\input texp.tex

\centerline {\large Estimation of The Mechanical Properties of  Ferritic
 Steel Welds:}
\centerline {\large Part II: Elongation and Charpy Toughness}

\centerline{S. H. Lalam,  H. K. D. H. Bhadeshia and D. J. C. Mackay$^{\dag}$}\medskip
\centerline{ University of Cambridge}
\centerline{ Department of Materials Science and Metallurgy}
 \centerline{ Pembroke Street, Cambridge CB2 3QZ, U.K.}
\medskip

\centerline{ $^{\dag}$Cavendish Laboratory}

\centerline{Madingley Road, Cambridge CB3 0HE, U.K.}
 \singlespace


\sec {ABSTRACT}

{\parindent=20pt \narrower \medskip
   \x  In previous work we presented models which can be used to  estimate the
yield and ultimate tensile strengths of ferritic steel welds. The work
presented here deals with properties which are much more difficult to predict,
the elongation and Charpy impact toughness. Whereas the models are found to be
useful and  emulate expectations  from current physical
metallurgy principles, it is clear that much more systematic experimental data
are needed before the predictability becomes as good as the strength models of
Part I.

 \medskip}


\sec {INTRODUCTION}

 The most common experiments done on weld metals include the testing of a sample
in tension to measure the strength and ductility, and the measurement of the
Charpy impact toughness. Although these tests are quite simple to conduct,
the principles governing the properties that are measured, are  
understood only on a  qualitative basis. In Part~1 of this paper [1], we
developed neural network models which allow the estimation of the yield and
ultimate tensile strengths of ferritic steel weld metals using a vast quantity
of data collected from the published literature and from commercial sources.
The predictions that can be made using these models are associated with
error bars which consist of  the perceived level of noise in the
output and a component representing the uncertainty of fitting. The
predictions are reliable when the error bars are small, but have to be used
with caution when they are not; large error bars can indicate a need for further experiments  to fill gaps in knowledge. In this sense, all predictions are useful irrespective of
the magnitudes of the error bars.

The purpose of the work presented here was to develop similar models for the
elongation and Charpy properties. There has been little practical progress in
modelling the tensile ductility of weld metals [2]. The ductility can 
to a good approximation be divided into two  components 
whose magnitudes are assumed to be controlled by different 
physical processes. These components are the uniform plastic 
strain, as recorded prior to the onset of  necking 
in the tensile specimen, and the nonuniform component which 
is the remainder of the plastic strain. 

By factorising the ductility in this way, it is 
possible to express the nonuniform component in terms of the 
inclusion content of the weld deposit, after taking into 
account variations in specimen cross--sectional area ($A_O$) 
and gauge length ($L_O$) [3]:
$${\rm nonuniform~~~elongation, \%\ } = 100 \times \beta 
{{A_O^{0.5}}\over {L_O}} \numeqn $$
where $\beta$ is Barba's constant, but now expressed as a 
function of the inclusion content [4]:
$$ \beta \simeq 1.239 - 9.372 \times (\rm wt.\%\ O) + (\rm 
wt.\%\ S). $$
There is as yet no reliable model for estimating the uniform component 
of strain, but such a model would require a detailed knowledge of the strain hardening behaviour of the individual phases of the microstructure, together with some theory for 
multiphase deformation. As for the nonuniform component is concerned, equation (\nnumeqn) emphasises the role of particles in reducing ductility. There are only two inputs, whereas a vast number of other variables are known to influence the
elongation that is measured in a tensile test. Hence the need for a different
approach which encompasses a wider set of variables.

The concept of toughness as a measure of the energy absorbed during
fracture is well--developed [5,6]. It is often measured using notched--bar
impact tests of which the most common is the Charpy test.  A
square section notched bar is fractured under specified conditions and the
energy absorbed during fracture is taken as a measure of toughness. The
Charpy test is empirical in that the data cannot be used directly in engineering
design. It does not provide the most searching mechanical conditions. The
sample has a notch, but this is less than the atomically sharp brittle crack.
Although the test involves impact loading, there is a requirement to start a
brittle crack from rest at the tip of the notch, suggesting that the test is
optimistic in its comparison against a propagating brittle crack [6].  Most
materials can be assumed to contain sub--critical defects so that the initiation
of a crack seems seldom to be an issue.

The Charpy test is nevertheless a vital quality control measure which is
specified widely in international standards, and in the ranking of samples in
research and development exercises. It is the most common first
assessment of toughness and in this sense has a proven record of
reliability. The test is usually carried out at a variety of temperatures in order to
characterise the ductile--brittle transition intrinsic to body--centred cubic
metals with their large Peierls barriers to dislocation motion.

It would therefore be useful to be able to quantitatively model the Charpy
toughness as a function of  metallurgical variables that are
believed to influence the cleavage and ductile fracture modes of commercial
steels. Some of these variables have in the past been studied quantitatively
(for example, the flow stress as a function of temperature [7]) whereas others
(such as the degree of organization in the microstructure [8]) have been
expressed using language alone.


Complex problems such as those described above, can usefully be
modelled empirically using an artificial neural network. The method has been
discussed thoroughly in Part I [1].


 \sec {ELONGATION MODEL}

 This model consists of the 20 input variables listed in \tablaa, which
are considered to influence ductility. The detailed chemical composition, the
heat treatment and the welding heat input and interpass temperature
essentially determine the microstructure and properties. It is easy to imagine
other variables which might be important, such as the size distribution of
oxide particles. However, the compilation of a dataset for neural network
analysis is always a compromise between two factors. Firstly, a larger dataset
is of value in creating a model based on a greater span of knowledge. However,
the probability of finding appropriate data diminishes as the number of
variables is increased, because incomplete sets of inputs are not of use in
the analysis.  The database reflects
multipass  welds made using the submerged arc, gas tungsten arc and manual metal
arc welding processes.  The welding parameters are represented by the heat input
and the interpass temperature; the post--weld heat treatment conditions are
represented by  temperature and time. The sources of
the data are listed in references [9--81]. The elongation values are those
measured on standard, cylindrical tensile test specimens, where the gauge length
is generally specified to be 5.65$\times \sqrt A$, where $A$ is the
cross--sectional area. 

A total of 1972 individual experimental data were
gathered. In 19 cases, the sulphur and phosphorus concentrations
were not reported, in which case they were set to the average values of the
other data in the database.  This is a better procedure than setting the
concentrations to zero because all welds inevitably contain impurities. On
the other hand,  alloying additions such as molybdenum can be set to
zero when they are not added deliberately, without affecting the overall
microstructure or mechanical property outcomes.  A visual impression of
the whole elongation database is shown in \fagg. The mean and standard deviation
of the percentage elongation are 26 and 5 respectively, showing that most of
the data lie in the range 21-31\%, \fagg. 

\bigskip

 \thicksize=0.7pt \thinsize=0.5pt
 \tablewidth=5.4truein {\smalltype \begintable
 {\bf Input element  }\hfill | {\bf Minimum  }\hfill |{\bf  Maximum  }\hfill | {\bf Mean  }\hfill | {\bf Standard Deviation } \cr
 Carbon     (wt\%)  \hfill | 0.01 \hfill | 0.16 \hfill | 0.07 \hfill | 0.0184 \nr
 Silicon    (wt\%) \hfill |  0.01 \hfill | 1.14 \hfill | 0.35 \hfill | 0.124 \nr
 Manganese  (wt\%) \hfill | 0.24 \hfill | 2.31 \hfill | 1.23 \hfill | 0.386 \nr
 Sulphur    (wt\%) \hfill | 0.002 \hfill | 0.14 \hfill | 0.008 \hfill | 0.005 \nr
 Phosphorus (wt\%) \hfill | 0.001 \hfill | 0.25 \hfill | 0.01 \hfill | 0.007 \nr
 Nickel     (wt\%) \hfill | 0.0 \hfill | 5.48 \hfill | 0.322 \hfill | 0.88 \nr
 Chromium   (wt\%) \hfill |  0.0 \hfill | 9.4 \hfill | 0.45 \hfill | 1.19 \nr
 Molybdenum (wt\%) \hfill | 0.0 \hfill | 2.4 \hfill | 0.17 \hfill | 0.358 \nr
 Vanadium   (wt\%) \hfill | 0.0 \hfill | 0.32 \hfill | 0.015\hfill | 0.044 \nr
 Copper     (wt\%) \hfill | 0.0 \hfill | 2.04 \hfill | 0.063 \hfill | 0.204 \nr
 Cobalt     (wt\%) \hfill | 0.0 \hfill | 2.8 \hfill | 0.005 \hfill | 0.097 \nr
 Tungsten   (wt\%) \hfill | 0.0 \hfill | 3.86 \hfill | 0.024 \hfill | 0.207 \nr
 Oxygen     (ppm wt.) \hfill | 63.0 \hfill | 1650 \hfill |  414 \hfill |  118 \nr
 Titanium   (ppm wt.) \hfill |  0.0 \hfill |  1000 \hfill |   86 \hfill |  127 \nr
 Boron      (ppm wt.) \hfill | 0.0 \hfill |  200 \hfill | 11 \hfill |   30 \nr
 Niobium    (ppm wt.) \hfill | 0.0 \hfill | 1770 \hfill |   48 \hfill |  141 \nr
 Heat input (kJ mm$^-$$^1$) \hfill | 0.55 \hfill | 4.8 \hfill | 1.23 \hfill | 0.71 \nr
 Interpass temperature ($^\circ$C) \hfill | 20 \hfill |  350 \hfill |  204 \hfill |   35 \nr
 Tempering temperature ($^\circ$C) \hfill | 20 \hfill |  750 \hfill |  321 \hfill |  191 \nr
 Tempering time  (h) \hfill | 0.0 \hfill |   32 \hfill | 10 \hfill | 6.2 \cr
 Elongation (\%)\hfill |  7.4 \hfill | 41.1 \hfill |  26 \hfill |  5
 \endtable }
 {\tabtit{\tablee : } {The variables used in developing the elongation model.}}




The training, test and log predictive errors   [82,83] associated with each of
the eighty models created are shown in \fagg. The behaviour of the single best
model is illustrated in \figg d,e. From the set of 80 models, a committee of 58
of the best models was found to give the lowest test error (\figg d); each
member of the committee was then retrained on the entire dataset to create the
final committee model (\figg f). The details of all these procedures are
described in [1]. \fagg\ shows the significance $\sigma_w $ of each of the input
variables. The behaviour of the committee model in making predictions for
specific cases in now illustrated for C--Mn weld metals.


\sec {APPLICATION TO C--Mn WELD METAL}

    The set of variables used for analysis is listed in
\tablaa; any variations illustrated in subsequent diagrams are about these
values. An increase in the strength is expected to lead to a decrease in
elongation [84]. It is not surprising therefore, that the
elongation decreases when the manganese and carbon concentrations are increased,
\fagg. Notice also that the effect is more pronounced at higher manganese
concentrations, consistent with the corresponding effect on strength.

 \midinsert
 \vskip 0.5 in
  \thicksize=0.7pt \thinsize=0.5pt
 \tablewidth=3.0truein {\smalltype \begintable
 Carbon     (wt\%)  \hfill | 0.06 \nr
 Silicon    (wt\%) \hfill | 0.50 \nr
 Manganese  (wt\%) \hfill | 1.50 \nr
 Sulphur    (wt\%) \hfill | 0.006 \nr
 Phosphorus (wt\%) \hfill | 0.008 \nr
 Nickel     (wt\%) \hfill | 0.0 \nr
 Chromium   (wt\%) \hfill | 0.0  \nr
 Molybdenum (wt\%) \hfill | 0.0 \nr
 Vanadium   (wt\%) \hfill | 0.0 \nr
 Copper     (wt\%) \hfill | 0.0\nr
 Cobalt     (wt\%) \hfill | 0.0\nr
 Tungsten   (wt\%) \hfill | 0.0 \nr
 Oxygen     (ppm wt.) \hfill | 300 \nr
 Titanium   (ppm wt.) \hfill | 0.0 \nr
 Boron      (ppm wt.) \hfill | 0.0 \nr
 Niobium    (ppm wt.) \hfill | 0.0 \nr
 Heat input (kJ mm$^-$$^1$) \hfill | 1.00 \nr
 Interpass temperature ($^\circ$C) \hfill | 175 \nr
 Tempering temperature ($^\circ$C) \hfill | 250 \nr
 Tempering time  (h) \hfill |  14.0
 \endtable}
 \tabtit{\tablee : }{ The inputs relevant for a typical \lqq
carbon--manganese" weld metal.}
 \endinsert

Ductile fracture can be described in terms of the 
nucleation, growth and coalescence of voids. Macroscopic  
fracture occurs when the voids link on a large enough scale. 
If the number density of voids is large, then their mean 
separation is reduced and coalescence occurs rapidly, giving 
a minimal amount of plastic deformation before fracture, and 
reducing the overall ductility (\fagg ). 



It has generally been assumed that in weld metals both sulphur and oxygen
contribute to the inclusion content and hence must be detrimental to the
toughness. Whereas it is found that an increase in the oxygen concentration
definitely reduces the elongation (\fagg a), the picture for sulphur is not
clear. The data in fact show a slight increase in elongation with the
sulphur concentration but the trend is not meaningful when the error
bars are taken into account (\figg b). Oxides are the main inclusions in weld
metals [85] whereas sulphur tends to be deposited in the form of very thin
layers on top of the oxide particles [86]. The observed trends may not therefore
be unreasonable in weld metals as opposed to wrought steels which tend to have a
very low oxygen concentration with a predominance of sulphide inclusions.

Another interesting feature of the difference between weld metals and
C--Mn type hot--rolled  steels is revealed by comparing the dataset used
in a previous analysis for wrought alloys [87] with the present work
which is on weld metals (\fagg). The difference between 
the yield strength ($\sigma_{YS}$) and ultimate tensile strength
($\sigma_{UTS}$) for welds is approximately constant at 100~MPa whereas for
plates, the difference becomes smaller as the strength increases.

The plastic strain $\varepsilon$ can be described as a function of strain using
a power law of the form 
$$\sigma = { K}\varepsilon^{ n} \numeqn $$
where $K$ and $n$ are constants, the latter being the strain hardening
coefficient. Since the yield strength is measured at a plastic strain of 0.02,
and the UTS is given by $Kn^n$, it follows that 
$$\sigma_{UTS} - \sigma_{YS} = K[n^n - 0.02^n] \numeqn$$

Since for the welds, $\sigma_{UTS} - \sigma_{YS} \simeq 100\,{\rm MPa}$, it follows that the strain hardening coefficient must be approximately constant for all
the welds considered. This in turn means that the uniform strain component of
the measured elongation of most ferritic steel welds must be about constant,
the total elongation being a function mostly of the non--uniform component
which occurs beyond necking during a tension test. Of course, the non--uniform
component of the elongation depends largely on void nucleation, growth and
coalescence so it is not surprising that the total elongation depends strongly
on inclusions. 

We note that this interpretation does not explain why the strain hardening
coefficient for welds is approximately constant whereas that of plates is not.
This remains an issue for further work.

A further consequence of these observations is that the ratio of the yield to
ultimate tensile strength will increase more rapidly for welds than for
plates, which may in turn have consequences on the fatigue properties. The
fatigue resistance is generally higher for materials where there is a large
difference between the yield and ultimate tensile strength. Indeed, \fagg\
shows that unlike plate steels, there is not much that can be done to
control the ratio
$\sigma_{YS}/\sigma_{UTS}$ by alloying.



\sec {CHARPY IMPACT TOUGHNESS MODEL}

This model was developed with 22 input variables (\tablaa), the nitrogen
concentration and the test temperature being the additional variables when
compared against the elongation model (\fagg). The test temperature is
expected to be an important variable because of the ductile--brittle transition
in ferritic iron, and the nitrogen concentration is known to have an
influence via strain hardening effects. Unfortunately, the Charpy data that
are available are not uniformly distributed (\fagg) because the tests are
frequently carried out at specified Charpy toughness values. 

The models were created from a dataset containing 3142 individual experiments
and the results are shown in \fagg. An optimum committee consisting of  around
62 of the best models was used in all subsequent analyses.

\bigskip
 \thicksize=0.7pt \thinsize=0.5pt
 \tablewidth=5.4truein {\smalltype \begintable
 {\bf Input element  }\hfill | {\bf Minimum  }\hfill |{\bf  Maximum  }\hfill | {\bf Mean  }\hfill | {\bf Standard Deviation } \cr
 Carbon     (wt\%)  \hfill | 0.022 \hfill | 0.19 \hfill | 0.07 \hfill | 0.0192 \nr
 Silicon    (wt\%) \hfill |  0.01 \hfill | 1.63 \hfill | 0.36 \hfill | 0.126 \nr
 Manganese  (wt\%) \hfill | 0.23 \hfill | 2.31 \hfill | 1.25 \hfill | 0.403 \nr
 Sulphur    (wt\%) \hfill | 0.002 \hfill | 0.14 \hfill | 0.008 \hfill | 0.008 \nr
 Phosphorus (wt\%) \hfill | 0.003 \hfill | 0.25 \hfill | 0.01 \hfill | 0.0134 \nr
 Nickel     (wt\%) \hfill | 0.0 \hfill | 5.58 \hfill | 0.366 \hfill | 1.012 \nr
 Chromium   (wt\%) \hfill |  0.0 \hfill | 11.8 \hfill | 0.453 \hfill | 1.387 \nr
 Molybdenum (wt\%) \hfill | 0.0 \hfill | 1.54 \hfill | 0.153 \hfill | 0.336 \nr
 Vanadium   (wt\%) \hfill | 0.0 \hfill | 0.53 \hfill | 0.0136\hfill | 0.0424 \nr
 Copper     (wt\%) \hfill | 0.0 \hfill | 2.18 \hfill | 0.0658 \hfill | 0.222 \nr
 Cobalt     (wt\%) \hfill | 0.0 \hfill | 0.016 \hfill | 0.0005 \hfill | 0.0023 \nr
 Tungsten   (wt\%) \hfill | 0.0 \hfill | 3.86 \hfill | 0.0076 \hfill | 0.1555 \nr
 Oxygen     (ppm wt.) \hfill | 63.0 \hfill | 1535 \hfill |  409 \hfill |  112 \nr
 Titanium   (ppm wt.) \hfill |  0.0 \hfill |  770 \hfill |   102 \hfill |  138 \nr
 Nitrogen     (ppm wt.) \hfill | 21.0 \hfill | 1000 \hfill |  96.5 \hfill |  63 \nr
 Boron      (ppm wt.) \hfill | 0.0 \hfill |  200 \hfill | 14.3 \hfill |   35 \nr
 Niobium    (ppm wt.) \hfill | 0.0 \hfill | 1770 \hfill |   40.55 \hfill |  139.6 \nr
 Heat input (kJ mm$^-$$^1$) \hfill | 0.6 \hfill | 6.6 \hfill | 1.194 \hfill | 0.69 \nr
 Interpass temperature ($^\circ$C) \hfill | 20 \hfill |  350 \hfill |  199.7 \hfill |   30 \nr
 Tempering temperature ($^\circ$C) \hfill | 20 \hfill |  760 \hfill |  182.5 \hfill |  261 \nr
 Tempering time  (h) \hfill | 0.0 \hfill |   100 \hfill | 2.2 \hfill | 5.66 \nr
 Testing temperature  ($^\circ$C) \hfill | -151 \hfill |   136 \hfill | -43.9 \hfill | 34.4 \cr
 Charpy toughness (J)\hfill |  2.6 \hfill | 300 \hfill |  74 \hfill |  43
 \endtable }
 {\tabtit{\tablee : } {The variables used in developing the Charpy  toughness
model.}}



\sec {APPLICATION TO C--Mn WELD METAL }

The reference values of the variables used in making predictions are listed in
\tablaa. \fagg\ shows the calculated variation in Charpy values  for
0\degg, as a function of the manganese and carbon concentrations. There are
two competing effects:

{\parindent 10pt \narrower \medskip

\zz{(i) }  At first an increase in hardenability leads
to a replacement of deleterious phases such as allotriomorphic and \wid\ by the
desirable acicular ferrite [85], leading to an increase in toughness. It is for
this reason that the peak in toughness occurs at a higher carbon concentration
when the manganese concentration is low. For equivalent hardenability, the
carbon concentration must be larger when that of manganese is small.

\zz{(ii) } The strength increases with the Mn and C concentration. In general,
an increase in strength leads to a deterioration in toughness because plastic
flow becomes more difficult, making cleavage cracking more probable. This
increase in strength may also be accompanied by the formation of undesirable
phases such as martensite. It follows that the toughness should eventually
begin to decrease as the carbon or manganese concentrations are increased.

\medskip}
\x Both of these effects are well illustrated by the computed data shown in
\figg. The microstructures for the welds described in \figg\   were calculated using a published physical model [88]. The \fagg\ shows that the above interpretations are correct in that, the initial raise in toughness corresponds to an improvement in the microstructure, with the subsequent decrease explained by the increase in strength. The \fagg\ shows clearly that in the context of charpy tests, there is always an optimum combination of manganese and carbon.

 \midinsert
 \vskip 0.5 in
  \thicksize=0.7pt \thinsize=0.5pt
 \tablewidth=3.0truein {\smalltype \begintable
 Carbon     (wt\%)  \hfill | 0.07 \nr
 Silicon    (wt\%) \hfill | 0.50 \nr
 Manganese  (wt\%) \hfill | 1.50 \nr
 Sulphur    (wt\%) \hfill | 0.006 \nr
 Phosphorus (wt\%) \hfill | 0.008 \nr
 Nickel     (wt\%) \hfill | 0.0 \nr
 Chromium   (wt\%) \hfill | 0.0  \nr
 Molybdenum (wt\%) \hfill | 0.0 \nr
 Vanadium   (wt\%) \hfill | 0.0 \nr
 Copper     (wt\%) \hfill | 0.0\nr
 Cobalt     (wt\%) \hfill | 0.0\nr
 Tungsten   (wt\%) \hfill | 0.0 \nr
 Oxygen     (ppm wt.) \hfill | 300 \nr
 Titanium   (ppm wt.) \hfill | 0.0 \nr
 Nitrogen     (ppm wt.) \hfill | 80 \nr
 Boron      (ppm wt.) \hfill | 0.0 \nr
 Niobium    (ppm wt.) \hfill | 0.0 \nr
 Heat input (kJ mm$^-$$^1$) \hfill | 1.00 \nr
 Interpass temperature ($^\circ$C) \hfill | 175 \nr
 Tempering temperature ($^\circ$C) \hfill | 20 \nr
 Tempering time  (h) \hfill |  0.0 \nr
 Testing temperature ($^\circ$C) \hfill | 0.0

 \endtable}
 \tabtit{\tablee : }{ The inputs relevant for a typical \lqq
carbon--manganese" weld metal.}
 \endinsert


Nickel is known to have an intrinsic beneficial effect on toughness by
increasing the work necessary to create cleavage cracks [89]. Thus, the toughness
at low temperatures is found to increase with the nickel concentration (\fagg
a);  however, the optimum concentration of nickel is found to depend
significantly on that of manganese. Higher concentrations of nickel are
beneficial only at low concentrations of manganese (\figg b) because both
elements enhance the hardenability and strength of the weld deposit.

As might be expected, \fagg a shows that the toughness at 0\degg\ decreases
with an increase in the oxygen concentration; oxides are sites for the
nucleation of cracks and voids. The toughness can nevertheless be optimised by
selecting the right manganese concentration, 0.7 wt\%\ in the case illustrated.
This is because low manganese concentrations lead to bad microstructures
whereas too high a concentration raises the weld strength. \figg b shows that
the toughness is maximised when the manganese to silicon ratio is about 2:1.
This may have something to do with deoxidation practice but the details are
not understood.



\sec {CONCLUSIONS}

It is possible to create reasonable neural network models for the tensile
elongation and Charpy impact energy properties of ferritic steel welds. The
models take into account the chemical composition, heat treatment and a
number of welding parameters. The models are based on large experimental
databases. However, significant deficiencies exist in the data which are not
uniformly distributed in the input space. Further work is needed to
systematically generate new data for a future analysis. 

 The models used for these predictions can be obtained
 freely from
 $$http://www.msm.cam.ac.uk/map/map.html$$


 \sec {ACKNOWLEDGEMENTS}

 The  authors are thankful to the to the Cambridge Commonwealth
 Trust for funding Ph.D. and to Kenneth Mitchell of National Power
 for further financial support. We would like to acknowledge Professor
 Alan Windle of the University of Cambridge for the provision of
 laboratory facilities.

 \sec{REFERENCES}

\def\ref#1#2#3#4#5#6#7{\hoffset=6.0mm \parindent=-6.0mm
\singlespace \rightskip=6.0mm \singlespace {#1.} #2: {\it
#4} {\bf #5} (#3) #6.\vskip 0.5truemm}

{\parskip=0mm \parindent=10pt \narrower





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\ref {20} {Evans, G. M.} {1978} {Oerlikon Schwei\ss mitteilungen 36} {82} {4--19} {Effect of Manganese on the Microstructure and Properties of All-Weld Metal Deposits.}

\ref {21} {Evans, G. M.} {1979} {Oerlikon Schwei\ss mitteilungen 37} {87} {17--31} {Effect of Interpass Temperature on the Microstructure and Properties of C-Mn All-Weld Metal Deposits.}

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\ref {23} {Evans, G. M.} {1982} {Oerlikon Schwei\ss mitteilungen 40} {100}
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\medskip}






 \topinsert
 \vskip 1.55 in
 \special{psfile=ElnC.eps hoffset=-58 voffset=-10 hscale=26 vscale=26}
 \special{psfile=ElnSi.eps hoffset=62 voffset=-10 hscale=26 vscale=26}
 \special{psfile=ElnMn.eps hoffset=180 voffset=-10 hscale=26 vscale=26}
 \special{psfile=ElnS.eps hoffset=305 voffset=-10 hscale=26 vscale=26}

 \vskip 1.55 in
 \special{psfile=ElnP.eps hoffset=-58 voffset=-14 hscale=26 vscale=26}
 \special{psfile=ElnNi.eps hoffset=60 voffset=-14 hscale=26 vscale=26}
 \special{psfile=ElnCr.eps hoffset=180 voffset=-14 hscale=26 vscale=26}
 \special{psfile=ElnMo.eps hoffset=305 voffset=-14 hscale=26 vscale=26}

 \vskip 1.55 in
 \special{psfile=ElnV.eps hoffset=-58 voffset=-14 hscale=26 vscale=26}
 \special{psfile=ElnCu.eps hoffset=60 voffset=-14 hscale=26 vscale=26}
 \special{psfile=ElnCo.eps hoffset=180 voffset=-14 hscale=26 vscale=26}
 \special{psfile=ElnW.eps hoffset=305 voffset=-14 hscale=26 vscale=26}

 \vskip 1.55 in
 \special{psfile=ElnTi.eps hoffset=-58 voffset=-14 hscale=26 vscale=26}
 \special{psfile=ElnB.eps hoffset=62 voffset=-14 hscale=26 vscale=26}
 \special{psfile=ElnNb.eps hoffset=190 voffset=-14 hscale=26 vscale=26}
 \special{psfile=ElnHI.eps hoffset=310 voffset=-14 hscale=26 vscale=26}

 \vskip 1.55 in
 \special{psfile=ElnIT.eps hoffset=-58 voffset=-14 hscale=26 vscale=26}
 \special{psfile=ElnPwht_T.eps hoffset=62 voffset=-14 hscale=26 vscale=26}
 \special{psfile=ElnPwht_t.eps hoffset=190 voffset=-14 hscale=26 vscale=26}
 \special{psfile=ElnO.eps hoffset=310 voffset=-14 hscale=26 vscale=26}
 { \tabtit{Fig. 1 : } {The database values of each variable versus the
 elongation.}}
 \endinsert

\topinsert
\vskip 2.9 in
\special{psfile=ELN_data_freq.eps hoffset=-10 voffset=-35 hscale=60 vscale=60}
 {\tabtit{Fig. 2 : }{The elongation data frequency distribution. }}
\endinsert


\topinsert
\vskip 2.3 in
\special{psfile=ElnHU_Sigma.eps hoffset=20 voffset=0 hscale=45 vscale=45}
\special{psfile=ElnHU_ener.eps hoffset=220 voffset=0 hscale=45 vscale=45}

\vskip 2.5 in

\special{psfile=Elncommittee.eps hoffset=0 voffset=-10 hscale=45 vscale=45}
\special{psfile=ElnOT_TRAIN.eps hoffset=210 voffset=-10 hscale=45 vscale=45}
\bigskip
\vskip 2.8 in

\special{psfile=ElnOT_PRED.eps hoffset=20 voffset=0 hscale=45 vscale=45}
\special{psfile=ElnCOM_RESULT.eps hoffset=210 voffset=-10 hscale=45 vscale=45}


 {\tabtit{Fig. 3 :  }{Characteristics of the elongation model. $\sigma_\nu$
is the model perceived level of noise in the elongation. (d) and (e)
represent the behaviour of the best single model, whereas (f) shows the
performance of the optimum committee model on the entire dataset.}}
\endinsert


\topinsert\vskip 3.5 in
\special{psfile=Eln_best5.eps hoffset=-20 voffset=-50 hscale=80 vscale=80}

{\tabtit{Fig. 4 : }{The significance ($\sigma_w$) of each of the
input variables, as perceived by the neural network, in influencing
the elongation. }}
 \endinsert

\topinsert
\vskip 2.2 in
\special{psfile=mod_pict/EC_0.5-1Mnpwht.eps hoffset=20 voffset=0 hscale=45 vscale=45}
\special{psfile=mod_pict/EMn_pwht.eps hoffset=220 voffset=0 hscale=45 vscale=45}

\vskip 2.5 in
\special{psfile=mod_pict/EC_Mn_alone.eps hoffset=-20 voffset=-550 hscale=80 vscale=90}
\special{psfile=mod_pict/EC_Mn_YS_alone.eps hoffset=220 voffset=-550 hscale=80 vscale=90}
{\tabtit{Fig. 5 :  }{ (a,b) Predicted elongation as a function of carbon and
manganese in carbon--manganese weld metal. (c,d) 
Contour plots showing the variation in elongation and
yield strength as a function of the carbon and manganese concentrations. The
error bars have been omitted for clarity but range from $\pm$2--6\%\ in
elongation and $\pm$10--30~MPa in the strength plots.}}
\endinsert

 \topinsert
\vskip 3.5 in
\special{psfile=mod_pict/voids.pss hoffset=40 voffset=-100 hscale=60 vscale=60}

{\tabtit{Fig. 6 : }{ An illustration 
of how a large density of void  nucleating particles can result
in fracture with a low  overall ductility, even though the
material fails by gross  plastic deformation on a microscopic
scale.}}
 \endinsert


\topinsert
\vskip 3.8 in
\special{psfile=EO_pwht.eps hoffset=0 voffset=0 hscale=45 vscale=45}
\special{psfile=mod_pict/ES_pwht.eps hoffset=225 voffset=0 hscale=45 vscale=45}

 {\tabtit{Fig. 7 :  }{Change in elongation in  carbon--manganese weld metal as
a function of (a) the oxygen concentration and (b) the sulphur concentration.
It is evident that sulphur does not have a significant effect on the ductility
of welds of the type considered in the present work.}}
\endinsert

\topinsert
\vskip 2.1 in
\special{psfile=plate_YS_UTS_R.eps hoffset=0 voffset=0 hscale=45 vscale=45}
\special{psfile=YS_UTS_RATIO.eps hoffset=213 voffset=-10 hscale=45 vscale=45}

\bigskip
\vskip 2.1 in
\special{psfile=plate_YS_UTS_el.eps hoffset=0 voffset=0 hscale=45 vscale=45}
\special{psfile=YS_UTS_Eln_RATIO.eps hoffset=213 voffset=-10 hscale=45 vscale=45}

 {\tabtit{Fig. 8 :  }{Plots of the ultimate tensile strength versus yield
strength for (a) hot--rolled C--Mn steel plates; (b) weld metals. 
 Plots of the elongation versus yield strength for (c) hot--rolled C--Mn steel plates; (d) weld metals. All of the data plotted are experiments, those for plates coming from previous work [85],
those for the weld deposits from the present analysis. }}



\vskip 2.5 in
\special{psfile=C_Mn_YS_UTS_ratio.eps hoffset=0 voffset=0 hscale=45 vscale=45}
\special{psfile=ys.uts2 hoffset=213 voffset=-170 hscale=45 vscale=45}
{\tabtit{Fig. 9 :  }{The effect of carbon, manganese on YS/UTS ratio on a) weld metal and b) steel plate [87].}}
\endinsert





 \topinsert
 \vskip 1.5 in
 \special{psfile=Chp_T.eps hoffset=80 voffset=-50 hscale=26 vscale=26}
 \special{psfile=ChpN.eps hoffset=220 voffset=-50 hscale=26 vscale=26}
 {\tabtit{Fig. 10 : }{The additional variables used in the Charpy toughness model.}}


\vskip 4.5 in
\special{psfile=CHP_data_frq_bar.eps hoffset=-10 voffset=0 hscale=80 vscale=80}

 {\tabtit{Fig. 11 : }{Frequency distribution for the Charpy toughness data.}}
\endinsert

\topinsert
\vskip 2.3 in
\special{psfile=ChpHU_Sigma.eps hoffset=20 voffset=0 hscale=45 vscale=45}
\special{psfile=ChpHU_ener.eps hoffset=220 voffset=0 hscale=45 vscale=45}

\vskip 2.5 in

\special{psfile=Chpcommittee.eps hoffset=0 voffset=-10 hscale=45 vscale=45}
\special{psfile=ChpOT_TRAIN.eps hoffset=210 voffset=-50 hscale=45 vscale=45}
\bigskip
\vskip 2.8 in

\special{psfile=ChpOT_PRED.eps hoffset=20 voffset=-20 hscale=45 vscale=45}
\special{psfile=ChpCOM_RESULT.eps hoffset=230 voffset=-10 hscale=45 vscale=45}


 {\tabtit{Fig. 12 :  }{Characteristics of the Charpy impact toughness model.
$\sigma_\nu$ is the model perceived level of noise in the toughness. (d) and
(e) represent the behaviour of the best single model, whereas (f) shows the
performance of the optimum committee model on the entire dataset.}}
\endinsert


\topinsert
\vskip 2.3 in
\special{psfile=CC_pwht_1.5.eps hoffset=20 voffset=0 hscale=45 vscale=45}
\special{psfile=CC_pwht_0.5.eps hoffset=220 voffset=0 hscale=45 vscale=45}

 {\tabtit{Fig. 13 :  }{Effect of carbon at 1.5 and 0.5  wt\% manganese,
0$^\circ$C, on the Charpy impact energy.}}
\vskip 4.5 in
\special{psfile=1.5Mn_YS_Chp_pwht.ps hoffset=-20 voffset=-30 hscale=45 vscale=45}
\special{psfile=0.5Mn_YS_Chp_pwht.ps hoffset=200 voffset=-30 hscale=45 vscale=45}

 {\tabtit{Fig. 14 :  }{Effect of carbon at 1.5 and 0.5  wt\% manganese at
on yield strength and the calculated microstructural fraction  using published physical model [88]. (allotriomorphic ferrite -- $\alpha$, widmanst$\ddot{a}$tten ferrite -- $\alpha  _{\it w}$, acicular ferrite~+~bainite -- $\alpha _{\it a}$, martensite -- $\alpha ^\prime$ and retained austenite -- $\gamma_{\it r}$ )}}
\endinsert


\topinsert


\vskip 3.5 in
\special{psfile=mod_pict/C_Mn_3d-0T_alone.eps hoffset=-45 voffset=-550 hscale=95 vscale=95}
\special{psfile=mod_pict/C_Mn_3d-20T_alone.eps hoffset=200 voffset=-550 hscale=95 vscale=95}

\smallskip
\vskip 3.4 in
\special{psfile=mod_pict/C_Mn_3d-40T_alone.eps hoffset=80 voffset=-550 hscale=95 vscale=95}

\smallskip
 {\tabtit{Fig. 15 : }{Predicted  variations in Charpy as a function of the
carbon and manganese concentrations and the test temperature. The error bars
have been omitted for clarity of presentation, but the trends have been confirmed to be significant.}}
\endinsert


\topinsert

\vskip 3.5 in
\special{psfile=mod_pict/Ni_T_0.5Mn_alone.eps hoffset=-45 voffset=-550 hscale=95 vscale=95}
\special{psfile=mod_pict/Mn_Ni-60_alone.eps hoffset=210 voffset=-550 hscale=95 vscale=95}

\vskip 1 cm
{\tabtit{Fig. 16 : }{The effect of nickel and manganese on the Charpy
toughness. (a) Nickel improves the toughness at low temperatures. (b) The
Optimum concentration of nickel depends on the manganese concentration. The
error bars have been omitted from these plots for clarity as before.}}
\endinsert

\topinsert
\vskip 3.5 in
\special{psfile=mod_pict/Mn_O-0_alone.eps hoffset=-45 voffset=-550 hscale=95 vscale=95}
\special{psfile=mod_pict/Mn_Si-20_alone.eps hoffset=210 voffset=-550 hscale=95 vscale=95}

\vskip 1 cm
{\tabtit{Fig. 17 : }{(a) The combined effect of manganese and oxygen on the
toughness at 0\degg. (b) The combined effect of manganese and silicon on the
toughness at 0\degg.}}
\endinsert



\vfil \eject
 \end



