martensitic and bainitic
transformations in carbon steel
Science and Technology, Delft University of Technology, Rotterdamseweg 137, 2628 AL Delft, The Netherlands.
The research described in this thesis was carried out in the framework of the Strate-gic Research Programme of the Netherlands Institute for Metals Research in the Netherlands (www.nimr.nl).
martensitic and bainitic
transformations in carbon steel
PROEFSCHRIFT
ter verkrijging van de graad van doctor aan de Technische Universiteit Delft,
op gezag van de Rector Magnificus prof.dr.ir. J.T. Fokkema, voorzitter van het College voor Promoties,
in het openbaar te verdedigen op dinsdag 24 februari 2004 om 13.00 uur
door
Stefanus Matheus Cornelis VAN BOHEMEN
doctorandus in de natuurkunde geboren te Wassenaar
Prof.dr. I.M. Richardson
Toegevoegd promotor: Dr.ir. J. Sietsma Samenstelling promotiecommissie: Rector Magnificus, voorzitter
Prof.dr. I.M. Richardson, Technische Universiteit Delft, promotor Dr.ir. J. Sietsma, Technische Universiteit Delft, toegevoegd promotor Prof.dr. G. den Ouden, Technische Universiteit Delft
Prof.dr.ir. M. Wevers, Katholieke Universiteit Leuven, Leuven, Belgi¨e Prof.dr. R. Boom, Technische Universiteit Delft
Dr. P.J. Jacques, Universit´e catholique de Louvain, Louvain-la-Neuve, Belgium Dr.ir. M.J.M. Hermans, Technische Universiteit Delft
Prof.dr.ir. S. van der Zwaag, Technische Universiteit Delft, reservelid Dr.ir. M.J.M. Hermans heeft als begeleider in belangrijke mate aan de totstandkoming van het proefschrift bijgedragen.
Published and distributed by: DUP Science DUP Science is an imprint of
Delft University Press P.O. Box 98
2600 MG Delft The Netherlands
Telephone: +31 15 2785678 E-mail: Info@Library.TUDelft.nl
Keywords: Phase transformations, acoustic emission, steel, martensite, bainite Copyright c° 2004 by S.M.C. van Bohemen
All rights reserved. No part of the material protected by this copyright notice may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording or by any information storage and retrieval sys-tem, without written permission from the publisher: Delft University Press. Printed in The Netherlands
1 Introduction 1
2 Acoustic emission and phase transformations 7
2.1 Historical review of the AE technique . . . 7
2.2 Basic theory of acoustic emission . . . 9
2.2.1 Material and transducer response . . . 10
2.2.2 Sensors and pre-amplifiers . . . 12
2.2.3 Attenuation and noise . . . 14
2.3 Phase transformations in steel . . . 15
2.3.1 Martensitic transformation . . . 15
2.3.2 Bainitic transformation mechanism . . . 18
3 Experimental 23 3.1 Acoustic emission system . . . 23
3.1.1 Sensor mounting and noise precautions . . . 27
3.1.2 Attenuation due to waveguides . . . 28
3.1.3 Source location . . . 31
3.2 Gas tungsten arc welding . . . 32
3.3 Thermo-mechanical simulator . . . 38
3.4 Dilatometer . . . 41
3.5 Furnace . . . 43
3.6 Materials . . . 43
4 Acoustic emission monitoring of phase transformations in steel 47 4.1 Study of steel C45 . . . 48
4.1.1 Thermo-mechanical simulator experiments . . . 48
4.1.2 Welding experiments . . . 56
4.2 Study of steel 42CrMo4 . . . 58
4.2.1 Welding experiments . . . 58
4.2.2 Dilatometer experiments . . . 60
4.2.3 Furnace experiments . . . 61
4.3 Study of low carbon steels . . . 64
4.4 Study of a high-alloyed steel . . . 65 i
4.5 Conclusions . . . 67
5 A study of acoustic emission energy generated during bainite and martensite formation 73 5.1 Theoretical background . . . 74
5.2 Martensite formation . . . 75
5.2.1 Travelling arc welding of steel 42CrMo4 . . . 76
5.2.2 Spot welding of steel 42MnV7 . . . 78
5.3 Bainite and martensite formation . . . 82
5.3.1 Spot welding of steel C45 . . . 83
5.3.2 Travelling arc welding of steel C45 . . . 84
5.4 Conclusions . . . 87
6 Kinetics of the martensitic transformation studied by means of acoustic emission 91 6.1 Introduction . . . 92
6.2 Theoretical background . . . 93
6.3 Experimental details . . . 94
6.4 Study of steels C50, C60, C70 and C80 . . . 96
6.4.1 Calculation of the martensite volume fraction . . . 96
6.4.2 Proportionality factors k and dislocation densities ρ . . . 97
6.4.3 Koistinen-Marburger kinetics . . . 99
6.4.4 A different analysis of the results for steel C80 . . . 105
6.4.5 Microstructural analysis . . . 106
6.4.6 Martensite-start temperature Ms . . . 107
6.4.7 Rate constant C1 . . . 109
6.5 Analysis of the results for steel 42CrMo4 . . . 112
6.6 Study of a shape memory alloy . . . 113
6.6.1 Acoustic emission experiments . . . 113
6.6.2 Optical Confocal Laser Scanning Microscopy observations . . 114
6.7 Conclusions . . . 116
7 Analysis of acoustic emission signals originating from bainite and martensite formation 121 7.1 Acoustic emission during plastic deformation . . . 122
7.2 Dislocation dynamics during displacive transformations . . . 125
7.2.1 Nucleation and growth of martensite . . . 125
7.2.2 Nucleation and growth of bainite . . . 127
7.3 Analysis of continuous acoustic emission . . . 127
7.4 Experimental details . . . 128
7.5 Results and discussion . . . 129
7.6 Discussion of proportionality factors k . . . 135
7.7 Conclusions . . . 137
Samenvatting 147
List of publications 153
Curriculum Vitae 155
Introduction
Acoustic emission (AE) is the name given to the phenomenon of elastic waves being generated by the rapid release of strain energy from localized sources within a material [1]. As an AE event occurs at a source, elastic waves are generated and propagate in all directions and ultimately reach the surface of the material.
Phenomena that are classified today as acoustic emission have been observed since the beginning of technology. For example during pottery making the early potters learned to associate the sound of pottery cracking as it cooled with the formation of cracks in their creations. Another familiar example of audible acous-tic emissions is the so-called ‘tin cry’, heard by tin smiths during the deformation of tin, which is due to mechanical twinning [2]. These observations date back to approximately 3000 BC. The first documented observation of acoustic emission dur-ing forgdur-ing of steel (iron) was made in the eighth century by an Arabian alchemist. These audible emissions were most likely produced by the formation of marten-site during cooling. Around the start of the twentieth century, the martensitic microstructure was observed for the first time by the German metallurgist Adolf Martens (1850-1914). In 1936 Forster and Scheil reported that the martensitic transformation in steel is accompanied by ”clicks” [3]. This may be considered as the first study of acoustic emission during martensite formation.
A unified (unambiguous) explanation of the source of acoustic emission does not yet exist. Usually the source is a process which involves a mechanism of deformation or fracture. Sources that have been identified in metals include dislocation motion (plastic deformation) [4], crack growth [5], oxidation [6], magnetic domain motion (the acoustic Barkhausen effect/emission) [7], twinning and displacive phase trans-formations [8]. In this thesis the acoustic emission during phase transtrans-formations in steel is discussed, mainly focussing on martensitic and bainitic transformations.
A martensitic transformation is a diffusionless first-order phase transition during which the lattice distortion is mainly described by a combination of shears [9, 10]. It involves a cooperative and almost simultaneous shear movement of atoms from parent to product phase, often indicated as a displacive process. The strain energy
produced during growth of the new lattice is reduced by plastic deformation [11]. In this process of martensitic transformation acoustic emission is generated. This causes transient surface displacements that can be detected with a transducer. The voltage signal from the transducer is then usually amplified in a pre-amplifier and analyzed with a computer to study the underlying processes in real-time.
In the development of new high strength steels the martensitic transformation, in combination with thermal and/or mechanical treatments, plays an important role. Due to the change in lattice structure and the fact that the transformation is displacive, several physical properties can be used to investigate the characteristics of the transformation, such as the transformation-start temperature and the kinet-ics of the transformation. The most common methods used to study the martensitic transformation in-situ are electrical resistivity, dilatometry and calorimetry. The acoustic emission technique used in this work is a rather new and promising tech-nique and has not often been used to study the martensitic transformation in real-time. Moreover, the acoustic emission technique is considered to be a good method to investigate the displacive character of a phase transformation [12]. Since there is still no general agreement about the mechanism of bainite formation [13, 14, 15, 16], acoustic emission measurements during bainite formation will give valuable infor-mation concerning its mechanism of growth.
Outline
The acoustic emission experiments described in this thesis have been performed mainly on medium carbon steels with the aim to study the martensitic and bainitic transformation in these materials under continuous cooling conditions. Continuous cooling of steel is achieved during/after welding, and in a thermo-mechanical simu-lator (welding simusimu-lator). One of the major merits of measurements under natural continuous cooling conditions is the absence of possible external noise from a heat-ing source, which is required for isothermal transformation conditions. Moreover, the transformation rate is usually faster during continuous cooling, which results in a better signal to noise ratio.
In chapter 2 the basic concepts of acoustic emission are presented, including an overview of the development and applications of the AE technique. The effect of the material and sensor response to the original waveform at the source is described followed by a discussion of sensitivity, attenuation and noise sources. Finally, the theory of the phase transformations from austenite to bainite and martensite is discussed.
The acoustic emission instrumentation, the signal processing technique and the experimental equipment is described in chapter 3. In most experiments, a welding apparatus or a thermo-mechanical simulator was used to apply a thermal cycle to the steel studied. The thermal treatment of a spot weld is usually very similar to the thermal treatment of a specimen used in the thermo-mechanical simulator. In order to investigate relatively large samples, some experiments were performed using a furnace and a salt bath. For a proper comparison between the AE technique and
dilatometry, simultaneous measurements of acoustic emission and dilatation were performed by connecting the AE system to a conventional dilatometer apparatus. The principles of these techniques are discussed and the procedure for measuring acoustic emission is given. This chapter concludes with an overview of the materials used in this study.
In chapter 4 the results of AE experiments on various carbon steels are pre-sented, which show that not only the martensitic transformation, but also the bainitic transformation is accompanied by acoustic emission. The implication of this observation for the transformation mechanism of bainite is discussed. For com-parison with the AE results during the martensitic and bainitic transformation, AE experiments were performed on a low carbon steel, which transforms from austenite to ferrite via a diffusion controlled mechanism. Furthermore, the AE signal mea-sured during martensite formation is compared with the change in dilatation of the sample during transformation.
In chapter 5 the relationship between the AE energy released during the marten-sitic transformation and the volume of martensite formed is studied by means of welding experiments. The AE energy due to the released strain energy accompa-nying martensitic transformations is theoretically and experimentally studied. It is shown that both for martensite and bainite formation a specific relation exists between the AE energy (rate) and the volume (rate) of the transformation.
Besides the fundamental interest in the nature of the martensitic and bainitic transformation in steels, the results of the study presented in chapter 5 may be used to develop an AE monitoring system to detect martensite and bainite formation during welding. Since these hard regions in the weld and heat-affected-zone (HAZ) are susceptible to cold cracking, real time monitoring of the welding process is of considerable practical importance.
The kinetics of the martensitic transformation in four carbon steels (C50, C60, C70 and C80) are discussed in chapter 6. By using the relation derived in chapter 5, the volume fraction of martensite f as a function of time t during cooling can be calculated from the measured AE signal. The results are compared with the kinetics predicted by the Koistinen and Marburger (KM) equation. At the end of chapter 6 the kinetics of the martensitic transformation in a CuAl-based shape memory alloy are studied by means of both acoustic emission and optical confocal laser scanning microscopy.
In chapter 7 the frequency spectra of acoustic waves generated during bainite and martensite formation are studied. The change in the mean frequency corres-ponding with the transition from bainite to martensite formation during cooling of a spot weld is attributed to differences in the interface motion of the two transfor-mations. This chapter concludes with an overview and discussion of the propor-tionality factors between the AE energy and the volume transformed, which were determined for a number of steels studied in this thesis.
[1] H.N.G. Wadley, C.B. Scruby and J. Speake, Int. Metals Rev. 3, 41 (1980). [2] R.B. Liptai, D.O. Harris and C.A. Tatro, Acoustic Emission (ASTM STP 505),
3 (1972).
[3] F. Forster and E. Scheil, Zeitschrift F¨ur Metallkunde 9, 245 (1936). [4] N. Kiesewetter, P. Schiller, Phys. Stat. Sol. A 38, 569 (1976).
[5] S.H. Carpenter and M.R. Gorman, J. Acoustic Emission 13, s1 (1995). [6] F. Ferrer, H. Idrissi, H. Mazille, P. Fleischmann and P. Labeeuw, NDT & E Int.
33, 363 (2000).
[7] C.C.H. Lo and C.B. Scruby, J. Appl. Phys. 85, 5193 (1999).
[8] G.R. Speich and A.J. Schwoeble, Acoustic Emission (ASTM STP 571), 40 (1975).
[9] Z. Nishiyama, Martensitic Transformation, Academic Press, London (1978). [10] D.A. Porter and K.E. Easterling, Phase transformations in metals and alloys,
Chapman & Hall, London (1992).
[11] R.W.K. Honeycombe and H.K.D.H. Bhadeshia, Steels, Edward Arnold, Lon-don (1995).
[12] P.C. Clapp, J. Phys. IV 5, 11 (1995).
[13] R.F. Hehemann, K.R. Kinsman and H.I. Aaronson, Metall. Trans. 3, 1077 (1972).
[14] H.I. Aaronson and H.J. Lee, Scripta Metallurgica 21, 1011 (1987).
[15] W.T. Reynolds, Jr., H.I. Aaronson and G. Spanos: Mat. Trans. JIM 32, 737 (1991).
[16] Y. Ohmori and T. Maki, Mat. Trans. JIM 32, 631 (1991).
Acoustic emission and phase
transformations
The acoustic emission measurements during phase transformations in steels dis-cussed in this thesis did not only result in a better understanding of the martensitic and bainitic transformation, but also of the AE technique itself. In this chapter some basic aspects of acoustic emission are explained, in as far as they are rele-vant for the subject of this thesis. First the background of the AE technique is briefly discussed in section 2.1. Subsequently, the effect of the material and sensor response to the original waveform at the source is described in section 2.2. Further-more, a discussion of sensitivity, attenuation and noise sources is given. The theory of the phase transformations from austenite to bainite and martensite is addressed in section 2.3.
2.1
Historical review of the AE technique
It is generally considered that acoustic emission as a technology started in the early 1950s with the work of Joseph Kaiser [1] who monitored the emissions of (audible) sound from materials subjected to external loads. He and his coworkers were the first to use electronic instrumentation to detect acoustic waves produced by metals during deformation. They reported that many metals such as zinc, steel, aluminium, lead and copper produce elastic waves under applied stress and that acoustic emission activity was irreversible: acoustic emissions were not generated during reloading until the previous stress level was exceeded. This phenomenon has become known as the Kaiser effect.
Investigators in the early 1960s realized extensive improvements in the instru-mentation of the acoustic emission technique. They found that many problems concerning background noise could be eliminated, or at least minimized, by work-ing with instrumentation whose frequency range was well above the audible range.
As a result, many engineers and scientists became interested in acoustic emission and utilized this technique in studies relating to materials research, structural eval-uation and non-destructive testing.
Most papers on acoustic emission were published in the 1970s and 1980s. In those years, an increasing effort was devoted to the understanding of the funda-mental aspects of acoustic emission, such as the nature of the source, and the way in which the elastic waves are propagated and detected. A long-term goal of these studies was to learn how to calculate a description of the source event from the voltage signal of the sensor. To solve the problem, scientists adopted analysis techniques from earthquake engineering in an attempt to model acoustic emission sources. Regarding the generation of waves, an earthquake is physically very sim-ilar to an acoustic emission event; it is actually only different in scale. Both the seismic and microseismic activity are initiated by a sudden release of strain energy at a ‘source’ and in both cases the vibrations propagate through the structure. Whereas the problem in the case of a semi-infinite material could be solved to some extent using this approach, in the case of a metal plate the waveform is quite complicated because of reflections, interference etc. Therefore, most applications of the AE technique were limited to a qualitative level over these years. Unsuccess-ful attempts in those years to utilize AE for investigation of the source properties and elementary mechanisms of martensitic transformations and plastic deformation discouraged researchers from these topics.
Although the application of AE for fundamental research declined in the late 1980s, the AE technology became more and more popular as a tool for non-destructive testing (NDT) in industry. In these areas use can be made of AE for in-situ detection of crack evolution and safety monitoring because of the intrin-sic nature of AE signals. Important reasons for its increasing acceptance and use were the improvements in microelectronics and in computer-based recording and analysis techniques to handle the high signal rates. Certain AE waveform param-eters, such as amplitude and duration became the standard quantities to describe the AE signals. The display of these parameters allowed a better analysis of results in comparison with the number of counts on X-Y recorders, which was used in the early days of AE technology.
The acoustic emission technique is considered quite unique among the non-destructive testing methods. In contrast to other NDT methods the detected energy is released from within the object rather than being supplied externally by the NDT method, as in e.g. ultrasonics, eddy current or radiography. This has the advantage that with the AE technique the entire structure can be monitored using only one sensor at a certain position. Moreover, with two or more sensors the location of the source can be determined. An inherent drawback is that the AE technique can only detect active sources like crack growth or plastic deformation; cracks or flaws which have formed previously do not radiate elastic waves. Another limitation of the AE technique is that it can be considerably influenced by ambient noise. Great care must be taken to distinguish AE signals from the ambient noise, especially during practical application of the technique in an industrial environment.
pre-amplifier signal
sensor
AE system
source AE waves
Figure 2.1: The basic principles of the acoustic emission technique. After Pollock [2].
Recent progress in electronics and computer technology has created new pos-sibilities to use the AE technology for laboratory studies through essential im-provements in AE measuring systems and analyzing tools. For instance, the full-waveform recording and processing of individual signals became possible with the aid of powerful computers. Nevertheless, the interpretation of detected signals should always be performed with considerable care because they are never simply related to the mechanism of the source. This and other fundamental aspects of acoustic emission are addressed in section 2.2.
2.2
Basic theory of acoustic emission
Acoustic emission is a highly sensitive technique for detecting active microscopic events in a material. This section gives a brief overview of the most important as-pects of the AE technique; more details about the equipment and signal-processing are addressed in section 3.1. In Fig. 2.1 the process of generation and detection is illustrated. The AE technique involves a source, which is active in a material, and AE instrumentation for the detection of the waves: a sensor, a pre-amplifier and signal processing equipment. The event at the source causes a release of energy which propagates in the form of a transient stress wave. This wave propagates through the material, until it reaches the sensor. The sensor converts the small surface displacements into an electrical signal, which is transmitted to a nearby pre-amplifier and subsequently to the signal-processing equipment.
The acoustic emission waveform at the source is generally thought of as a simple pulse [3]. This is related to the nature of the generating source, and therefore the emissions contain a broad spectrum of frequencies. Depending on the source, the
frequency of the waves extends from tens of kHz up to tens of MHz [2, 4]. In general, the detected signal has a complex waveform, which depends on both the characteristics of the AE event and the wave propagation effects (wave modes, wave velocity, attenuation, reflections, and interference) between source and sensor. In addition to the wave propagation behaviour, the waveform is further changed by the sensor response. When a sensor is excited by a broadband transient pulse, it ’rings like a bell’ at its own natural frequencies of oscillation. These two effects, the material response and transducer response, can make the actual signals observed very different from the original pulses emitted by the source. This is discussed in more detail in section 2.2.1.
2.2.1
Material and transducer response
The simplest model for an acoustic emission event with non-zero rise time is the force dipole F (t), whose time variation is pulse-like giving a step-like displacement S(t) at the source [3] as shown in Fig. 2.2. The width and height of this pulse depend on the dynamics of the source process. During the event at a source elastic waves are generated. In order to evaluate the surface displacements due to an event at the source, it is important to understand the wave propagation behaviour in materials.
There are different wave modes for acoustic waves in materials: longitudinal (compression), transverse (shear), surface and plate waves. These waves travel at different velocities, which are a function of the density of the material, the Young’s modulus and the Poisson’s ratio. In the case of steel, the velocity of longitudinal waves is approximately 5000 m/s and transverse waves travel at a speed of approximately 3000 m/s. The wave velocity of the fastest mode in a material can be measured by using two sensors at different locations and is particularly important for source location, because it is used in the computation of the location of the source (see section 3.1.3).
For a semi-infinite material, it is in principle possible to relate the time variation of the displacement waveform at the sensor to the event life time at the source [3]. In a plate as shown in Fig. 2.2, however, the surface displacement waveform at the sensor has usually little resemblance to the original waveform at the source. Especially the later part of the waveform may have undergone significant changes due to multiple reflections, interference and mode conversions.
There are usually many wave paths connecting source and sensor, as illustrated in Fig. 2.2. Waves are reflected at the boundaries of the material. The amount of energy reflected depends on the geometric (angle of incidence) and material mismatch at the reflecting boundary. In the case that the damping of waves at re-flecting boundaries is low, the detected waveform is made up of many components reaching the sensor by different paths. This also implies that the signal amplitude does not necessarily result from the first component, but may result from the con-structive interference of several components arriving later at the sensor. The AE wave bounces around the specimen, thereby exciting the sensor each time it passes,
t X (t ) t S (t )
( )
U t
( )
X t
T
( )
S t
M
F
F
t U (t )M
T
Figure 2.2: The original waveform at the source S(t) is significantly changed after propagation through a plate (M ) and subsequent conversion in the transducer (T ) to an electrical signal U (t).
until it finally decays; the decay time depends on the dimensions of the specimen and the damping of the material. When the damping at the reflecting boundary is high, only the first component (the direct wave path) is measured. Typically, the duration of the detected waveform is much longer than the event life time at the source. Since the measured waveform displays the response of the specimen to the initial waves at the source, the frequency information in the waveform may there-fore be more related to the specimen geometry than to the event characteristics of the source.
The above described effects of the material (material properties and geometry of the specimen) on the source function S(t) can be written as [3]
X(t) = M ∗ S(t) (2.1)
with X(t) the displacement waveform at the surface and M the material response function. Such a mathematical description, indicated with the asterisk in Eq. (2.1), is known as a convolution of the signal. The opposite operation, i.e. the calcula-tion of the source descripcalcula-tion from the displacement is called deconvolucalcula-tion of the signal. It can be easily verified by taking the Fourier transform of Eq. (2.1) that an equivalent relation is valid for the source spectrum S(f ), with f the frequency of the waves. However, in Fourier space convolution is just multiplication, and therefore deconvolution is analytically possible in Fourier space.
The conversion of the displacement X(t) to an electrical signal U (t) is made by a highly sensitive transducer. AE transducers are typically based on a ceramic wafer of piezoelectric material (see Fig. 2.3). This material converts a mechanical deformation into an electrical voltage. In addition to the propagation through the material, the original signal is further changed during conversion in the transducer. The displacement waveform X(t) is convolved with the transducer response function T , as shown in Fig. 2.2, according to
U (t) = T ∗ X(t) = T ∗ M ∗ S(t) (2.2) where U (t) is the voltage output of the sensor. It should be noted that this waveform is radically different from the signal at the source. The original signal is signifi-cantly changed during propagation through the material and after conversion by the transducer. Although the transducer response function can be measured with reasonable accuracy, it should be realized that the attachment of the transducer to the material changes the mechanical boundary conditions at the previously free surface; the surface displacement is altered by the presence of the sensor. Further-more, the material response function is in practice difficult to determine, because an accurate simulation of AE sources is complicated, especially inside a material. All these complications mean that deconvolution of the measured voltage signal to evaluate the source function is extremely difficult, and in general has therefore not been pursued in the literature. Recently, some simulation studies on acoustic emission have been carried out [5, 6].
2.2.2
Sensors and pre-amplifiers
Sensitivity and bandwidth are the most important factors when choosing a sensor for AE monitoring. The sensor most often used nowadays for AE monitoring is the piezoelectric transducer [4]. The active element in a piezoelectric transducer is usually a special ceramic such as lead-zirconate-titanate (PZT). The piezoelectric crystal converts the displacement at its surface into an electrical voltage. It exhibits the piezoelectric effect: when the crystal is deformed, the electric voltage across the crystal is changed. The design of a typical AE sensor with piezoelectric element is shown in Fig. 2.3.
The sensor housing and electronics are designed to minimize electromagnetic interference (EMI). Regarding the electronics, the sensors can be divided into two types: single-ended or differential. A single-ended sensor contains one crystal and is susceptible to EM noise signals. In contrast, a differential sensor has a design such that common noise signals due to EMI are rejected. It contains two crystal elements of opposite polarity, and the signal outputs of these elements are transmitted to the two inputs of a differential pre-amplifier, where the difference of the two signals is amplified. A detected AE signal produces two voltage signals of opposite polarity and thus the difference is two times the signal output from one element. EMI signals picked up by the two electronic circuits produce signals of the same polarity, which cancel out in the pre-amplifier.
Figure 2.3: Schematic illustration of a typical acoustic emission sensor with piezo-electric element. After Miller [4].
Pre-amplifiers are used to provide a higher voltage, which is more usable for further processing. It is preferable to place the pre-amplifier close to the sensor to minimize pick-up of electromagnetic interference; sometimes the pre-amplifier is integrated in the sensor housing. Pre-amplifiers contain a frequency filter to reject unwanted noise signals, and have a wide dynamic range. They inevitably generate electronic noise (thermal noise), and it is this background noise (and that of the sensor) that determines the smallest microscopic movement detectable with AE.
The sensitivity of an AE transducer (detection threshold) can be defined as the minimum level of the signal amplitude that can be detected above the background noise. Whereas in other types of experiments such white noise can be reduced by signal averaging, this does not hold for AE experiments, because the relevant AE signals are also changing with time, and are in fact noise-type signals themselves. It is important that the pre-amplifier (and transducer) generate the minimum elec-tronic background noise. Typically for modern equipment, the smallest signal that can be well distinguished from the electronic noise is approximately 4 µV at the output of a typical transducer, corresponding to a surface displacement of about 10−14 m. This illustrates that piezoelectric transducers are extremely sensitive.
For comparison, atomic radii are in the order of 10−10 m, thus displacements of
1/10000 of an atomic radius can produce well-distinguishable AE signals. The dy-namic range of a transducer is normally 105, from 10−14 m to 10−9 m. Usually,
the amplitudes of AE signals are expressed on a logarithmic (decibel) scale, with 1 µV corresponding to 0 dB and 100 mV corresponding to 100 dB (each 20 dB is a factor 10).
Sensitivities of sensors are typically shown as frequency response diagrams (out-put voltage versus frequency). In order to fully characterize a source in terms of its time scale and/or frequency content, the transducer bandwidth should match or even overlap the bandwidth of the surface displacements. This is a difficult con-dition to fulfil because the frequency range of the surface displacements typically extends from 500 Hz to 500 MHz [8, 9] whereas the bandwidth of the transducer is usually in the range of 100 kHz to 1 MHz [3]. Therefore, the amount of source
information that can be retrieved from the detected signal is limited.
Two other types of sensors, alternative to piezoelectric crystals, that have been considered in the past are the laser interferometer and the capacitive transducer. However, their characteristics regarding sensitivity or bandwidth were not found to be optimal for acoustic emission monitoring. Laser interferometers have a too small bandwidth and therefore insufficient sensitivity for the typical bandwidth of acoustic emission. Capacitive transducers can be constructed to be sensitive over a wide frequency range with a flat frequency response. However, they are less sensitive; the typical minimum displacement that can be measured is in the order of 10−10m, which is normally insufficient for acoustic emission monitoring.
Sensor coupling and reproducibility of response are important factors. Calibra-tion checks should be performed after mounting the transducer on the specimen to ensure that the sensor is operating properly at the correct sensitivity. This is discussed in more detail in section 3.1.
2.2.3
Attenuation and noise
Whether a signal can be detected is in the first place determined by the sensitivity of the AE instrumentation and the amplitude of the elastic waves emitted by the source. Furthermore, the detectability of the generated AE signals depends on the attenuation and the noise over the frequency range of the detecting instrumentation. Attenuation refers to the reduction of the wave amplitudes during propagation. The major mechanisms governing attenuation are geometric spreading of the wavefront, loss of AE energy into adjacent media and damping in the propagating material [4]. The attenuation due to geometric spreading of the wavefront is dominant close to the source, because due to geometric spreading the amplitude falls off inversely with distance. Due to absorption, the amplitude falls off exponentially with distance; thus this attenuation mechanism becomes predominant far from the source. Also grain boundary scattering and scattering against welds may contribute to attenuation; their effects cannot be predicted quantitatively. The attenuation due to welds in the waveguides relevant for the work described in this thesis is discussed in section 3.1.2.
In laboratory studies the attenuation due to damping and geometric spreading does not normally limit the detectability because the specimens employed are usu-ally small. On the other hand, the use of a waveguide between the specimen and the sensor may influence the detectability very strongly in the following way: In case the waveguide is a rod with a cross-section that is relatively small compared to the size of the specimen, the waveguide acts as an acoustic resistor; not all the available AE energy in the specimen can be transmitted to the sensor. A systematic study of the attenuation of some different waveguides is given in section 3.1.2.
Sources of noise fall into two main categories, electrical and mechanical [4]. Noise sources should be examined to discriminate between noise and relevant acous-tic emission signals. Both relevant AE signals and noise signals can be classified as burst signals or continuous signals. The distinction is based on the rate of
oc-currence. For burst AE signals, start and end points are clearly visible whereas for continuous AE signals amplitude and frequency variations can be observed but the duration of the signal is relatively long.
Continuous noise signals may be caused by leaking air lines in the vicinity of the set-up or electromagnetic interference (EMI). The EMI noise signals are coupled to the acoustic emission equipment by radiation or electrical conduction. Some examples of sources of EMI are transformers, powerful lamps and electric motors. Many mechanical noise sources give rise to burst-type noise signals. In principle, any movement of mechanical parts in contact with the test object forms a potential source of noise. Fortunately, most mechanical noise diminishes in amplitude at frequencies above 100 kHz, i.e. in the operating range of the sensor.
Effort must be made to reduce ambient acoustic noise and EMI. In the absence of external noise sources, which can be obtained for example under laboratory conditions, the sensitivity is still limited by the noise produced by the pre-amplifier, i.e. the background noise.
2.3
Phase transformations in steel
During cooling of steel, phase transformations from austenite to ferrite, pearlite, bainite and martensite can occur in order of increasing undercooling below the Ar3
temperature [7]. The microstructure formed during cooling mainly depends on the chemical composition of the steel together with the cooling rate and the prior ther-mal history. The mechanical properties of steel, such as strength and toughness, are strongly correlated to the microstructure that is formed during cooling. Under-standing the various phase transformations is therefore of primary importance in order to optimize the microstructure and the mechanical properties.
Any phase transformation in the solid state involves nucleation and growth, and based on the mechanism by which the new phase is formed, two types of phase transformations can be distinguished: diffusional and diffusionless transformations. Excellent overviews of the characteristics of the diffusion-controlled transformations from austenite to ferrite and pearlite are given in references [7, 10, 11]. For the steels studied in this thesis, the final microstructure is mainly controlled by the austenite to bainite and martensite phase transformations. In this section the most important characteristics of these phase transformations are discussed.
2.3.1
Martensitic transformation
The martensitic transformation is a diffusionless first-order phase transformation during which the lattice distortion can be described by a combination of shears [12]. It involves a cooperative and almost simultaneous movement of atoms from parent to product phase. Sometimes this type of phase transformation is also called a displacive or shear transformation.
Martensitic transformations can occur in many metals provided the conditions are such that diffusion-controlled transformations are prevented [7]. The
transi-Figure 2.4: The Bain correspondence: (a) two fcc austenite cells to show that a tetragonal cell can be outlined in austenite (b) Bain strain of this cell with axial ratio√2 into bct martensite with c/a ratio dependent on the carbon content (After Christian [10]).
tion from austenite to martensite in steels is the best-known and most important martensitic transformation because of the technological importance of hardened steel. About a century ago, the martensitic microstructure in steel was first ob-served with a microscope by the German metallurgist Adolf Martens. Nowadays, martensite is the term commonly used to describe the transformation product in a system where the phase transformation occurs in a displacive manner, and corre-spondingly, the martensitic transformation is the generic name for these transitions. Owing to the diffusionless (displacive) character of the transformation, the martensite has exactly the same composition as its parent austenite. In most prac-tical cases the amount of carbon exceeds the solubility in ferrite, and consequently the martensitic phase in steel can be simply described as a super-saturated solution of carbon in the ferritic phase, in which the carbon content leads to a tetragonal distortion of the lattice. The correspondence in lattice structure between austen-ite and martensausten-ite was first pointed out by Bain. He showed that a body-centered tetragonal (bct) unit cell could be constructed between two face-centered cubic (fcc) unit cells as illustrated in Fig. 2.4a. The strain necessary to transform this bct unit cell into a martensite cell is known as the ’Bain strain’. There is a contraction along the z axis, and a uniform expansion along the x and y axes (see Fig. 2.4b).
Martensitic transformations usually occur under conditions of rapid cooling; then there is little time at high enough temperature for the carbon atoms to diffuse and consequently the carbon atoms are trapped in the octahedral sites of the
body-centered cubic (bcc) lattice structure. The equilibrium solubility of carbon in the bcc lattice is exceeded, and as a consequence of the transformation mechanism this results in the bct structure, the distorted form of the bcc structure. Accordingly, the tetragonality of the bct structure increases with increasing carbon concentration of martensite. Owing to the high carbon content, the martensitic crystal structure is actually a meta-stable phase. In case the temperature is increased (the martensite is heated) the carbon atoms become mobile and will diffuse from the martensite lattice to form carbides. During this so-called tempering, martensite decomposes into a mixture of ferrite and cementite, with concentrations according the Fe-C phase diagram.
In steels, the transformation of an austenitic microstructure to a martensitic microstructure usually takes place due to a decreasing temperature rather than as a function of time, which is referred to as an athermal transformation. A necessary condition for the transformation to start is that the free energy G of martensite (α0) is lower than that of austenite (γ). Since additional energy, such as surface
en-ergy and strain enen-ergy, is required for the transformation to take place, martensitic transformations do not begin at T0, where ∆Gγ→α
0
= 0, but start at a lower tem-perature, the martensite-start temperature Ms. The free energy change ∆Gγ→α
0
, which corresponds to the temperature difference between T0 and Ms, constitutes
the driving force for the transformation [12].
Besides the thermodynamics, which determine the available driving force for transformation, the occurrence of a phase change is governed by the kinetics. The kinetics of a martensitic transformation depend solely on nucleation, because the growth of a martensitic crystal usually occurs rapidly. It is well known that the mechanism of growth is displacive, i.e. the growth takes place by the cooperative movement of atoms. How the phase nucleates, however, is even today not com-pletely understood. This is mainly due to the great speed of formation, which makes the martensitic transformation a difficult process to study experimentally. The kinetics of the transformation are the main subject of chapter 6.
Below the martensite-start temperature, the nucleation of martensite during cooling is believed to take place at structural imperfections in the parent phase and these pre-existing embryos (defects) are stimulated to grow into martensite crystals at different degrees of undercooling below Ms; they have different energy
barriers to activation [11]. Since growth is very fast, each nucleation event almost instantaneously leads to the formation of a certain volume of the new phase. Be-cause of the different energy barriers to nucleation the volume fraction of martensite varies only with the degree of undercooling expressing the athermal character of the transformation. Although the exact nature of the nucleation sites is not com-pletely understood, a nucleus is usually visualized as an embryo of the new phase (martensite) which has a semi-coherent interface with the parent phase (austenite). This glissile interface consists of arrays of parallel dislocations, which glide on ap-propriate slip planes as the interface moves [11]. A more detailed description of the interface motion including the generation of acoustic emission during this process in addressed in chapter 7.
2.3.2
Bainitic transformation mechanism
Bainite is the transformation product that forms below the pearlite formation tem-perature and above the martensite-start temtem-perature. Due to additional changes during and/or after the phase transformation a strong diversity exists in the mi-crostructural appearance of bainite. Although the bainitic reaction has been studied extensively since the discovery of bainite in 1930 by Bain and Davenport, there is still no general agreement about the mechanism of bainite formation [13, 14]. Two alternative models have been proposed to describe the transformation kinetics: the diffusional model and the displacive model [15, 16, 17].
The diffusional model assumes that the transformation mechanism involves re-constructive diffusion of substitutional atoms, i.e. ferrite and cementite are preci-pitated from austenite by diffusive mechanisms [16]. This mechanism thus is similar to the formation of pearlite, although the typical lamellar structure of pearlite does not occur.
In the displacive model, the atomic rearrangements during bainite formation are believed to occur in a diffusionless fashion as far as the substitutional atoms are concerned [15, 17]. In fact, it is assumed that a plate of bainite forms according to a martensite-like mechanism without diffusion, followed by a rejection of excess carbon into the remaining austenite which subsequently forms carbides. It should be emphasized that in this model the growth occurs without diffusion, whereas the nucleation at the austenite grain boundaries might still require some partitioning of carbon [17, 18].
For martensite, crystallographic analysis can be used to verify that the transfor-mation takes place without diffusion since the local compositions before and after the transformation are equal. Bainite, however, forms at somewhat higher temper-atures, at which the carbon can still escape from the bainitic ferrite. This implies that by crystallographic means it is difficult to determine the nature of the bainitic reaction mechanism.
Regarding the morphology of bainite, two main structures can be identified which are called upper and lower bainite. Upper bainite forms at a relatively high temperature, usually in the range of 450 – 600◦C, and lower bainite between
300 and 450 ◦C. The change in morphology with transformation temperature is
a direct consequence of the change in diffusivity of carbon. In the case of upper bainite the diffusivity of carbon is relatively high and therefore carbide precipitates from the carbon-enriched austenite between the ferrite plates. For lower bainite also carbide precipitation within the bainitic ferrite occurs owing to the decrease in diffusivity of carbon in austenite at lower temperatures; the diffusivity in bainitic ferrite is much higher. Therefore, two kinds of cementite can be recognized in lower bainite: cementite particles that precipitated from the carbon-enriched austenite and cementite particles that precipitated from supersaturated ferrite. The latter precipitation shows a strong resemblance with the tempering of martensite. The layers of carbide in lower bainite are usually extremely fine compared with those in upper bainite. Consequently, a steel with a lower bainitic microstructure is tougher
than a steel with an upper bainitic microstructure. Moreover, lower bainite is stronger since the precipitates are finer.
The amount of cementite particles in bainite does not only depend on the carbon concentration but also on the alloying elements. For example, by increasing the silicon concentration the cementite precipitation can be greatly retarded because silicon has a negligible solubility in cementite. This can improve the toughness of bainitic steels, and is also of importance in the production of TRansformation Induced Plasticity (TRIP) steels.
Acoustic emission and the displacive character of transformations In the past many debates have taken place dealing with the exact nature of dis-placive (martensitic) transformations and how to define such a transition. Although it is still not completely understood how growth of the new (martensitic) phase occurs, there is general agreement between researchers that a displacive transfor-mation involves the cooperative movement of atoms that causes a shape change. Despite this theoretical agreement, it is sometimes very difficult to prove that a transformation is diffusionless, especially in the case of bainite discussed above. Usually the high transformation kinetics at the relatively low transformation tem-perature is given as an argument that the transformation must be diffusionless. However, this argument is no real proof since what speed is high and what tem-perature is low is open to question. The best known and well recognized proof for the displacive character of a transformation is probably the observation of surface upheavals on a polished surface.
Recently, it was argued by Clapp [19] that acoustic emission is the best test to prove the displacive character of a phase transformation. This is based on the fact that the emission of acoustic energy is strongly related to the coordinated movement of atoms. In contrast to the above mentioned metallographic test, acoustic emission offers a relatively simple in-situ test to investigate whether or not the transformation is displacive. In this respect it is quite surprising that acoustic emission has only rarely been used to monitor phase transformations, especially since it has been known for a long time that the formation of martensite in steel is accompanied by acoustic emission. In view of above arguments, acoustic emission monitoring during bainite formation will give valuable information about its transformation mechanism.
A general study of acoustic emission during phase transformations in carbon steels is presented in chapter 4, with the emphasis on the bainitic transformation. In chapter 5 the acoustic emission energy is studied as a function of the transformed volume. How the acoustic emission technique can be used to follow the progress of martensitic transformations is discussed in chapter 6. Finally, in chapter 7 the char-acteristics of the acoustic waves generated during the bainitic and the martensitic transformation are studied.
[1] J. Kaiser, Untersuchungen uber das auftreten Gerauschen beim Zugversuch, Ph.D. thesis, Technische Hochschule, Munich (1950).
[2] A.A. Pollock, Practical guide to acoustic emission testing, PAC, Princeton (1988).
[3] H.N.G. Wadley, C.B. Scruby and J. Speake, Int. Metals Rev. 3, 41 (1980). [4] R.K. Miller, P. McIntire, Acoustic Emission Testing, Vol 5, 2nd ed.,
Nonde-structive Testing Handbook, (American Society for NondeNonde-structive Testing, 1987).
[5] J. Cerv, M. Landa and A. Machova, Scripta Mater. 43, 423 (2000).
[6] W.M. Mullins, R.D. Irwin, J.C. Malas III and S. Venugopal, Scripta Mater. 36, 967 (1997).
[7] D.A. Porter and K.E. Easterling, Phase transformations in metals and alloys, Chapman & Hall, London (1992).
[8] C. Scruby, H. Wadley and J.J. Hill, J. Phys. D: Appl. Phys. 16, 1069 (1983). [9] W.J.P. Vink, Niet-destructief onderzoek, 1st ed., Delftse Uitgevers
Maatschap-pij, Delft (1995).
[10] J.W. Christian, Theory of Transformations in Metals and Alloys, 3rd ed., El-sevier Science, Oxford (2002).
[11] R.W.K. Honeycombe and H.K.D.H. Bhadeshia, Steels, Edward Arnold, Lon-don (1995).
[12] Z. Nishiyama, Martensitic Transformation, Academic Press, London (1978). [13] R.F. Hehemann, K.R. Kinsman and H.I. Aaronson, Metall. Trans. 3, 1077
(1972).
[14] H.I. Aaronson and H.J. Lee, Scripta Metallurgica 21, 1011 (1987). 21
[15] H.K.D.H. Bhadeshia, Bainite in steels, The Institute of Materials, London (2001).
[16] W.T. Reynolds, Jr., H.I. Aaronson and G. Spanos: Mat. Trans. JIM 32, 737 (1991).
[17] Y. Ohmori and T. Maki, Mat. Trans. JIM 32, 631 (1991).
[18] G.B. Olson, H.K.D.H. Bhadeshia and M. Cohen, Acta Metall. 37, 381 (1989). [19] P.C. Clapp, J. Phys. IV 5, 11 (1995).
Experimental
In this chapter the experimental equipment is described that was used to measure the acoustic emission signals generated during phase transformations in steel. A description of the AE system used is addressed in section 3.1, including a discus-sion of noise suppresdiscus-sion precautions, attenuation due to waveguides and source location. In most experiments an arc welding device or a thermo-mechanical (weld-ing) simulator was used to apply a thermal cycle to the studied specimen, which upon cooling was monitored by means of acoustic emission. These two heat cycling methods are characterized by a relatively high cooling rate due to the metallic heat conduction. The set-ups for AE measurements during welding and during thermal cycling in the thermo-mechanical simulator are described in section 3.2 and section 3.3 respectively. A few experiments were performed using a conventional dilatome-ter in order to facilitate comparison between acoustic emission and dilatometry. In section 3.4 the set-up and measurement procedure for experiments with the AE system connected to the dilatometer is given. Furthermore, some experiments were performed using a furnace in order to study large-sized samples, which give a better signal to noise ratio. During quenching in a salt bath the acoustic emission was measured as described in section 3.5. At the end of the chapter an overview is given of the steels studied in this thesis.
3.1
Acoustic emission system
In order to measure the AE signals a PAC (Physical Acoustics Corporation) AE system was used [1]. This is a fully digital two-channel acoustic emission system that performs AE waveform and AE signal parameter measurements and stores and displays the resulting data. The main components are the AEDSP board, which is integrated in a standard computer, and the Mistras software.
In addition to the two AE channels, the system has two input connections for external signals, which are known as parametric 1 and parametric 2. The external signal can be derived from a load cell or a thermocouple if the applied stimulus to
(a)
(b)
Figure 3.1: (a) Typical burst AE waveform. (b) Part of continuous AE waveform.
produce AE is respectively a force or a temperature change. This parametric input, which is recorded along with the AE data, can be used for the interpretation of the acquired data, in order to relate the AE signal to the stimulus (force, temperature). Usually, the measured AE parameters are plotted against the parametric input.
Acoustic emission is normally described in terms of parameters associated with the magnitude and rate of occurrence of acoustic emission events [2, 3]. Depending on the rate of occurrence of AE signals at the sensor, two types of signals can be distinguished: burst and continuous emission. A burst-type signal is thought of as a signal from a single, discrete event. When the rate of occurrence is high, the individual burst signals overlap and combine to form continuous emission. It should be realized that sometimes the distinction can be rather arbitrary, for instance when the successive individual signals are visible in the overall continuous wave. In Fig. 3.1 an example of both types of AE signals are shown.
Related to the two types of signals, two types of data are recognized: time driven data and hit driven data for continuous and burst emission respectively [1]. In Fig. 3.2 a block-diagram is given which illustrates how the data acquisition is performed, what kind of data can be measured and which are the control settings. Continuous acoustic emission is characterized by the root mean square (rms) voltage Urms of the recorded waves. The mean square voltage U2 corrected for the
background noise is defined by [4]
U2(t) = 1 τ t+τ Z t Up2(t0)dt0− Un2 (3.1)
where Up(t) is the voltage output at the pre-amplifier and τ a time constant usually
chosen as τ = 0.1 s. The amplification of signals is standard 40 dB (100 ×) and throughout this thesis the amplified values are displayed; they are not converted back to the voltage output at the transducer. For measurements of the rms voltage with an amplification of 60 dB, the results are divided by 10, i.e. converted back to
=
>
ττ
+ = ! " τ # "Figure 3.2: Diagram showing the two types of acquired AE data with the most important settings and parameters: time driven data for continuous emission and hit driven data for burst emission.
the 40 dB scale. The rms voltage is measured with a resolution of 0.02 mV relative to a background noise level (U2
n)1/2 of 0.24 to 0.28 mV. It should be mentioned
that early measurements were performed with a resolution of 0.2 mV.
For characterizing burst-type AE signals a threshold level is set somewhat above the background noise level; the chosen threshold value depends on the amplitude of the AE signals and the desired amount of data acquired. If the AE signal exceeds the threshold in either positive or negative direction a so-called hit is recorded. A typical example of a corresponding waveform is shown in Fig. 3.3. Such a waveform is produced by joining many single points called samples. They correspond to single measurements at constant time intervals. The system can sample with 1, 2, 4 or 8 MHz; normally a sample rate of 4 MHz is sufficient for measuring signals with frequencies up to 1 MHz.
In general, some hundreds or thousands of bursts are recorded for evaluation. To evaluate all the waveforms corresponding to the bursts requires a huge amount of memory, and interpretation of the waveforms themselves is difficult. Therefore the most important features of each waveform are determined, which are called the AE parameters. These allow an easier comparison with other results. The main signal parameters describing the waveform are the signal amplitude, the signal rise time and the signal duration. They are illustrated in Fig. 3.3. The time of the first threshold crossing is called arrival time and is needed for the calculation of the location of the AE event. The parameter ’counts’ gives the number of times the signal crosses the threshold. The amplitude is the peak voltage of the AE waveform and can be a useful measure of the signal size. The time from the first threshold
Figure 3.3: The system timing parameters for capturing of a burst-type AE signal, and the commonly used AE parameters to describe the waveform.
crossing (count) to the peak voltage is the rise time; it can for instance be used to filter out noise signals, since these have usually very short rise times. The duration is the time from the first to the last threshold crossing.
The above-mentioned signal parameters cannot simply be related to the charac-teristics of the source because they strongly depend on the threshold setting and the system timing parameters. For so-called hit driven data the measurement process begins when the voltage signal from the pre-amplifier first crosses the threshold. This threshold crossing triggers certain timers which determine when the hit has passed and the system is ready for the next hit, hence, this is not trivial. The timers that capture a hit are the Peak Definition Time (PDT), the Hit Definition Time (HDT) and the Hit Lockout Time (HLT) [1]. They determine to a large extent the measured AE parameters described above, such as rise time, duration and peak amplitude.
The function of the PDT is to enable the determination of the true peak am-plitude and rise time of the AE waveform. The PDT circuitry is triggered by the first maximum after the threshold crossing and retriggered if a new maximum is measured within the set PDT. The function of the HDT is to enable the system to determine the end and thus the duration of the waveform. The HDT circuitry is (re)triggered by the threshold crossing(s). When no threshold crossing occurs within the set HDT, the end of the hit is defined by the last threshold crossing. The HDT should be set as short as possible to ensure that two (or more) separate hits will not be treated as a single hit; but also not too short to avoid
fragmenta-Figure 3.4: The typical frequency response of a wide-band sensor (PAC model WD).
tion of the burst signal. The function of the HLT is to exclude the measurement of reflections and late-arrival parts of the AE signal. The HLT circuitry is triggered by the time out of the HDT.
Common AE plots are based on the measured parameters of the AE signal, together with the external parametric input variables, such as load, temperature, dilatation etc. The plots can be classified into different types such as history plots, distribution plots and location plots. It should always be remembered that mea-sured hit driven data cannot simply be compared with the results obtained by other researchers because they depend strongly on the system settings. In this thesis mainly time driven data (Urms) is used, whilst in chapter 7 hit driven data
is used for the frequency analysis of waves.
3.1.1
Sensor mounting and noise precautions
Surface displacements were measured with a wide-band (100 – 1000 kHz) differential AE sensor (PAC model WD). The frequency response of this sensor is shown in Fig. 3.4. In order to determine this sensitivity of the sensor, a calibration was carried out according to the face-to-face technique, which is based on the voltage output per unit of pressure input.
The signal from the sensor is amplified by 40 or 60 dB with a low-noise broad-band (100 – 1200 kHz) pre-amplifier (PAC 1220A). The measured rms voltage due to the electronic noise of the sensor and the pre-amplifier is 0.24 to 0.28 mV. Al-though the sensor has a differential design, the shielding to guard against external EMI noise is not sufficient under all circumstances. For example, it was found that in the case the pre-amplifier was positioned closer than approximately 20 cm to the computer monitor, the background noise level increased significantly. Positioning of the sensor close to a computer monitor also leads to an increase in background noise, typically to a value of 0.5 to 1 mV.
out. Many noise problems will become apparent during setting up of the experiment and can be dealt with before data acquisition starts. Good observation and a careful and systematic approach are very important for diagnosis of noise problems. For example, if the noise has a continuous character, the cause is probably an electrical problem (e.g. bad shielding of the BNC cable). On the other hand, if the noise pattern is rather irregular, the noise source is for instance the descaling of an oxide layer during cooling of the specimen. At best, the background noise is just the electronic noise of the pre-amplifier and the sensor.
An essential requirement in mounting a sensor is sufficient acoustic coupling between the wear-plate of the sensor and the surface of the specimen. Before mounting, the wear-plate and the surface need to be cleaned. In case the surface of the specimen is not smooth, it has to be polished/ground with silicon-carbide paper. Then the couplant, e.g. vacuum grease, can be smeared on the wear-plate. Subsequently, the sensor can be pressed on the surface of the specimen. The couplant layer should be thin and fill all the gaps caused by the surface roughness to ensure a good acoustic transmission. The sensor should also be attached firmly to the mounting surface at all times during operation. This can be achieved by a holding device such as magnetic hold-down or just tape. Electrical contact between the sensor case and the structure needs to be avoided.
In the case the specimen becomes very hot or very cold, a waveguide is required for two reasons. The temperature range in which the sensor can operate is typically −40 – 180 ◦C . Secondly, commonly used couplants may become unstable at very
high or low temperatures. Waveguides are also necessary when the size of the specimen is smaller than the diameter of the sensor or when access to the specimen is difficult. These reasons for using a waveguide are especially relevant for laboratory studies. A waveguide is typically a metal rod which conducts the acoustic signal from the specimen to the sensor. One end is designed for acoustic coupling with the specimen; the other end is usually conical to accommodate the mounting of an AE sensor. To minimize attenuation, the diameter of the waveguide should be as large as possible, and the waveguide should have an acoustic impedance similar to that of the specimen. Furthermore, it is preferred that the joints are made by welding to obtain a good acoustic conductance.
3.1.2
Attenuation due to waveguides
Preliminary measurements showed that the use of waveguides reduces the measured AE energy in comparison with the case where the sensor is mounted directly onto the workpiece. The results indicated that the attenuation is primarily governed by the diameter of the waveguide and the quality of the welded joints.
To investigate the attenuation due to the presence of a waveguide in a quantita-tive manner, three waveguides with different diameters (d = 1, 2 and 4 mm) made of plain steel were welded onto the workpiece as shown in Fig. 3.5. The length of each waveguide was 100 mm; the disc-shaped mounting plates for the sensor were identical (diameter = 24 mm, thickness = 10 mm).
Figure 3.5: The experimental set-up used to measure the attenuation due to wave-guides: (1) welding torch; (2) spot weld; (3) workpiece; (4) waveguide; (5) sensor-1; (6) sensor-2; (7) pre-amplifiers (60 dB); (8) AE analyzing system.
t [s]
0 1 2 3 4 5U
rm s[
m
V
]
0 2 4 6 8 10 12 14 plate d = 4 mm d = 2 mm d = 1 mmFigure 3.6: The rms voltage as a function of time for a sensor on three different waveguides and another sensor mounted on the plate.
π
d2/4 [mm2] 0 4 8 12 16 no rm al iz ed d et ec te d en er gy 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 d = 1 mm d = 2 mm d = 4 mm d = 2 mm, l = 200 mm d = 4 mm, RVSFigure 3.7: The detected energy for each waveguide normalized to the detected energy on the sensor that was mounted directly onto the plate.
As a highly reproducible source of continuous acoustic emission the martensitic transformation, which occurs during cooling of a spot weld, was employed [5, 6]. After the production of a spot weld, the acoustic emission due to the formation of martensite in the weld was measured with two identical sensors: sensor-1 mounted on a waveguide and sensor-2 mounted on the plate. With both sensors the rms volt-age of the generated acoustic emission signals was measured as a function of time. Repeated measurements with a certain waveguide did not reveal any significant systematic differences.
The results obtained for the three waveguides are plotted in Fig. 3.6. As ex-pected, it can be seen that the intensity of the signal decreases with decreasing waveguide diameter. For each waveguide the AE energy detected on sensor-1 was determined from the area under the peak in a plot of U2 against t, i.e. the
in-tegrated value ∫ U2dt [5, 6]. The calculated values, normalized to the AE energy
detected on sensor-2, are plotted against 1 4πd
2in Fig. 3.7. It can be seen that the
acoustic conductance of a waveguide is proportional to the cross-section. Although theoretical predictions for comparison do not exist, the relationship observed seems reasonable in view of the fact that a similar relationship exists for electric and heat conduction.
In order to investigate the influence of the welded joint between the waveguide and the plate on the measured AE energy, the measurements were repeated with identical waveguides spot welded on the plate. The results from this second series of experiments indicated that the quality of the welds have a quite strong effect on the measured signal, which is expressed by the error bars in Fig. 3.7.
t [s]
0 1 2 3 4U
2 /U
2 m ax im um 0.0 0.2 0.4 0.6 0.8 1.0 plate d = 4 mm d = 2 mm d = 1 mmFigure 3.8: The mean square voltage U2 (normalized to the maximum value) as
a function of time for the sensor mounted on the plate and the sensor mounted on three different waveguides. This shows that the attenuated signals and the non-attenuated signal from Fig. 3.6 can be mapped onto each other by a single multiplication factor.
To examine the influence of the waveguide material, a measurement with a stainless steel rod (d = 4 mm, RVS) was performed. The result obtained was not significantly different from the result obtained for the steel rods discussed above (see Fig. 3.6). Furthermore, the measured signal did not change significantly when the length of the waveguide rod was increased to 200 mm (d = 2 mm, l = 200 mm), which indicates that the waveguide diameter is much more important than its length. Using the same set-up with the two sensors, it was also found that a thick layer of grease between the sensor and the plate has a strong attenuation effect on the measured signal.
With respect to the power (U2) of the signals shown in Fig. 3.6, it is interesting
to mention that for all measurements, the signals obtained can be mapped onto each other by a single multiplication factor as shown in Fig. 3.8. This indicates that the attenuated signals contain the same information of the martensitic transformation as the non-attenuated signal.
3.1.3
Source location
By using multiple sensors the position of an AE source can be determined. Com-putation of the source location is possible by using the wave velocity of the acoustic
