Formation, Quantification and Significance of Delta ...

International Journal of Engineering Technology, Management and Applied Sciences

www.ijetmas.com September 2015, Volume 3, Special Issue, ISSN 2349-4476

23 Rati Saluja, K. M. Moeed

“Formation, Quantification and Significance of Delta Ferrite

for 300 Series Stainless Steel Weldments”

Rati Saluja1*, K. M. Moeed

2

1Department of Mechanical Engineering, Integral University, Lucknow, INDIA

2 Department of Mechanical Engineering, Integral University, Lucknow, INDIA

Abstract The ability to calculate the delta-ferrite content expressed in terms of Ferrite Number (FN) has proven very

valuable in assessing the performance and predicting microstructure of austenitic stainless steel. 300 series of austenitic

stainless steel is primarily monophasic at room temperature. These grades of steels generally solidify during welding as

a mixture of austenite and ferrite. During cooling of austenitic stainless steel, the ferrite almost fully transforms to

austenite, but there could be retention of delta ferrite in the weld metal. However, the variation in chemical composition

of weld metal directly affects on precipitation of delta ferrite. It significantly influences both the mechanical properties

and welding characteristics of the steel. This paper presents the formation of delta ferrite, significant of ferrite content on

weld microstructure and mechanical properties. This paper presents a chronological review from the first predictive

diagram as Strauss Maurer diagram, Schaeffler’s diagram, and Delong’s diagram, WRC's diagram up to the latest

mathematical model and measurement methods, for determining the content of delta ferrite in steel.

Keywords: Austenitic stainless steel, Constitution Diagrams, Delta ferrite, Ferritoscope, Neural Network.

1. Introduction

Achievements in steel refining technology and analysis techniques over the past decade have made it

possible to strictly control the chemical composition of Grade 300 austenitic stainless series. Minor changes in

the percentage of alloying elements and trace elements can noticeably affect performance, weldability,

machinability, corrosion resistance, and surface finish, several subgroup specifications have been developed

within the AISI specification [28]. The excellent corrosion resistance and high temperature strength of the

newest austenitic grade of 304 steel, places 300 series at the forefront of metallurgical technology [76]. While

solidification, the welded microstructure of austenitic stainless steel is either fully austenitic, or mixture of

austenite with little amount of ferrite along solidification grain and subgrain boundaries [43]. To control the

structure of 304 stainless steel weld deposits and amount of delta ferrite in weld is important because the

microstructure of the steel influences many of its properties [64]. Adverse effects of delta ferrite might include

increase in magnetic permeability of alloys containing ferrite, or reduction in impact strength during long-time

high-temperature service through an increase in the rate of sigma phase formation which leads to hot cracking

and embrittlement [24].

For designating the ferrite content of steel weld metal Ferrite Number (FN) is an arbitrary

standardized value. Several diagrams had been advanced to explain the formulation of ferrite number in weld.

The Ferrite Number approach was built up in order to minimize the huge variation in ferrite levels determined

on welds when measured using different techniques [23]. The ferrite level is important to assure minimum

exposure to solidification cracking when depositing austenitic stainless steel weld metal. To prevent weld

from corrosion resistance the lower ferrite number is essential, while balancing higher ferrite content to avoid

solidification cracking in the weld deposition [55]. Due to limitations in the recognizable methods, various

researchers had shown their scientific and technological interest towards the prediction and measurement of

ferrite number in austenitic stainless steel welds [70]. Study showed no significant differences in the content

of delta ferrite in relation to different methods of determining the delta ferrite [72].

The purpose of this review is to present a chronology of the different methods that researchers have

proposed for prediction of formation ferrite number with particular emphasis on formation and role of delta

ferrite on 300 series austenitic stainless steel weldments along with influence of phase transformations. It

includes predictive and measurement methods as well as merits and drawbacks of the presently used methods

are also considered.




2. Formation of ferrite When welded, 304 steel exhibit a wide range of microstructures, solidification behaviors and resulting

ferrite morphologies [26,40]. As welded microstructures contain mainly the skeletal ferrite morphology, some

regions solidified as primary ferrite while other regions solidified as primary austenite, in which case the

ferrite is a product of the eutectic reaction occurring during the last stages of solidification [22]. The liquidus

projection starts at the peritectic reaction on the Fe–Ni system and moves down to the

eutectic reaction on the Cr–Ni system in the Fe–Cr–Ni ternary system [17].

This eutectic ferrite is then in attendance along cell boundaries, unlike the skeletal and lathy ferrite

morphologies, in which the ferrite is contained primarily within the cell cores [13, 26]. The microstructure

of welds in this alloy can then be characterized by regions of both the eutectic ferrite and skeletal ferrite as of

Fig. 1[39]. The lathy ferrite and skeletal ferrite characteristic of welds characteristic of welds of theses alloy

are also revealed [9].

Figure 1 Pseudobinary section of the Fe–Cr–Ni ternary diagram at 70% Fe, showing solidification modes

[39], Figure 2 Schematic of solidification behavior and ferrite morphologies [9]

In cooling from the molten state austenitic stainless weld metal of normal carbon content solidifies

first as a mixture of delta ferrite and austenite, most of the ferrite subsequently transforms to austenite as the

deposit cools through a temperature range just below the delta ferrite region[10,51]. The ferrite does not

instantaneously transform, but does so gradually over a short period of time. Theoretically the transformation

could be avoided almost completely if the deposit could be instantaneously quenched from the just frozen

state to black heat, which would result in a much higher percentage of ferrite [52]. Practically, the final

amount of delta ferrite in virtually all weld metal depends only to a rather minor degree upon the cooling rate

[10,45].

3. Significant of ferrite content

The Welding Research Council (WRC) Subcommittee on Welding Stainless Steel adopted FN as its

value for measuring ferrite in 1973 [16], and its method for calibration is specified by the AWS A4.2 and ISO

8249 standards [9]. FN approximates the "volume percent ferrite"[12]. Minimum ferrite content at levels

below 8 FN is necessary to avoid hot cracking in stainless steel welds. Hot cracking in 304 austenitic stainless

steel is amplified by low-melting eutectics containing impurities such as S, P, Si, N. It could be diminished by

small increase in Carbon, Nitrogen, Chromium, Nickel, and Silicon or by substantial increase in Manganese

content [55]. The amount of ferrite in the weld metal also controls the micro structural evolution during high

temperature service, corrosion and stress corrosion resistance. The low temperature toughness of the weld

metal is also related to the weld metal ferrite content [44].

1. The hot cracking sensitivity gain increased as δ ferrite content is increasing and which has inverse

effect on, the ductility because of martensite formation and thus the potential for fracture increases [45].

2. The ductility of ferrite at high temperatures is greater than that of austenite, allowing relaxation of

thermal stresses [63].




3. The lower thermal expansion coefficient of ferrite as compared to austenite results in less contraction

stresses and fissuring tendency [42].

4. As the δ ferrite is usually controlled to prevent microcracks as well as refines the grain size of the

solidified metal, which results in better mechanical properties and cracking resistance in stainless steel welds

[43,66].

5. The higher solubility for impurity elements in δ ferrite leads to less interdendritic segregation [55].

6. The presence of ferrite results in a larger interface area due to the solid state transformation to

austenite that begins soon after solidification. The increased area disperses the concentration of impurity

elements at the grain boundaries [43].

7. The solidification temperature range of primary ferrite welds is less than that of primary austenite

solidified welds, providing a smaller critical temperature range for crack formation [63].

8. Coarse grain formation in the HAZ occurring by recrystallisation and grain growth in fully austenitic

metals increases susceptibility to liquation cracking, while ferrite forming compositions are not susceptible

[40].

9. The volume contraction associated with the ferrite-austenite transformation reduces tensile stresses

close to the crack tip, which decreases cracking tendency [8].

10. Coarse grain formation in the HAZ occurring by recrystallisation and grain growth in fully austenitic

metals increases susceptibility to liquation cracking, while ferrite forming compositions are not susceptible

[40].

4. Chronology of Predictive and Measurement Methods

Presented below is a chronological review of the different diagram and techniques that researchers

have proposed including predictive and measurement methods.

4.1 Determination of ferrite number by constitution diagram

Regarding the prediction of stainless steel weld metal, the austenitic-ferritic alloy systems accumulate the

most interest of all. This preference for the austenitic-ferritic systems began in 1920, when Strauss and Maurer

introduced a nickel-chromium diagram that allowed the prediction of various phases in the microstructure of

wrought, slowly cooled steels [67]. The design of the above diagram was used as a model for many diagrams

to follow. These include the Schaeffler diagram, DeLong diagram, WRC-1988 diagram, and WRC-1992

diagram. Delong diagram are widely used for quantifying the ferrite number because this diagram is

particularly designed for stainless steel welds containing minimal percentage of ferrite [39].

4.1.1 The Strauss-Maurer diagram

The Strauss-Maurer diagram was modified by Scherer et al. in 1939 with the addition of austenite-

ferrite stability lines [60]. Strauss and Maurer introduced a nickel-chromium diagram that allowed the

prediction of various phases in the microstructure of wrought, slowly cooled steels [67]. This revised diagram

uses the Strauss-Maurer axes that represent the actual chromium and nickel content. The left side of the

diagram contains the lines proposed by Strauss and Maurer, while the right side of the diagram is the

contribution of Scherer et al [60, 67].

Newell and Fleischmann recognizing that other elements besides chromium and nickel had an effect

on the microstructure; hence, they developed an expression for austenite stability on the Strauss-Maurer

diagram [50]. The Newell-Fleischmann equation for the austenite/austenite and ferrite boundary is as follows:

(1.1)

In 1943, Field, Bloom, and Linnert then Binder, Brown, and Franks as well as Thomas, also in 1949 ,

proposed similar equations to establish the boundary between austenite stability and the formation of delta

ferrite, but grouping in the same arm of the expression all the alloying elements that promoted the same

microstructural phase equation 1.2 and equation 1.3 respectively[7].

(1.2)

(1.3)




With the appearance of the above coefficients, much of the research on the development and construction of

constitution diagrams was centered on determining the coefficients of these formulas, termed as chromium

equivalent and nickel equivalent equations.

Figure 3 Strauss-Maurer nickel-chromium microstructure diagram as modified by Scherer et al. [60]

4.1.2 Schaeffler diagram

In 1946, Campbell and Thomas proposed the concept of chromium equivalent for the first time during

the microstructual study of welded alloy 25Cr-20Ni while adding small amounts of molybdenum and niobium

[25]. The concept of equivalent included the contribution of those alloying elements responsible for the

specific phase formation, as depicted in Equation 1.4.

Creq = Cr + 1.5 Mo + 2Nb (1.4)

Anton Schaeffler focused on the construction of a constitution diagram for weld metals that would

allow the prediction of weld metal microstructure based on the chemical composition [56]. Schaeffler diagram

contained Creq and Nieq formulas as Ferrite-promoting elements and austenite-promoting elements

respectively, for the axes, with ranges for the specific weld metal microstructural phases plotted in the

diagram [39]. The diagram was considerably accurate for most of the 300 series alloys of that time, using

conventional arc welding processes. Although nitrogen is known to be a strong austenite promoter, Schaeffler

did not include a nitrogen term in the nickel-equivalent equation, probably due to the difficulty in determining

the nitrogen content [7].

Nieq = Ni +0.5 Mn + 30C

Creq = Cr + 2.5Si +1.8 Mo + 2Nb (1.5)

In Figure 4, one of the first Schaeffler constitution diagrams, with the Strauss-Maurer lines, is presented.

Along with the first diagram, Schaeffler reported a new equation for the phase boundary between fully

austenitic alloys and alloys composed by austenite and ferrite. As depicted in the diagram, the microstructural

boundary between γ-austenite and the two phases -ferrite and - austenite is claimed to follow a second-

degree expression [6].

Nieq = (Creq -16)2 + 12 (1.6)

12

This equation implies curvature, due to the quadratic term, and that the lines on the Schaeffler

diagram are curved. In later studies in 1948, Schaeffler modified his diagram and the curved line of the

austenite/austenite + ferrite boundary became a straight line as displayed in figure 5 [57].

Figure 4 Schaeffler diagram of 1947, with the Maurer-Strauss curve , Figure 5 Schaeffler diagram of 1948, with linear boundaries [56,57]




The 1948 diagram increased the capability to quantitatively predict weld metal microstructure, adding

additional isoferrite lines in the two-phase austenite and ferrite region, while retaining the original equivalency

formulas [57]. In 1949, Shaeffler published the final version of his constitution diagram, which is still in use

today and is presented in Figure 6. The expression for the chromium equivalent calculation was modified,

decreasing the relative weight of molybdenum, silicon, and niobium compared with the equivalent initially

proposed. However, the microstructural phases observed and the percentage volume ferrite were still

illustrated [58].

Creq = Cr + Mo + 1.5Si + 0.5Nb (1.7)

This diagram was declared to give a universal precision of ± 4% volume ferrite, or ± 3 FN for 78% of

cases, and it has been widely used for ferrite prediction in welded stainless steels as well as for prediction of

microstructure in dissimilar welds once the characteristic percentage dilution due to the welding process is

recognized [39].

Figure 6 Schaeffler diagram of 1949, which is still in use [58]

4.1.3 DeLong Diagram

In 1956, instead of calculating the weld metal constitution for the entire composition range of stainless steels,

DeLong et al. introduced centered on 300 series austenitic stainless steels [13]. A part of their research was

the examination of nitrogen on the weld metal microstructure, for more accurate prediction of the ferrite

content in the stainless steel weld metal. Location of the lines in the diagram was significantly affected, due to

addition of nitrogen in the nickel equivalent. Slope of the isoferrite lines was increased due to the differences

that DeLong et al. found between the measured and calculated ferrite content on high-alloyed stainless steels

types (e.g. 316, 316L and 309), while keeping the spacing between isoferrite lines relatively constant [14].

He quantified the austenitizing effect of the nitrogen with a coefficient of 30 carbon in the expression

of nickel equivalent, while he considered valid the last Schaeffler’s expression for chromium equivalent [14].

Nieq = Ni +0.5 Mn + 30C

Creq = Cr + Mo + 1.5Si + 0.5Nb (1.8)

Figure 7 DeLong diagram of 1956 for austenitic stainless steels [14]




4.1.4 DeLong-WRC Diagram

The DeLong-WRC Diagram, was reported by Long and DeLong is fairly insensitive to the normal

range of heat input variations associated with arc welding[45]. The Subcommittee on Welding Stainless Steel

of the Welding Research Council initiated an effort in the mid-1980s to revise and expand the Schaeffler and

DeLong diagrams, in order to improve the accuracy of ferrite prediction in stainless steel weld metal. For

ferrite contents higher than 8 FN, DeLong harmonized the diagram with new experimental outcomes from

GTAW and GMAW welds in order to replace the ancient extrapolations he had established before. He

significantly altered slopes of the iso-ferrite lines to improve the ferrite prediction of higher alloyed stainless.

For 95% of cases, precision was claimed to be ± 3 FN for GMAW and GTAW and ± 4 FN for SMAW [18].

The Welding Research Council (WRC) Subcommittee on Welding Stainless Steel adopted FN as its value for

measuring ferrite in 1973 [27,70], and its method for calibration is specified by the AWS A4.2 and ISO 8249

standards. The introduction of Ferrite Number (FN) scale resulted due to the difficulty of measuring the ferrite

content quantitatively by volume in stainless steel welds [45]. The FN values are based on magnetic

measurements, since the BCC delta ferrite is ferromagnetic, while the FCC austenite is not. The FN values are

not intended to relate directly to percent ferrite, although at values below 10 they are considered to be similar

[3].

Figure 8 DeLong diagram of 1973, introducing the concept of Ferrite Number [13,14]

4.1.5 WRC-1988 and WRC-1992 Diagrams

In order to improve the accuracy of ferrite prediction in stainless steel weld metal, the subcommittee on

Welding Stainless Steel of the Welding Research Council initiated an attempt to revise and expand the

Schaeffler and DeLong diagrams. In 1988, in a study funded by WRC, Siewert et al. [49] proposed a new

predictive diagram, which covered an expanded range of compositions, from 0 to 100 FN, compared to the 0

to 18 FN range of the DeLong diagram. It was a result of an extremely large database of welds (approximately

950) gathered from electrode manufacturers, research institutes and the literature. WRC-1988 diagram also

included boundaries that defined the solidification modes [25].

New equivalency formulas were developed which removed the manganese coefficient from the nickel

equivalent, thereby eliminating the systematic overestimation of FN in highly alloyed weld metals. The WRC-

1988 equivalency formulas are given as:

Creq=Cr+Mo+0.7Nb

Nieq=Ni+35C+20N

(1.9)

Soon after, Lake proposed the addition of a copper coefficient with value from 0.25 to 0.30, in the Nickel-

equivalent formula [41]. Various researchers tracked Lake’s study and planned their estimations for the

copper coefficient, in order to add them in the Schaeffler and DeLong nickel-equivalents. In 1992, Kotecki,

using Lake’s data as a basis, proposed a coefficient of 0.25 for copper in the nickel-equivalent formula [29-

38]. Kotecki and Siewert also proposed a new diagram, which included the coefficient 0.25 for copper in the

nickel-equivalent formula [34]. The WRC-1992 diagram diagram has been widely accepted worldwide and

has replaced the DeLong diagram in the ASME code.

Nieq=Ni+35C+20N+0.25Cu (1.10)




The WRC-1992 diagram is presented in Figure 10. Whereas the extended axes of the diagram allow a

wide range of base and filler metal to be plotted, the FN prediction is valid only when the weld metal

composition falls within the iso-FN lines of the diagram [7]. At the present time, the WRC-1992 diagram is

the most reliable and most accurate for the prediction of Ferrite Number in the austenitic and duplex stainless

steel welds. The only shortcoming of the WRC-1992 may be the absence of a factor for titanium, which is a

potent carbide former, also a ferrite-promoting element in the absence of carbon. Titanium also influences the

phase balance by removing carbon from the matrix [39].

Figure 9 WRC-1988 diagram with solidification mode boundaries, Figure 10 The WRC-1992 diagram [39]

Also worthy of mention is the research carried out in 2007 by Anderson et al. regarding the influence of

molybdenum on ferrite content of stainless steel welds [2]. FN = –48.53 – 13.85 C + 12.73 Si + 1.16 Mn + 3.89Cr – 3.14 Ni + 4.60 Mo + 10.10 Cu – 20.36 N (1.11)

4.2 Development of mathematical model

The regression procedure was used for the development of mathematical model to predict ferrite

number. The Second order polynomial representing the response surface for ―k‖ factors is given by Eq.1.12

Y =bo + + Xj +εi (1.12)

Where

b0 = free term of the regression equation,

b1,b2, b3, b4, andb5 = linear terms,

b11, b22 , b33 , b44 , and b55 = quadratic terms,

b12, b13, b14, b15, b23, b24, b25, b34, b35 and b45 = interaction terms and

term ― ε‖ = error term.

The response function representing ferrite umber can be expressed as shown in equation 1.13

F=φ(θiu, V iu, Liu, Iiu, Qiu) + eu (1.13)

Where, φ = response surface, eu = residual, u = number of observations in the factorial experiment

and iu represents level of the ith factor in the uth observation. Box and Hunter proposed central composite

rotatable design for fitting a second-order response surface based on the criterion of rotatability. Standard

error ―yu‖ can be calculated at any point on the surface from the result of the experiment. The standard error

will be the function of the coordinates ―xi‖ at any point. In a rotatable design, the standard error of

the response ―F‖ is same for all points that are at the same distance from the center of the region [47,65].

The adequacy of the model is tested using the analysis of variance (ANOVA). As per the ANOVA technique,

the model can be considered to be adequate if the calculated value of F-ratio of the model should not exceed

the standard tabulated value of F-ratio for a desired level of confidence (95%) [52].

4.3 Neural Network In the 21st Century, a noteworthy improvement in the quantification of ferrite number has been the

development of artificial neural Networks [72]. The aim of the analysis is to model the ferrite number in

stainless steel welds as a function of composition and few physical properties. The neural network is a simple

combination of transfer functions and weights. The influence of the inputs on the output variable is together

with the transfer functions implicit in the values of the weights. The artificial neural network is a multivariable

nonlinear regression method that can identify complex relationships between variables that are difficult to




recognize by linear regression. The method is based on the interaction between three layers, as illustrated in

diagram [75]. The first layer is composed of input nodes depicting the concentration of each alloying element.

Secondly, there is a hidden layer with an adjustable number of nodes that have to be optimized in order to get

the best project in the result but without overloading the system of variables. The third layer is the output layer

containing one single node whose value is the predicted FN value [73].

Figure 11 FNN-1999 artificial neural network sketch [72].

For Vitek’s network, first layer was formed by 13 elements further achieved with six hidden nodes for

second layer. He proposed that delta ferrite at room temperature depends on different variables that are

interconnected as chemical composition, the solidification mode, and the cooling rate, which further affect the

solid-state transformation [74]. He developed two neural networks between 2000 and 2003, the FNN-1999,

which only considered the chemical compositions as input data, and the ORFN, which added the weld cooling

rate to the earlier mentioned parameters with application range in between 10–3.106 °C/s, 14.7–32% Cr, 4.6–

33.5% Ni, 0.008–0.2% C, 0.01–6.85% Mo, 0.35–12.7% Mn, 0.003–1.3% Si for ferrite number 0–131 FN[7].

That work led to the conclusion that ferrite number depends on two variables: the total alloying level

(Creq+Nieq) and the ratio Creq/Nieq. By using Hammar and Svensson’s equivalents as depicted in equation 1.14,

a general expression was proposed as equation 1.15 [48, 22].

Creq=Cr+1.37Mo

Nieq=Ni+0.31Mn +22C+14.2N (1.14)

In order to validate the new general expression the WRC database (Ref. 31), which contains 279

samples whose chemical compositions are within the range of the austenitic, was used. The predicted FN

values were then compared with the experimental FN provided by the database [7].

FN =54.22 – 126.26 (Creq + Nieq ) + [- 48.11 + 37.14(Creq + Nieq )] + [-0.23 +61.95(Creq + Nieq )] (1.15)

4.2.1The Bayesian neural network

Neural network in a Bayesian framework allows the calculation of error bars representing the

uncertainty in the fitting parameters. The method aimed that there are many functions that can be fitted into

uncertain regions of the input space, without unduly compromising the fit in adjacent regions, which are rich

in accurate data. Instead of calculating a unique set of weights, a probability distribution of a set of weights is

used to define the fitting uncertainty. The error bars, therefore, become large when data are sparse or locally

noisy [7].

The Bayesian framework employed consists of thirteen input nodes, xi, representing the thirteen composition

variables (e.g. C, Cr, Ni, Mo, N, Mn, Si, Fe, Cu, Ti, Nb, V and Co), a number of hidden nodes, hI, and one

output y ((ferrite Number). The single output represents the ferrite number. Both the input and output

variables were normalized within the range ± 0.5 as follows [71]

(1.16)

Where

= normalized value of ,

= maximum value of and




= minimal value of .

The outputs are calculated from the inputs as follows: linear functions of the inputs, xj multiplied by

the weights wij are operated on by a hyperbolic tangent transfer function so that each input contributes to

every hidden unit where N is the number of input variables.

(1.17)

The bias is designated and is analogous to the constant that appears in linear regression. The transfer from

the hidden units to the output is linear, and is given by the output y is therefore a non-linear function of xj, the

function usually selected being the hyperbolic tangent because of its flexibility.

y = i (1.18)

The network is completely described if the number of input nodes, output nodes and the

hidden units are known along with all the weights wij and biases i. These weights are determined by training

the network which involves the minimization of an objective function [71].

4.4 Measurement Methods

This section presents well-founded and currently used measurement techniques and predictive

methods for determining the δ-ferrite content in austenitic stainless steel weld metal.

4.1.1 Magnetic Determination

The magnetic determination method is proposed as the arbitrary, standard method of calibrating non-

destructive ferrite content measuring devices [54]. The WRC recommends magnetic determination techniques

based on attractive force and magnetic permeability. The method is based on the magnetic response of the

delta ferrite phase as against the nonmagnetic γ-austenite phase [3].

The main magnetic techniques currently in use are based on the attractive force (such as Magne-Gage

magnetic balance) and on the magnetic permeability (Fischer Feritscope equipment), while some earlier

techniques were based on magnetic saturation and the Mossbauer Effect [48]. The attractive force technique is

based on the force required to separate the ferromagnetic sample from a standardized permanent magnet in the

equipment [19]. The measurement of this force was correlated to the FN scale using calibration standards. The

classic example of this equipment (Magne Gage) is generally considered the reference tool of its type [3,7].

4.4.2 Metallographic Determination

Metallographic determination consists of doing a visual quantitative counting manually or

automatically of the number of subdivisions where the presence of ferrite is detected in a previously polished

and metallographically etched sample [3]. This method is based on the internationally accepted assumption

that the ferrite volumetric proportion is analogous to the proportion at the surface measured [4]. It is essential

that the selected area is microstructurally representative of the whole sample as ferrite distribution is not

homogeneous in the weld deposits. Other inconveniences are the difficulty in determining with accuracy the

counting when the ferrite morphology is too thin, such as in cases of eutectic or skeletal ferrite morphologies

[1]. A suitable field of application for quantitative metallography is the determination of the ferrite in the

heat affected zone (HAZ) of duplex welds, as this is so narrow that magnetic measurements do not give

accurate results due to the influence of contiguous materials [7].

4.4.3 X-ray Diffraction.

The technique is based on exposure of the stainless steel sample to a monochromatic X-radiation and

depending on the crystallographic structure of the phases present (BCC ferrite, FCC austenite) [61]. There will

be reflection peaks or ach phase whose intensity will be related to the concentration of each phase in the

sample the quantitative determination of ferrite in stainless steel welds has not been satisfactory, possibly

because the fine skeletal morphology of the ferrite and the compositional segregations between the dendrite

core and the matrix make the diffraction patterns diffuse. The equipment is expensive and only used in

laboratory studies [11, 21].

4.4.4 Electrochemical Determination.

This technique was proposed by Gill et al. in 1979. It is based on dissolving the γ-austenite phase and

keeping the ferrite phase passivated by exposing it to an electrolyte and a predetermined voltage; therefore,




the selective dissolution of austenite isolates the ferrite [20]. The undissolved ferrite was separated and

estimated gravimetrically. The estimation of the ferrite by this technique, when compared with optical and

magnetic methods, showed slightly lower values [7]. The limitations and inconveniences are related to the fact

that this is a destructive technique, and that it is necessary first to establish the austenite polarization diagram

for each alloy where the technique is applied, because the correct voltage is required to ensure the total

dissolution of the austenite [5]. It is also an extremely slow method, as it is said to take 40 hours to dissolve

the austenite in a 1mm thick sample with 3%-volume ferrite [68].

4.4.5 Ferritscope

In 1990, Elmer and Eagar had measured the -ferrite content in very small weld samples of mass 5

mg and less than 0.5 mm thickness from EBW and LBW stainless steels (cooled at around 104–105 °C/s). The

small dimensions of the samples would not allow a magnetic measurement or quantitative metallography;

therefore, the authors used a magnetometer with sample vibration and a technique based on saturation

magnetization of the ferrite [15, 16]. Ferritscope or Ferrite content meters are very durable and portable. It

offers full non-destructive measurement of many kinds of metal. Ferrite testing equipments can detect ferrite

in piping materials, welded cladding, and austenitic and duplex stainless steels. It is easy to measure the ferrite

content accurately when using the Ferritscope upon probe placement on the surface of the specimen; the

reading is displayed automatically and stored in the instrument [22]. When scanning the weld seam with the

probe positioned, the continuous readings are captured and stored. This provides a ferrite content profile along

the weld seam.

Figure 12 Ferritoscope [22]

A magnetic field generated by a coil begins to interact with the magnetic portions of the specimen.

The changes in the magnetic field induce a voltage proportional to the ferrite content in a second coil. This

voltage is then evaluated [7]. All magnetizable structure sections are measured i.e., in addition to delta ferrite

also strain-induced martensite, for example, or other ferritic phases. A specific advantage of the magnetic

induction method for measuring the ferrite content is that a sigma phase. A Fe-Cr precipitation, which has

formed due to excess ferrite content and unfavorable cooling conditions, is also recognized correctly as a non-

ferritic structural component. In comparison, erroneous interpretation of ferrite content is likely in a

metallographic section where a sigma phase is not easily distinguished from a ferritic structure [15,16] .

4. Conclusions

Determination of ferrite content in stainless steel weld deposits is a topic that has generated much

interest and challenged researchers from the early days of welding until today, which is the reason for the

development of such a variety of predictive and measurement methods since 1920[46].

It is also recognized that for the same combination of base material and consumable, differences in the

experimental values can also be found related to the specific welding procedure and parameters used [54].

Therefore, whatever FN value is allocated to a weld metal should be derived from an average obtained from

several measurements taken [59].

There are a few tools used for predicting delta ferrite content, like Strauss-Maurer, Schaeffler-

DeLong Diagram WRC Diagram as well as others methods like neural network, magnetic and

metallographic determination, electrochemical and X ray diffraction technique, Ferriteometer etc. The final

welding method has a significant influence on the delta ferrite content of weld material [53]. For example, the




chromium content can change in SAW depending on the specific flux used, or in SMAW, GTAW, or GMAW

the atmospheric nitrogen could be absorbed into the weld pool if the arc length is too long[61].

1) The effectiveness of the presence of a small but controlled amount of delta ferrite in preventing

cracking of austenitic stainless steel weld deposits is well known [39].

2) The amount and distribution of delta ferrite was strongly affected by the steel chemical composition,

but less affected by the cooling rate [15].

3) Despite their practical limitations, wherever it is possible, experimental measurements based on

magnetic determination are better than predictive methods, whose accuracy is mainly dependant on the

reliability of chemical composition. However, in those cases where the weld deposit is not available such as in

the early stage of projects where alternative welding consumables are being considered, then predictive

methods have their scope [54].

4) Whichever method is used the proportion of delta ferrite can vary within a few percents. It is very

important to known that the test made to measure ferrite delta of steel before welding is no so conclusive

because some ferrite is transformed to austenitic during hot working[59].

5) Each diagram has been obtained according to specific assumptions in terms of linear regressions, and

that variations in the chemical composition could also be attributed to the specific welding process and its

settings [7].

6) According to Vitek’s study, the FNN-1999 neural network is claimed to create a more accurate

prediction than the WRC-1992 diagram and the ORFN neural network. However, in the case of laser or high-

energy processes, which are related to high cooling rates, it is claimed the ORFN neural network makes the

most accurate predictions [74].

7) The neural network model is the most accurate composition only dependent FN prediction method

currently reported in literature with predicted RMS error less than 2. This model is 65% more accurate than

the WRC – 1992 diagram and 40% more accurate than the other neural network model reported in the

literature [75].

Table 1 Comparison of root mean square (RMS) errors for different Ferrite Number prediction methods (265

datasets)[74] FN Prediction method RMS error for complete

training database

RMS error for independents

dataset not used in training

Bayesian Neural Network (BNN) model 2.1 2.03

FNN-1999 (Back Propagation Neural

Network) model

3.5 2.3

WRC-1992 Diagram 5.8 2.6

Function Fit model 5.6 5.1

8) All the literature indicates that a content of delta ferrite of max 8% in austenitic stainless steels weld is

accepted without problems moreover decreases the cracking susceptibility of weld material and improve the

cracking resistance. In proportion greater than 10 %, delta ferrite is more can be harmful to the welded area

due to the transformation of ferrite to sigma phase which is a specific transformation of the steel alloyed with

chromium.

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