths when scanning thick specimens [14].

Increased frequency will however detect smaller defects. Other settings for SAM that will

affect detectability of defects are pulses per second and scanning speed.

2.3.3 Acoustic impedance

A part of the ultrasonic wave will be reflected when it encounters a change in acoustic

impedance. Impedance is defined in Equation 1:

𝑍 = 𝜌𝑣 (1)

Where ρ is the density of the material [kg/m3], and v is the speed of sound through the

material [m/s]. A greater difference in impedance between two materials will results in a

greater reflection of the ultrasonic wave [15].

Table 1 presents the acoustic impedance for some different materials, in form of a Z-factor,

which have been calculated by dividing the acoustic impedance of base quartz material

(8.83) with the acoustic impedance of the material [16].

Table 1. Z-factor for some different materials [16].

Formula Material Name Z-factor

Al2O3 Aluminium oxide 0.336

MgO Magnesium oxide 0.411

MnO Manganese oxide 0.467

MnS Manganese (II) sulphide 0.940

The difference in acoustic impedance is larger between steel-air than steel-inclusion [17].

2.3.4 Focal plane

The ultrasonic beam will reach its maximum in intensity at a certain distance from the probe,

and the probe manufacturer will mention this as the probe’s focus. By changing the water

gap, the distance between the probe and the sample, the desired focal point can be reached.

The water gap can be calculated by using Equation 2:

𝑊𝑎𝑡𝑒𝑟 𝑔𝑎𝑝 = 𝐹 −𝑣𝑠𝑡𝑒𝑒𝑙

𝑣𝑤𝑎𝑡𝑒𝑟∙ 𝐹𝑝 (2)

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Where F [mm] is the focus for the probe, which is a fixed value from the manufacturer, 𝑣𝑠𝑡𝑒𝑒𝑙

[mm/s] is the speed of sound in the steel sample, 𝑣𝑤𝑎𝑡𝑒𝑟 [mm/s] is the speed of sound in

water and 𝐹𝑝 [mm] is the desired focal point.

2.3.5 Amplification and defect sizing

The ultrasonic wave travels faster and easier in steel than in water. If the water gap

increases, this means that the amplification [dB] needs to be increased as well, this means

that the amplitude will be increased. The SAM software usually presents the amplitude as an

echo amplitude index [%], and it is desired to not obtain amplitudes over the maximum

100%, since it is not possible to tell if it is 101% or 110%. But increased amplification

increases the sensitivity to detect defects, so it is desired to obtain as high amplitude as

possible, without reaching the maximum.

One way to set the amplification is by using the reflection echo from a flat bottom hole (FBH)

or a side drilled hole (SDH). An FBH or SDH is a reference reflector that is commonly used for

calibration and to evaluate detectability. The FBH is drilled with a specific diameter, at a

certain depth from the bottom of a reference block. The SDH is drilled with a specific

diameter from the side of a reference block, also at different depths, see Figure 10.

Figure 10. Illustration that shows the difference between flat bottom holes and side drilled holes. The image shows transducers with angled beams on top of the samples [18].

It is commonly said that a -6dB drop corresponds to half of the original amplitude, since the

rule of thumb in acoustics says that -6dB corresponds to a decrease of the sound pressure

level by one half. This can be used for sizing defects by swiping the probe over a defect. A

signal will appear when the ultrasonic beam encounters the defect, and the maximum

amplitude is achieved when the beam is completely over the defect. The -6dB drop occurs

when half of the beam is over the defect. By swiping the probe over a defect in a straight

line there will be two -6dB positions and the distance between these positions gives the size

of the defect, see Figure 11 for example.

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Another correlation is that if the diameter of an FBH or SDH would be doubled, the gain

difference would be 12dB for FBH and 3dB for SDH. If the sound path would be doubled, the

gain difference would be 12dB for FBH and 9dB for SDH [19].

Figure 11. Example of a defect size measurement by using the -6dB drop method [20].

Distance Amplitude Correction (DAC) curves or Distance-Gain-Size (DGS) method can be

used to predict the size of the defect based on the echo amplitude. These are used since the

echo decreases with increased depth of the defect. SDH are used for DAC and FBH are used

for DGS. By using a reference block with SDH at different depths, a DAC curve can be

recorded, by maximizing the echo for each hole. A curve can be drawn between the peaks,

see Figure 12. A DSG diagram shows gain differences for different sizes of FBH at different

depths [19].

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Figure 12. An example of a DAC-curve, where the blue rings are marked out echo-peaks for each hole, and the green curve is drawn based on these peaks [19].

It should also be noted that a defect will be detected when the edge of the ultrasonic beam

hits a defect, and that coordinate will be stored. The indication will continue until the whole

ultrasonic beam have passed completely over the defect. This means that the diameter size

of the ultrasonic beam will add to the detected defect size [21].

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3 Method

To evaluate which type of inclusions that is possible to detect with 25 MHz UST, and which

type of inclusions that is difficult to detect, a method was developed involving 25 MHz UST

(performed with SAM), RBF, SEM and LOM. Steel samples were scanned with SAM and then

the same samples were fatigue tested with RBF. The fracture surfaces were analysed with

SEM and LOM. The microscopical investigation of the inclusions that caused the fractures

were then compared with the results from SAM.

3.1 Sample Preparation

The samples were obtained from the bottom of ingots, which is considered to have the

highest concentration of inclusions. The ingots had been rolled down to 147x147 mm square

billets, and the samples were obtained from the centre line of the billets, see Figure 13. Two

different bearing steel grades were used for this study and will be referred to as steel grade

1 and steel grade 2, and samples from 5 different heats were investigated. 3 samples from

each heat were obtained from the bottom of three 147x147 mm squared billets.

Before the samples were obtained, they were scanned with 10 MHz UST and the results

were gathered for this study. The thickness of the samples was reduced by machining and

then scanned with 25 MHz UST. Afterwards, 5 smaller samples with square cross-section

were cut out from the larger piece, see Figure 13. For dimensions of the samples, see Table

2. To get an increased chance of obtaining fracture from an inclusion during RBF testing, the

samples are obtained from the most critical direction, which is transverse to the rolling

direction.

13

Figure 13. The left illustration shows how the sample was obtained from the 147 mm square billet. The right illustration shows this sample and how five samples were cut out for RBF testing. The image

is not according to scale.

Table 2. Dimensions for the different samples used.

Width [mm] Length [mm] Thickness [mm]

10 MHz 147 200 60

25 MHz 147 130 20

25 MHz RBF samples 147 20 20

Table 3 presents the naming of all the samples used for this study.

14

Table 3. The heats used for this study and the naming of the samples.

Heat Steel Grade 147x130 mm

Samples 147x20 mm Samples

B 1 B1 B11-B15

B2 B21-B25

B3 B31-B35

C 1 C1 C11-C15

C2 C21-C25

C3 C31-C35

D 2 D1 D11-D15

D2 D21-D25

D3 D31-D35

E 2 E1 E11-E15

E2 E21-E25

E3 E31-E35

F 1 F1 F11-F15

F2 F21-F25

F3 F31-F35

15

The 147x20 mm samples were scanned with 25 MHz UST, and then hard turned to RBF

samples with circular cross-section. The samples were heat treated and then hard turned

once again to the final dimension. In Figure 14, an RBF sample before and after hard turning

can be seen. To be able to perform UST with the equipment available, the surfaces of the

samples needed to be flat.

Figure 14. Photos showing RBF samples before and after hard turning.

3.2 10 MHz Ultrasonic Testing

The equipment used for 10 MHz UST was GE Lab750 Ultrasonic Scanning Tank – GE USIP40

Instrument. The transducer used was a GE 10 MHz Alpha Probe Point Focused 6’’ (152 mm)

Diameter 0.75’’ (19 mm). Table 4 presents the data that was used for 10 MHz UST.

Table 4. Presenting data used for 10 MHz UST.

Speed of sound in water 1480 m/s

Speed of sound in steel grade 1 & 2 5980 m/s

Length of gate 30 mm

Focal point 148 mm

Water gap 26.8 mm

Target value for centre of gate 30 mm

Pulses per second 2000

Size of sound field 1.3 - 1.6 mm diameter

Size of 1 pixel 0.2 x 0.2 mm

Everything above 25% amplitude and an area more than 4 adjacent pixels were reported as a

defect.

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3.3 25 MHz Ultrasonic Testing

The equipment used for 25 MHz UST was the scanning acoustic microscope, SAM301 PVA

TePla with the software PVA TePla Winsam 8. The transducer was a 25 MHz Alpha probe GE,

with probe diameter 0.25’’ (6.35 mm). Configuration was set to echo. The probe and steel

sample were immersed in water. The samples and probe were brushed to avoid bubbles,

which is a common source of error that will give deviating results. Table 5 presents relevant

data used for 25 MHz UST. One scan with these settings of an 147x130 mm area takes

approximately 1 hour and 10 minutes, while one scan of an 147x20 mm area takes

approximately 10 minutes.

Table 5. Presenting data used for 25 MHz UST.

Speed of sound in water 1484 m/s

Speed of sound in steel grade 1 & 2 5970 m/s

Focus for 25 MHz probe 46.5 mm

Pulses per second 2000

Size of sound field 450 µm diameter

Size of 1 pixel 100 µm x 100 µm

Everything above 30% amplitude and an area of at least 4 adjacent pixels for the 147x130

mm samples were reported as defects. Everything above 25% amplitude and at least 2

adjacent pixels for the 147x20 mm samples, were reported as defects. For the larger

samples, a higher amplitude and number of pixels was chosen to decrease the time to load

the data in the software. The images from the 147x20 C-scans were analysed with ImageJ,

and all pixels under 25% were sorted into one colour (red). It is with 25% amplitude that 50

µm FBH can be detected.

Two different strategies were performed. For heat B & C, one 5.4 mm thick layer per sample

was scanned with Strategy 1 (S1), see Figure 15.

17

Figure 15. Illustration of the probe, ultrasonic beam and the gate for Strategy 1. The large square illustrates the short end of the scanned 147x20 mm sample, and the circle illustrates the smallest

diameter (10 mm) of an RBF sample. The image is not according to scale.

For Strategy 2 (S2), three 2 mm thick layers were scanned, and then the sample was flipped

along the long side and three layers were scanned again, see Figure 16. Strategy 2 was used

for heat D, E & F.

Figure 16. Illustration of Strategy 2, where six different layers of the 147x20 mm sample are scanned. The image is not according to scale.

Table 6 presents the set up for the different strategies and layers.

18

Table 6. The set up for the different strategies and layers for the 25 MHz UST.

S1 S2 L1&L6 S2 L2&L5 S2 L3&L4

Length of gate [mm]

5.4 2 2 2

Length of gate [ns]

1809 670 670 670

Focal point [mm]

10 5 7 9

Water gap [mm]

6.27 26.39 18.34 10.29

Water gap [ns] 8450 35560 24716 13873

Path in the steel [mm]

20 10 14 18

Target value for centre of gate

[ns]

8450 1675 2345 3015

Start of gate [ns]

2446 1340 2010 2680

Amplification [dB]

47 49 47 45

Table 7 presents the scanned volume for each method.

Table 7. Scanned volume for each method.

Method Scanned volume [mm2]

10 MHz 882000

25 MHz S1 103194

25 MHz S2 229320

The samples for Strategy 2 were marked with a horizontal line on the edge, so that it would

be possible to track on the RBF sample from which direction the first layer was scanned, see

19

Figure 17. This was necessary to be able to correlate detected defects from 25 MHz UST to

real inclusions.

Figure 17. Photo of the end of one RBF sample, showing the marking that makes it possible to know which direction the sample was scanned.

3.4 Rotating Bending Fatigue Testing

The equipment used for RBF testing was Roell + Korthaus AMSLER UBM 200. The samples

were placed in the machines, and a load of 625 MPa was applied. If there was no fracture

after 106 cycles, the load would be increased with 25 MPa at the time.

3.5 Scanning Electron Microscopy

The fractured RBF samples were analysed with an analytical SEM with a field emission

electron source, Zeiss FE-SEM Sigma 300 with Gemini Column. For the chemical analysis, the

energy-dispersive X-ray spectroscopy (EDS) detector Oxford Instruments Ultim Max Silicon

Drift Detector was used, with the EDS software Oxford Instruments Aztek analysis suite.

Length and width of the inclusions, distance between the inclusion and the surface, angle of

inclusion’s orientation towards surface and chemical composition were gathered during the

analysis. Images were obtained with 125x, 250x and 500x magnification with angle-sensitive

backscatter detector. The aperture size was 30 µm, the electron high tension was 20kV and

working distance approximately 8.5 mm. The inclusions were classified into:

▪ Ca-sulphides

▪ Mn-Mg-sulphides

▪ Oxy sulphides

▪ Oxy sulphide stringers

▪ Globular oxides

▪ Complex oxides

20

The area of the inclusions was measured with the software ImageJ on the images obtained

with SEM.

3.6 Light Optical Microscopy

The equipment used for LOM analysis was a Leica Macroscope fitted with a USB-camera. The

fractured RBF samples were investigated in the macroscope. For Strategy 2, the samples

were placed so that the first layer would be at the top, see Figure 18. The angle from top to

fisheye was measured with the software DinoCapture 2.0. Why this angle was obtained is

explained further in upcoming subchapter.

Figure 18. An image from LOM analysis on the fracture surface of an RBF sample. By using the software, the angle between the top to the fisheye could be measured and which layer the inclusion is

situated can be derived.

3.7 Layer Analysis

It was of importance to know at which depth of the RBF sample that the inclusion that

caused the fracture were, so that the scan from the 25 MHz UST could be matched with the

inclusion. Several assumptions were made for the layer analysis:

▪ The inclusions were elongated in the rolling direction

▪ The RBF samples were obtained transversal to the rolling direction with high accuracy

▪ The RBF samples with circular cross-section were obtained from the centre of the

147x20 mm samples with high accuracy

The angle between the RBF sample surface and the inclusion’s elongation was measured on

the SEM images and this was used to determine whether the inclusion was scanned within

the 5.4 mm gate in Strategy 1. Figure 19 shows how an inclusion perpendicular to the

surface of the circular cross-section was expected to be inside the gate, while an inclusion

parallel to the surface was expected to be outside the gate.

21

Figure 19. The illustration to the left shows how inclusions are elongated in the rolling direction, and to the right shows how the orientation of the inclusion towards the RBF sample surface can be used to

locate the inclusion in the 25 MHz UST scan. Note that the RBF samples surface is visible in the SEM images.

By using Equation 3:

sin 𝛼 =ℎ

𝑟(3)

where h is half of the gate’s height and r is the radius of the RBF sample, a critical angle

could be calculated. All inclusions with an angle <35° were expected to have been scanned

with Strategy 1, see Figure 20.

22

Figure 20. Illustrations showing the gate for the 25 MHz UST and the critical angle between the inclusion and the RBF sample surface.

This method was applied for Strategy 2 as well, where Table 8 shows correlation between

the layers and the angles. In addition, the angle between the top of the circular cross-section

to the fisheye was measured in macroscope, and the angles for each layer was also

calculated.

Table 8. Correlation between layers and measured angles.

Angle SEM Angle LOM

Layer 1 53° - 90° 0° - 37°

Layer 2 24° - 53° 37° - 66°

Layer 3 0° - 24° 66° - 90°

Layer 4 0° - 24° 90° - 114°

Layer 5 24° - 53° 114° - 143°

Layer 6 53° - 90° 143° - 180°

3.8 Region of Interest in 25 MHz Ultrasonic Testing

With the C-scan obtained from the 25 MHz UST it was possible to match the x- and y-

coordinate with the real inclusion, see Figure 21. By using the coordinates, a region of

interest (ROI) could be marked in the Winsam 8 software.

23

Figure 21. A C-scan from the 25 MHz UST with a marked region of interest in WINSAM 8 software.

The fractured sample was measured to find the x-coordinate, see Figure 22.

Figure 22. Manual measurement of the fractured RBF sample.

By using the image from LOM analysis, the distance from centre could be calculated and the

y-coordinate could be obtained, see Figure 23.

24

Figure 23. Image from LOM analysis showing how the y-coordinate could be obtained.

The following sources of error were considered when choosing the size of ROI:

• x-coordinate

- Human error during manual measurement 0.25 mm

- Difference in length between scan and sample 0.75 mm

= 1 mm

• y-coordinate

- Sample was not placed straight in the 25 MHz UST equipment 0.2 mm

- Sample was not completely straight during hard turning 1 mm

= 1.2 mm

ROI was therefore set to 2.4 mm in height (y-axis) and 2 mm in width (x-axis) for Strategy 2.

For Strategy 1, where it was not possible to derive at which y-coordinate the inclusion was

situated, the height was increased to 11 mm.

25

4 Results

4.1 10 MHz vs 25 MHz Ultrasonic Testing

Table 9 presents the number of defects detected with 10 MHz UST and 25 MHz UST.

Everything above 25% amplitude and more than 4 adjacent pixels, which is an area above 0.2

mm2, were reported as a defect for 10 MHz UST. For 25 MHz UST everything above 30%

amplitude and at least 4 adjacent pixels, which is an area of 0.04 mm2, were reported as a

defect.

Table 9. Number of defects reported for 10 MHz and 25 MHz UST.

Number of defects Number of defects / 106 mm3*

Sample 10 MHz 25 MHz 10 MHz 25 MHz

B1 0 25 0 242

B2 0 32 0 310

B3 0 33 0 320

C1 1 37 2 359

C2 0 18 0 174

C3 0 13 0 126

D1 10 293 17 2839

D2 5 246 9 2384

D3 4 248 7 2403

E1 2 117 3 1134

E2 1 159 2 1541

E3 2 75 3 727

F1 0 37 0 161

F2 0 76 0 331

F3 0 47 0 205

*Scanned area approximated to 147x130 mm.

Gate 10 MHz: 30 mm.

Gate 25 MHz: Heat B-E: 5.4 mm. Heat F: 6 x 2 mm.

26

4.2 Scanning Electron Microscopy & Energy-Dispersive X-ray Spectroscopy

4.2.1 Classification of inclusions

The classification of inclusions is based on chemical and visual analysis. The inclusions were

classified into six different groups:

▪ Ca-sulphides

▪ Mn-Mg-sulphides

▪ Oxy sulphides

▪ Oxy sulphide stringers

▪ Globular oxides

▪ Complex oxides

See Figure 24 - Figure 29 for typical inclusions for each class, and a typical EDS analysis for

each type of inclusion.

Figure 24. Typical Ca-sulphide, sample E31.

Figure 25. Typical Mn-Mg-sulphide, sample C31.

27

Figure 26. Typical oxy sulphide, sample B32.

Figure 27. Typical oxy sulphide stringer, sample D11.

Figure 28. Typical globular oxide, sample E15.

28

Figure 29. Typical complex oxy sulphide stringer, sample E23.

4.3 25 MHz Ultrasonic Testing Results

6 samples from heat B fractured around an inclusion that had an angle of 35° or less towards

the RBF sample surface, and hence these inclusions were expected to have been scanned

with Strategy 1. See Table 10 for presentation of the inclusions that were scanned with 25

MHz UST, and if there was a defect reported from 25 MHz UST inside the marked ROI.

Table 10. Presentation of each inclusion that were scanned with 25 MHz UST, the area of the inclusion measured with ImageJ, length and width of the inclusion measured in SEM and if there was a defect reported inside the ROI in the C-scan from the 25 MHz UST.

Sample Type of inclusion Area [µm2]

Length [µm]

Width [µm] Defect inside ROI?

B12 Mn-Mg-sulphide 2088 143 18 Yes

B13 Mn-Mg-sulphide 3233 197 47 No


B22 Oxy sulphide 3221 223 22 No


B32 Oxy sulphide 9078 580 27 Yes

6 samples from heat C fractured around an inclusion that had an angle of 35° or less towards

the RBF sample surface, and hence these inclusions were expected to have been scanned

with Strategy 1. See Table 11 for presentation of the inclusions that were scanned with 25

MHz UST, and if there was a defect reported from 25 MHz UST inside the marked ROI.

29


Sample Type of inclusion Area [µm2] Length [µm]

Width [µm] Defect inside ROI?

C14 Mn-Mg-sulphide 3757 283 39 No

C15 Mn-Mg-sulphide 1713 113 17 Yes



C32 Mn-Mg-sulphide 3570 170 62 Yes


Heat D, E and F were scanned with Strategy 2 and therefore all the inclusions that caused

fracture were scanned with 25 MHz UST. See Table 12, Table 13 and Table 14 for

presentation of the inclusions that caused fracture and if the inclusion were detected with

25 MHz UST. Sample D15 broke during sample preparation and is therefore not included.

30


Sample Type of Inclusion Area [µm2] Length [µm]

Width [µm]

Defect inside ROI?

D11 Oxy sulphide stringer 11699 668 27 No


D13 Globular oxide 10935 337 78 Yes*

D14 Globular oxide 2509 200 32 Yes*




D24 Oxy sulphide stringer 13036 505 42 Yes







*D13 - The inclusion was expected to be found in layer 5, but reported defect was found in

layer 4

*D14 – The inclusion was expected to be found in layer 3 or 4 – but reported defect was

found in layer 5

31


Sample Type of inclusion Area [µm2]

Length [µm]

Width [µm]

Defect inside ROI?

E11 Oxy sulphide stringer /Broken globular oxide

3170 256 35 No

E12 Globular oxide 3354 211 42 No

E13 Ca-sulphide 3458 231 23 No


E15 Globular oxide 10201 251 72 Yes

E21 Oxy sulphide stringer 4105 307 17 Yes

E22 Oxy sulphide stringer 1756 166 15 No

E23 Complex oxy sulphide stringer 7730 300 35 No

E24 Globular oxide 8275 181 92 Yes



E32 Complex oxy sulphide stringer 3715 231 39 No


E34 Ca-sulphide 2565 313 15 Yes

E35 Oxy sulphide stringer 950 86 16 No

32


Sample Type of inclusion Area [µm2] Length [µm]

Width [µm]

Defect inside ROI?

F11 Mn-Mg-sulphide 2489 180 22 No















4.4 Detected Inclusions

Figure 30 shows the difference of the noise-level between the layers when analysing the C-

scan images in ImageJ, where everything below 25% amplitude is set to one colour (red).

Layer 1 shows more blue pixels evenly spread over the image, compared to layer 2 and 3.

33

Figure 30. Amplitude analysis in ImageJ of the C-scan images, showing difference of noise-level depending on the depth of the gate, where a lot of blue pixels can be seen in layer 1 compared to

layer 2 and 3.

Table 15 presents the number of pixels and maximum amplitude for the defects that were

detected inside the marked ROI, as well as the real inclusion’s equivalent circle diameter

34

(ECD). The ECD is based on the area that was measured with ImageJ, and calculated with

Equation 4:

𝐸𝐶𝐷 = √4 ∙ 𝑎𝑟𝑒𝑎

𝜋(4)

Table 15. Presenting the number of maximum adjacent pixels and amplitude in the ROI that were reported as defects with 25 MHz UST, as well as the real inclusion’s equivalent circle diameter.

Sample Number of Maximum Adjacent Pixels

Max Amplitude [%] ECD [µm]

B12 18 40 52

B15 9 32 40

B23 2 27 45

B32 47 93 108

C15 3 26 47

C32 2 26 67

D13 35 62 118

D14 3 34 57

D24 16 46 129

D32 7 38 133

D33 4 36 89

D35 2 31 132

E15 24 73 114

E21 6 36 72

E24 22 63 103

E34 3 28 57

35

Figure 31 - Figure 39 shows the images from amplitude analysis with ImageJ, together with

SEM images of the inclusion that caused the fracture. The images from the C-scan have been

imported in ImageJ, and the detected defect have been zoomed in. Everything under 25% is

set to one colour (red).

Figure 31. Sample B12. Left: Amplitude analysis from ImageJ. Right: SEM image of Mn-Mg-sulphide with 125x magnification. Area 2088 µm2, length 129 µm and width 16 µm.


36


Figure 34. Sample B32. Left: Amplitude analysis from ImageJ. Right: SEM image of oxy sulphide with 125x magnification. Area 9078 µm2, length 442 µm and width 21 µm.

37

Figure 35. Sample C15. Left: Amplitude analysis from ImageJ. Right: SEM image of Mn-Mg-sulphide with 125x magnification. Area 1713 µm2, length 106 µm and width 16 µm.

Figure 36. Sample C32. Left: Amplitude analysis from ImageJ. Right: SEM image of Mn-Mg-sulphide with 124x magnification. Area 3570 µm2, length 98 µm and width 36 µm

38

Figure 37. Sample D13. Left: Amplitude analysis from ImageJ. Right: SEM image of globular oxide with 125x magnification. Area 10935 µm2, length 217 µm and width 50 µm.

Figure 38. Sample D14. Left: Amplitude analysis from ImageJ. Right: SEM image of globular oxide with 125x magnification. Area 2509 µm2, length 125 µm and width 20 µm.

39

Figure 39. Sample D24. Left: Amplitude analysis from ImageJ. Right: SEM image of oxy sulphide stringer with 125x magnification. Area 13036 µm2, length 396 µm and width 33 µm.


40



41

Figure 43. Sample E15. Left: Amplitude analysis from ImageJ. Right: SEM image of globular oxide with 125x magnification. Area 10201 µm2, length 188 µm and width 54 µm.

Figure 44. Sample E21. Left: Amplitude analysis from ImageJ. Right: SEM image of oxy sulphide stringer with 250x magnification. Area 4105 µm2, length 272 µm and width 15 µm. Note that there is

a different magnification compared to other SEM images.

42

Figure 45. Sample E24. Left: Amplitude analysis from ImageJ. Right: SEM image of globular oxide with 125x magnification. Area 8275 µm2, length 128 µm and width 65 µm.

Figure 46. Sample E34. Left: Amplitude analysis from ImageJ. Right: SEM image of Ca-sulphide with 130x magnification. Area 2565 µm2, length 232 µm and width 11 µm.

Figure 47 - Figure 49 shows three different plots of how the amplitude varies with area,

length, and width of the detected inclusions.

43

Figure 47. Plot of the area of the detected inclusion on the x-axis and the amplitude of the detected defect with 25 MHz UST on the y-axis.

Figure 48. Plot of the length of the detected inclusion on the x-axis and the amplitude of the detected defect with 25 MHz UST on the y-axis.

0

20

40

60

80

100

0 2000 4000 6000 8000 10000 12000 14000 16000

Am

plit

ud

e [%

]

Area of inclusion [µm2]

0

20

40

60

80

100

0 100 200 300 400 500 600 700

Am

plit

ud

e [%

]

Length of inclusion [µm]

44

Figure 49. Plot of the width of the detected inclusion on the x-axis and the amplitude of the detected defect with 25 MHz UST on the y-axis.

4.5 Comparison of Inclusions That Were Detected and Those Who Were Not

Figure 50 presents the ECD of the inclusions sorted by those that were detected and those

that were not detected with 25 MHz UST.

0

20

40

60

80

100

0 20 40 60 80 100

Am

plit

ud

e [%

]

Width [µm]

45

Figure 50. Box and whisker-plot showing the difference between the inclusions’ equivalent circle diameter between the ones detected and the ones not detected.

Figure 51 presents the area of the inclusions measured with ImageJ on the y-axis and the

length/width-ratio measured with SEM on the x-axis. The data is sorted on the inclusions

that were detected and those that were not detected with 25 MHz UST. Figure 52 shows a

similar plot but with the width of the inclusion on the x-axis.

46

Figure 51. The area of the inclusions on the y-axis and the length/width ratio on the x-axis, sorted on the inclusions that were detected and those that were not detected.

Figure 52. Area of the inclusion on the y-axis and width of the inclusion on the x-axis. The data is divided into the ones detected and the ones not detected.

0

2000

4000

6000

8000

10000

12000

14000

16000

0 5 10 15 20 25 30

Are

a o

f in

clu

sio

n [

µm

2]

Length/Width

Detected

Not detected

0

2000

4000

6000

8000

10000

12000

14000

16000

0 20 40 60 80 100

Are

a o

f in

clu

sio

n [

µm

2]

Width of inclusion [µm]

Detected

Not detected

47

Figure 53 presents a comparison on how many inclusions that were detected and how many

that were not detected, sorted by type of inclusion.

Figure 53. Comparison of how many inclusions that were detected and how many were not detected, sorted on type of inclusion.

0 5 10 15 20 25 30

Calcium sulphides

Mn-Mg-sulphides

Oxy sulphides

Oxy sulphide stringers

Globular oxides

Complex oxy sulphide stringers

Detected Not detected

48

Figure 54 - Figure 56 shows plots of the area and length/width ratio between detected and

not detected inclusions, divided into different types: Mn-Mg-sulphides, globular oxides and

oxy sulphide stringers.

Figure 54. Area of the Mn-Mg-sulphides on the y-axis and the length/width ratio of the same inclusions on the x-axis. The data is divided into the ones detected and the ones not detected.

0

1000

2000

3000

4000

0 5 10 15 20 25

Are

a o

f in

clu

sio

n [

µm

2]

Length/Width

Not Detected Mn-Mg-Sulphides

Detected Mn-Mg-Sulphides

49

Figure 55. The area of the globular oxides on the y-axis and their length/width-ratio on the x-axis. The data is divided into the ones detected and the ones not detected.

Figure 56. The area of the oxy sulphide stringers on the y-axis and their length/width-ratio on the x-axis. The data is divided into the ones detected and the ones not detected.

0

2000

4000

6000

8000

10000

12000

0 2 4 6 8 10

Are

a o

f in

clu

sio

n [

µm

2]

Length/Width

Not detected globular oxides

Detected globular oxides

0

2000

4000

6000

8000

10000

12000

14000

16000

0 5 10 15 20 25 30

Are

a o

f in

clu

sio

n [

µm

2]

Length/Width

Not detected oxy sulphide stringers

Detected oxy sulphide stringers

50

For comparison between some types of inclusions that were detected compared to the

inclusions of the same type that were not detected, Figure 57 - Figure 59 shows the SEM-

images of all the oxy sulphide stringers that were not detected with 25 MHz UST. Figure 60

shows three SEM-images of all the globular oxides that were not detected with 25 MHz UST.

Figure 57. Oxy sulphide stringers that were not detected with 25 MHz UST.

51



52

Figure 60. Globular oxides that were not detected with 25 MHz UST.

53

5 Discussion

5.1 10 MHz vs 25 MHz Ultrasonic Testing

25 MHz UST detected more defects than 10 MHz UST, which was expected. This shows that

it is important to increase the frequency from 10 MHz UST to get more accurate results

when quantifying inclusions in steel. The smallest difference was 126 more defects detected

per 106 mm3 with 25 MHz compared to 10 MHz, and the largest difference was 2822 more

defects detected per 106 mm3. If considering all the samples, then 25 MHz UST detected on

average 881 defects per 106 mm3 more than 10 MHz UST.

Ovako is currently evaluating the use of even higher frequencies as well, 50, 80 and 125 MHz

UST and higher frequencies will have a higher sensitivity to smaller defects. But it should be

kept in mind that the focus zone decreases with higher frequency, which increases the risk

of missing large inclusions, since the chance of having the whole inclusion in the focus zone

decreases. One way to decrease this risk is to scan in several layers, which will however be

more time consuming. The different frequencies can serve different purposes, and several

different frequencies can be used to complement each other.

5.2 Comparison of Strategy 1 and Strategy 2

Strategy 2 increases the accuracy when trying to correlate the real inclusion to the detection

in the 25 MHz UST. S2 had the disadvantage though of being more time-consuming,

especially the scanning time of the 147x20 mm samples, which took 60 minutes, instead of

only 10 minutes as in S1. The different layer scan could not be started automatically, instead

they had to be started manually every 10th minute.

5.3 Detection of Inclusions

Earlier investigations have shown that 25 MHz UST can detect 50 µm FBH, but the FBH

cannot be directly translated to inclusions, since inclusions with an ECD larger than 50 µm

were not detected, as seen in Figure 50. This plot shows though that detected inclusions are

generally larger than the inclusions not detected. When looking at the plots in Figure 51 and

Figure 52, it can be seen that the inclusions’ area, width and length/width ratio is not

determining whether or not the inclusion can be detected with 25 MHz UST. In Figure 53,

where different inclusion types were compared, it was shown that most of the globular

oxides were detected, and that was the only type of inclusions that had more inclusions

detected than not detected.

When analysing the C-scans for defects, there is a risk that the noise from the image will be

reported as a defect, and there is clearly a difference when scanning layers at different

depths, as seen in Figure 30. Looking at Figure 31 - Figure 46, it can be seen that some of the

defects reported are much clearer than others. For some of the samples it can be considered

54

that there is a risk that the noise has been reported as a defect. These samples are the Mn-

Mg-sulphides B15, B23, C15, C32, the oxy sulphide stringers D33, D35, E21, the Ca-sulphide

E34 and the globular oxide D14. These samples also reported rather small defects in terms of

number of pixels, see Table 15. The number of pixels were not more than 9 adjacent pixels,

and the amplitude was not higher than 36%, compared to the sample B32 with the

maximum number of pixels 47 and amplitude 93%.

To avoid the risk of reporting noise as defects, the amplitude threshold can be increased. But

it is with 25% amplitude threshold that 50 µm FBH is detected, and by increasing the

threshold, the minimum size of detected defect will be decreased.

16 out of 74 inclusions (22%) were detected with the method used for this study. If regarding

previously discussed risk of reporting noise as a defect, then only 7 inclusions (9%) gave a

clear indication.

5.3.1 Manganese-magnesium-sulphides

Most of the Mn-Mg-sulphides were not detected with 25 MHz UST, 20 out of 25 samples.

The marked ROI for heat B and C is 11 mm in height, this increases the risk of correlating an

inclusion to a reported defect that is not the one that caused the fracture. There is a risk that

the large defect reported for sample B12 is not the Mn-Mg-sulphide with the area of 2088

µm2 that caused the fracture. It is much possible that there was another inclusion present in

the sample, but it was located somewhere where the local stress was low. In addition, it is

possible that the samples B15, B23, C15, C32 had a risk of reporting noise as a defect, as

discussed previously. If this is true, then no Mn-Mg-sulphide were detected with 25 MHz

UST. Sample B15, B23 and C15 also had an ECD below 50 µm, see Table 15, and are

therefore not expected to be detected with 25 MHz UST.

One argument to why Mn-Mg-sulphides are difficult to detect with 25 MHz UST, is that it

could be because of there is a small acoustic impedance difference, which makes no clear

material boundary for the ultrasonic wave to reflect on, which is discussed further in

subchapter 5.5. Another argument could be that the Mn-Mg-sulphides in this study have

small areas, below 4000 µm2, which corresponds to an ECD smaller than 72 µm. Additionally,

it can be discussed if it could be because they are elongated, which will be further explained

in subchapter 5.4.

5.3.2 Oxy sulphide stringers

If considering the risk of noise being reported as a defect, and that some of the C-scans had

rather small defects reported in terms of number of pixels, this includes the oxy sulphide

stringers D33, D35 and E21, leaving only two oxy sulphide stringers that gave a clear

indication: D24 and D32. In Figure 39 it can be seen that the inclusion in sample D24 had

more oxides (the darker areas) that were not encapsulated by sulphides, but rather in direct

contact with the steel matrix. This might create a greater difference in acoustic impedance

55

for the ultrasonic waves to reflect upon. Sample D32 had a relatively large sized oxy sulphide

stringer, with an area of 13815 µm2 and as can be seen in Figure 40 the inclusion shows

some darker areas that could be assumed to be oxides. The indication was still not that clear,

with only 7 adjacent pixels and 38% amplitude.

That morphology and chemical composition affects if an inclusion can be detected with 25

MHz UST can be further observed by looking at the oxy sulphide stringers in sample D11,

D22, and D25, see Figure 57 and Figure 58, which have large areas and were not detected

with 25 MHz UST, and it can be seen that these have sulphides in direct contact with the

matrix. But it should also be noted that the oxy sulphide stringer in sample D23, area 6163

µm2, that can be seen in Figure 58, seem to have oxides in contact with the steel matrix, but

this was still not detected with 25 MHz UST. The oxy sulphide stringers that were detected

had larger areas than D23 though.

Figure 56 that shows the area and length/width ratio of the oxy sulphide stringers, shows

that neither of these factors seems to determine whether the stringers will be detected or

not.

5.3.3 Globular oxides

Three globular oxides, samples E12, E14, E25, were not detected with 25 MHz UST, and their

area was measured to be 3354 µm2, 2201 µm2 and 7254 µm2. The smallest globular oxide

that was detected, D14, had an area of 2509 µm2, but this result has previously been

discussed as a risk of noise being reported as defect. As seen in Figure 60, the globular

oxides in sample E12 and E25 have been heavily deformed by rolling. The globular oxide in

E14, Figure 60, can be compared to the globular oxides in sample D13, E15, and E24, see

Figure 37, Figure 43 and Figure 45, and the structure seem somewhat different, with the

detected oxides having more darker areas.

E15 and E24 had almost intact globular oxides and these gave a clear indication with 24 and

22 pixels, and amplitudes of 73% and 63%. The globular oxide D13 have been rolled out, but

is not that fragmented, and have left a large void. This inclusion gave a clear indication with

35 pixels and 62% amplitude. In addition, the globular oxides that gave a relatively clear

indication, E15, E24, and D13 had large areas of 10201, 8275 and 10935 µm2.

As seen in Figure 55, it could not be determined a specific limit for area or length/width ratio

for the globular oxides in order to be detected or not. But it can be seen that globular oxides

with low length/width ratio and large area were detected, showing the effect of deformation

and fragmentation of the oxides on the detectability.

5.3.4 Calcium sulphides

There were four Ca-sulphides present in this study, and one of them was reported as a

defect in 25 MHz UST. As previously discussed, there is a risk that sample E34 had noise

56

reported as a defect, and then it can be assumed that no Ca-sulphide gave a clear indication

with 25 MHz UST.

5.3.5 Complex oxides

No complex oxides were detected with 25 MHz UST, but only two inclusions of this type

were investigated in this study.

5.3.6 Oxy sulphides

Only one oxy sulphide, B32, was detected with 25 MHz UST, but only two inclusions of this

type were investigated in this study. The oxy sulphide detected can be seen in Figure 34 to

have oxides in direct contact with the matrix. It should be considered though, that sample

B32 was investigated with Strategy 1, which increases the risk of not correlating the

detection in 25 MHz UST with the real inclusion.

5.4 Morphology

The ultrasonic beam for this study was circular with a diameter of 450 µm. Figure 61

illustrates the ultrasonic beam passing over two different types of inclusion, one spherical

and one elongated. From this it can be seen that a higher percentage of the beam will be

reflected for the spherical inclusion than for the elongated, and that this would be the case

for inclusions that could have the same area but different length/width-ratio.

Figure 61. Illustration of the ultrasonic beam (light blue circle) passing over a spherical inclusion, to the left, and passing over an elongated inclusion to the right.

This can also be seen in the plots in Figure 47 - Figure 49, where there seems to be a

correlation between width of the inclusion and the amplitude, but there seems to be no

correlation between area or length and the amplitude. It can then be considered, that if it

would be possible to decrease the diameter of the ultrasonic beam, then it could be

expected that elongated inclusions would be easier to detect.

Figure 61 could also be compared with the globular oxides that gave the clearest indications,

E15, E24 and D13, see Figure 43, Figure 45 and Figure 37.

57

Another issue regarding the morphology would be the direction of the reflection. If the

inclusion is completely flat, then the sound waves will bounce straight back to the

transducer, while if it has a curved surface where the ultrasonic wave will hit, then there is a

risk that the reflected ultrasonic waves can go to other directions instead of straight back to

the transducer.

5.5 Acoustic Impedance

The acoustic impedance for the inclusions in this study is not known. Table 1 showed an

acoustic impedance factor of some oxides and MnS, where the sulphide differed a lot

compared to the oxides. Since the difference between the steel and the inclusions are not

known, it is not possible to draw any conclusions from this. If there is a good adhesion with

the inclusion and the steel matrix or if there is a cavity around the inclusion might play a part

in the acoustic impedance difference. It is also known that acoustic impedance difference is

larger between steel-air than between steel-inclusion, so it could be that the inclusions’

adhesion to the steel matrix plays a larger part, than the differences between different

inclusions’ acoustic impedance.

5.6 Sources of Error

Regarding Strategy 2, where several layers were scanned, there was a risk of reporting noise

as defects, especially in the layers closest to the surface, as can be seen in Figure 30.

No DAC- or DSG- curve was made for this thesis work, which means that the decrease in

amplitude due to defects being located at different depths in the samples was not taken into

consideration. Since this study did not focus on correlation between amplitude and size of

true defect, this was not considered necessary.

When trying to correlate the inclusion seen in microscopy to the results from the 25 MHz

UST, there were some sources of error to consider:

1) Human error of manual measurements of distance between end and fracture

surface. By measuring 10 times and looking at the variation, it varied the most 0.25

mm.

2) The size of the C-scan did not always correlate to the real size sample. The largest

misalignment discovered was 1.5 mm, which will give an error of 0.75 mm of each

end of the sample.

3) The SAM equipment did not have a holder for the steel samples that were placed in

the tank. This caused a difficulty in achieving repeatability, and the sample was

sometimes not placed straight. This would in turn cause errors in both x- and y-

direction, but mostly in y-direction. This error is expected to be maximum +/- 0.2 mm

in y-direction.

58

4) The pre-machined sample was not always hard turned completely straight. It was

estimated that there could be an error of maximum +/- 1 mm in y-direction.

5) The marking on the RBF samples to relocate the layers of the UST scan, had a risk of

human error when marking, since it was marked on “free hand”.

This means that there is a risk that the detected defect with the 25 MHz UST is not

corresponding to the real inclusion.

5.7 Social and Ethical Aspects

Further developing techniques for analysing cleanliness of steel is part of the work towards

cleaner and stronger steel. This in turn will lead to components with a longer lifetime, hence,

less components needs to be produced leading to reduced energy consumption. Cleaner

steel also provides lighter components, which is beneficial especially in automotive,

aerospace and train industry. Lighter components lead to higher fuel efficiency, and with

stronger steel, the loads can be increased and then less transportation is needed.

Improvement of steel’s fatigue properties leads to decreased risk of fatigue failure,

something that can cause a high economic impact and in worst case even fatal impact.

59

6 Conclusions

The aim of this study was to investigate what type of inclusions, in terms of size, chemical

composition and morphology, that could be detected with 25 MHz UST, and which type of

inclusions that is difficult to detect with 25 MHz UST. The conclusions that could be drawn

from this study are:

▪ It can be difficult to distinguish real defects from noise with 25% amplitude threshold

for the 25MHz UST set up used for this study

▪ Mn-Mg-sulphides are difficult to detect with 25 MHz UST

▪ The results indicate that oxy sulphide stringers and oxy sulphides can be detected

with 25 MHz UST if the inclusion have oxides in direct contact with the steel matrix,

rather than oxides encapsulated by sulphides

▪ Globular oxides can be detected with 25 MHz UST, at least down to an area of 8275

µm2. A globular oxide that is fragmented due to rolling will not give an as clear

indication as an intact oxide

60

7 Recommendations

To evaluate 25 MHz UST further, there are some suggestions for improvements when

performing this type of study:

▪ It is suggested to use another fatigue testing method that will test a larger volume of

the sample than RBF testing does. For example: pull-pull, gigacycle fatigue or push-

pull test

▪ If applying Strategy 2 as method, then it should be developed a way to start each

layer scan automatically to reduce work hours

▪ It is recommended to gain more experimental data with correlation between 25

MHz UST and real inclusions, to evaluate what can be indicated with 25 MHz UST,

since most of the data achieved in this study showed what could not be detected.

Only 16 out of 74 samples could be correlated to an inclusion, and several of these

had a risk of having noise reported as defect

▪ It would be better if the hard-turned steel sample for fatigue testing, with its circular

cross-section, was scanned with SAM equipment that can rotate the sample, which

exists but was not available for this study. There is a risk that indications that was

detected in the 25 MHz UST results may have disappeared during hard turning due

to material removal

▪ To get more exact coordinates, it is recommended to use a holder for the steel

samples when placing them in the SAM equipment

▪ If continuing developing this method, it is recommended to make a DAC/DSG-curve

and to use a higher limit on amplitude when reporting defect in layers that is close to

the surface

But most importantly it is recommended to evaluate the use of higher frequencies for UST,

to be able to detect all inclusions that can affect the fatigue properties of the steel.

61

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