A STATISTICAL INSIGHT OVER HAZ HARDNESS IN MMA WELDING

3rd International Conference on Welding Technologies and Exhibition (ICWETrsquo14) 21-23 May 2014 Manisa-TURKEY

A STATISTICAL INSIGHT OVER HAZ HARDNESS IN MMA WELDING

M Bodea a L Bracircnduşan b R Mureşan c

Technical University of Cluj Faculty of Materials Engineering and Environmental Sciencea mbodeastmutclujro b liviubrandusanstaffutclujro c rmuresanstmutclujro

Abstract

Mechanical properties of the welded materials play an important role in designing and exploitation of the welded metallic structures like civil buildings offshore platforms or different machines and equipments etc A major indicator of the weld quality and mechanical strength it is represented by the HAZ hardness In this paper we have studied the influence of a large number of welding parameters and material characteristics over the HAZ hardness and microstructure associated with those mechanical properties We have presented a new and robust mathematic model to estimate the hardness in HAZ for the MMA welding process Also we have proposed very sensitive coefficients that takes in account the smallest materials chemical influence like O2 content in ppm or others different elements that shows significative influence over the mechanical properties in the weld and microstructure as well The empirical data over 1600 runs ensure a large amount of data that was correlated and analyzed in order to establish the main factors that can affect the quality and mechanical properties of the welds

Key Words HAZ hardness microstructure modeling inclusions

1 Introduction

The aim of the present study is to determine if the proposed theoretical model for HAZ hardness and microstructure estimation is in correlation with a large amount of experimental data from a database developed in the Materials Algorithms Project

The Materials Algorithms Project (MAP) originated from a joint project of the University of Cambridge and the National Physical Laboratory and it serves as a centre for the validation and distribution of algorithms of use in the modeling of materials in the context of materials science and metallurgy Validation in this context means that effort has been expended to check that the program reproduces the example output from the example inputs that there is a reasonable level of documentation and that appropriate references are provided [1]

The mechanical properties of the welded joint are based on the parent and filler material characteristics but also on the welding process parameters Therefore the mathematical model must consider both initial categories of data materials data and process parameters data in order to produce accurate predictions based on the input data This is prove to be a very difficult task because of the very large amount of initial data that has to be considered but also due to very complex way in which interaction between those parameters occurs Finally is important to mention that measuring experimental data would lead natural to scattered values which in turn can produce significative errors that start from initial small deviations which grow with each level of calculus For example oxygen content can vary from 200 to 500 ppm in our data


It seems so negligible but it has a strong influence over the acicular ferrite formation which is a first level where scattered values for oxygen produce some errors over the acicular ferrite estimation Further those small errors would produce bigger errors in hardness estimation that represent a second level of calculus If we have to estimate UTS property or Charpy values based on the hardness values we go up to the third level estimation where the errors grow bigger That is why we try to adopt a model that is developed horizontally instead to serial sequences based model where the errors are amplified with each step

The heat source is moving in respect to the parent material with the welding speed vs that would produce a thermal field in the material that is not changing for an observer that is moving with the heat source The heat flow and the temperature distribution in the weld together with the cooling rates especially between 800-500 ordmC there are the main factors that will shape the HAZ width and hardness the amount and types of the transformation phases that occurs in the cooling stage of the weld

The net heat input Hnet [Jm-1] represent an important process parameter that has a great influence on the heat flow during welding eq1 [2]

(1)

where Qj [J] represent the amount of heat that enters to the joint U [V] and I [A] are welding current parameters f1 is a coefficient that takes in account the heat transfer efficiency from the electrode tip to the joint and f2 is a melting efficiency factor that takes into account the heat losses in the base material due to conduction convection and radiation and vs is the travel speed [msec-1]

High peak temperature in the weld would lead to the coarsening of the γ grains size that in turn would increase the hardenability of the HAZ of the weld To counteract that effect were developed new steels for welding by micro alloying additions like Nb V Ti Al which form with N precipitate particles that prevent the grain coarsening at high temperatures Other way to promote the nucleation of the acicular ferrite within coarse γ grains is by using non metallic inclusions like Ti2O3 that are more effective than other undissolved inclusions [3]

The model proposed by us is using the 4 Logistic function for estimating primary ferrite acicular ferrite and by difference the ferrite with second phases Carbon equivalent is considered the main variable the effect of micro alloying additions with Nb V Ti Al and oxygen content is quantified in the model by using some corrections applied over carbon equivalent formula as is presented in the diagram from Fig1

The cooling rate is dependent on the heat input plate thickness and plate initial temperatures respectively on the preheating conditions The cooling rate R [ordmCsec] is maximum at the weld centerline and because at the fusion boundary is only a few percent lower consequently the equation for the cooling rate could be applied for the entire weld and the HAZ However we should take into account the critical plate thickness tc which is dimensionless variable given in eq (2) over which the crossover between the 2-D and 3-D conditions of heat flows takes place Thus we would have two set equations for the cooling rate accordingly to the critical plate thickness eq (3) and (4) [23]


(2)

For thick welded plates that requires several passes to complete the joint (t c 075) the cooling rate R [ordmCsec-1] is given by eq(3) where Tc [ordmC] represent the temperature at which cooling rate R is calculated [2-3]

(3)

For thin plates (tc lt 075) the cooling rate is given by eq(4) where K is thermal conductivity of the parent metal [J(m sordmC)-1] t is the plate thickness [m] and ρc is the specific heat per unit volume [J m-3 ordmC-1]

(4)

The cooling rates between 800-500 ordmC t8-5 are calculated also based on the heat flows regime For 2-D conditions of heat flow t8-5 is given in eq(5) while for 3-D conditions of heat flow t8-

5 is given in eq(6) [4]

(5)

(6)

Fig1 The working diagram for the proposed mathematical model

DATABASE IMPORT TO EXCEL FROM ASCII FILE welddbdata

CALCULATING Ceq

CALCULATING PRIMARY FERRITE USING 4PL

CALCULATING ACICULAR FERRITE USING 4PL

CALCULATING FERRITE WITH 2ND PHASES

Micro alloying additions CORRECTIONS

Nb Ti Al V

ESTIMATION OF HAZ HARDNESS or UTS USING 5PL FUNCTION


For estimation of microstructure phases within the weld we have used a 4PL function for primary ferrite respectively for acicular ferrite and the ferrite with second phases was calculated by difference up to 100 total phases In eq (7) is given the general formula for a 4PL function that it completely described by a parameter vector p [min max c b]

The coefficient min from the parameter vector p represent the lowest values that function F(Ceqp) will reach asymptotically In the same manner max represent the upper horizontally asymptote for the 4PL function The coefficient b stands for Hill slope and the coefficient c represent the concentration of carbon equivalent for which the 4PL function is at the inflection point as is presented in the Fig2

(7)

a)b)

Fig 2 Parameter vector p[min maxcb] influence for 4PL function shape in phase volume fraction estimation vs cooling rates between 800-500 ordmC t8-5

2 Experimental

The experimental data is structured in several tables available at Materials Algorithms Project web site [1] that was used as input data for neural network analysis The file weldtoughnessdata and the file welddbdata contains the chemical composition of the steels studied and their room temperature mechanical properties These data have been collated from available literature The presence of an `N indicates that the value was not reported in the publication This is not meant to be an indication that the value is zero but unfortunately we can not use those records in our mathematical model because we have unknown data that can introduce uncontrollable errors

The input data consist in chemical compositions in 21 columns informations on welding process current parameters polarity I U the heat input interpass temperature post weld heat treatment temperature and post weld heat treatment time The output data consist on microstructure type and ratio and also on some mechanical properties of the weld hardness and Charpy values YS and UTS


3Results and Discussion

Analyzing the diagram presented in the Fig 3 it can be observed that is no apparent correlation between any micro structural phase and the carbon content in the welded material However we observe that between primary ferrite and the ferrite with 2nd phases seems to be a correlation the volume fraction of the ferrite with 2nd phases being proportional in a some degree with the volume fraction of the primary ferrite The mean value for the acicular volume fraction for all welded samples is 535 with a 226 standard deviation

Obtaining high values for acicular ferrite is desirable in order to obtain good mechanical properties with an equilibrate balance between UTS and Charpy values for the welded joints

Fig3 Microstructure volume fraction vs carbon content

The volume fraction of acicular ferrite in the weld is correlated with the inclusions content especially O2 and Ti but also Nb and V

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Primary ferrite 4logistic Primary ferrite -Experimental

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Ceq corrections(TiO2 Ni Cr V)

a) b)

Fig4 Primary ferrite 4PL estimation vs experimental data


Analysing the diagrams presented in Fig 4 a) we observe that the 4 logistic function for primary ferrite estimation offers a relatively good model for fitting experimental data with a correlation coefficient R higher than 07 However this estimation can be improved further as can be seen in the Fig 4 b) when a correction over carbon equivalent is made based on the O2 Ni Cr and V content The parameter vector for the 4PL function in the Fig 4 is p[0 05 027 5] or min =0 max=05 c=027 and Hill slope b=5 according to eq (7)

The correction formula utilized for carbon equivalent it was

(8)

where O2 and Ti are given in ppm and the other elements in wt

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Acicular Ferrite - Experimental4L Acicular

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Ceq correction (Ni V O2 Ti) 4L rec

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Fig5 Acicular ferrite 4PL estimation vs experimental data

The same 4 logistic function can be used with a different parameter vector p[5 90 025 -5] to estimate the volume fraction of the acicular ferrite in the weld microstructure as can be seen in the Fig 5a) Also the correction on the carbon equivalent based on the same elements used in the correction of the primary ferrite estimation lead to a better accuracy of the mathematic model that shows a better correlation with experimental data ilustrated in the Fig 5 b)

The correction coefficients for carbon equivalent used in eq (8) were calculated using the error sum of squares for estimated and experimental values of both microstructural phases and are given in the Table 1 Practically they have very close values and we can consider the mean of the values obtained in an single equation for both microstructural phases This correlation of the correction coefficients for both microstructural phases it shows that using a modified formula for equivalent carbon lead to a better correlation between experimental and modeled data

Table 1 The correction coefficients used for microstructure estimation using 4PL function

Microstructure KCr KNi KV KO2 KTi

Primary ferrite 005 -01 05 06 03

Acicular ferrite 00 -03 08 07 04


Either reduction of oxygen under 120 ppm or Ti under 100 ppm would have an negative impact on acicular ferrite volume fraction Higher values for oxygen especially would lead to decrease in toughness of the welded material So a proper balance between oxygen and titanium is required as well as for Nb V and B content

However we have still a significantly amount of experimental data that show some deviations from the mathematical mode That can be explained in several modes The chemical analysis of the welded material it has an statistical character so we can expect to have a distribution of the elements within the different parts of the welded joint Even small range variation of some elements can have great impact on the microstructure phases ratio with direct influence over mechanical properties hardness Charpy or UTS Another source for scattering experimental data it is represented by the welding process parameters that affect especially the thermic cycle during the weld The welding speed the variation of the distance between the parent material and the electrode tip any change in the heat input or cooling speed would introduce variation in the welded material microstructure with the consequences described earlier

The hardness in the weld can be expressed also in terms of 4PL function vs carbon equivalent over which the same corrections can be applied The experimental data is fitting the mathematical model with the same degree of correlation R The results are shown in the Fig 6 The hardness is increasing with the carbon equivalent but it has a saturation behaviour depending on the inclusions content and the volume fraction of the ferrite with second phases

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Fig 6 The estimation of hardness vs carbon equivalent for the population samples in

MMA welding

Fig 7 The UTS and YS vs carbon equivalent for the population samples in MMA welding

The mechanical properties UTS and YS vs carbon equivalent of the welded samples shown an increasing variation with the equivalent carbon content as can be seen in the Fig7 and like in the microstructural phases it present a high noise level that can be reduced if we consider a correction over equivalent carbon based especially on the impurities elements present in the material

The Charpy tests shows that the absorbed energy is highly dependent on the oxygen content and impurities segregations at the former austenite grains The thermal cycle it has also a very strong influence over this mechanical property


4 Conclusion

The prediction acuracy of the mechanical properties of the weld and microstructural volume fraction for each phase are direct related with the number of levels implied in the mathematical model The output errors present a behavior accordingly to the butterfly effect from chaos theory From this point of view is more convenable to devellop mathematical models that estimate directly the output data from primary input data instead to calculate intermediate variables which are used further for modelling the output data

This behavior is especially important in case of the inclusions especially for oxygen but also for micro alloying elements like Nb Ti Al and V Even smallest variation of these elements can change the output data in very large proportions

The chemical composition of the parentfiller material it is a key factor in designing a particular microstructure in the weld but it does not provide enough data to predict accurate results only from this point of view We have to consider the thermal cycle together with chemical composition in order to obtain better predictions In this respect we have to made corrections on the chemical composition represented by equivalent carbon formula from inclusions and microalloying point of view but also from the net heat input and cooling time t8-5 point of view

Even so the output values present a scattered distribution because of the local variations of the variables involved in the welding process For example the welding speed it was considered constant but because is a manual process that is hardly possible That would lead to variation of the thermal cycle in the process which in turn would result in different ratio and microstructure characteristics with obvious modifications in mechanical properties of the welded joint

Based on the chemical composition of the parentfiller material we have shown that the allotriomorphic ferrite but also the acicular ferrite volume fraction can be predicted using a four logistic formula The accuracy of this model can be improved if we perform corrections of the equivalent carbon based on the O2 Ti V and Al content in the weld

Also the hardness in the HAZ in the MMA welding can be predicted using a five logistic function based on the cooling time t8-5 The same mathematic model it is valid if we consider the equivalent carbon variable but this must be corrected due to the inclusion and micro alloying content as well as for thermal cycle influence

5 References

1 Materials Algorithms Project httpwwwjwriosaka-uacjpmapbackghtmlaim

2 Ibrahim Khan Welding Science and Technology New Age International Ltd Publishers ISBN 978-81-224-2621-5 (2008)

3 Sindo Kou Welding Metallurgy 2nd Ed John Wiley amp Son Inc ISBN 0-471-43491-4 (2003)

4 K Poorhaydari BM Patchett DG Ivey Estimation of Cooling Rate in the Welding of Plates with Intermediate Thickness Supplement to the Welding Journal 2005

5 HKDH Bhadeshia LE Svensson Mathematical Modelling of Weld Phenomena Inst Of Materials London pp109-182 (1993)

6 ASM Handbook Volume 6 Welding Brazing and Soldering ISBN 0-87170-377-7(V1) ASM International (1993)


CORRESPONDENCE ADDRESS

BODEA MARIUS Faculty of Materials Engineering and Environmental Science Bd Muncii 103-105 Cluj-Napoca Postal Code 400641 Cluj-Napoca Romacircnia

Telephone +40729123754

E-mail address mbodeastmutclujro

SHORT BIOGRAPHIES

BODEA MARIUS ndash born in 1968 PhD from 2005 Lecturer at the Faculty of Materials Engineering and Environmental Science 2 years experience in production in Morocco in metallic construction 2 years in an material research institute and 16 years in university education where he has published over 30 articles in scientific journals or proceedings at international conferences on powder metallurgy advanced materials or weldings

BRAcircNDUŞAN LIVIU ndashage 60 holds a position as professor at Technical University of Cluj Professional background in the field of the project 32 years of experience in the field of powder metallurgy 168 Works developed andor published 10 ISI 6 published in journals included in the indexed databases and other in national and international magazines or proceedings of conferences in the field 8 professional books out of which 5 in the development of materials their processing and powders metallurgy

MUREŞAN RADU ndash he graduated the Faculty of Mechanical Engineering in 1986 at the Polytechnic Institute of Cluj Metallurgy specialization and in 2001 he received his PhD in Materials Science He published over 40 scientific papers in scientific international journals he participated also in over 20 national and international research contracts He was project director in several contracts with manufacturers in the military strategic applications The researches that he conducted were in the heavy alloys field obtained via powder metallurgy technology Most of the applications were in the military and aeronautics area


4 K Poorhaydari BM Patchett DG Ivey Estimation of Cooling Rate in the Welding of Plates with Intermediate Thickness Welding Journal (2005) pp 149-155

httpswwwawsorgwjsupplement10-2005-POORHAYDARI-spdf

5 HKDH Bhadeshia LE Svensson Modelling the Evolution of Microstructure in Steel Weld Metal Mathematical Modelling of Weld Phenomena Vol2 Inst of Materials London (1993) pp109-182

httpwwwmsmcamacukphase-trans2005Grazpdf


It seems so negligible but it has a strong influence over the acicular ferrite formation which is a first level where scattered values for oxygen produce some errors over the acicular ferrite estimation Further those small errors would produce bigger errors in hardness estimation that represent a second level of calculus If we have to estimate UTS property or Charpy values based on the hardness values we go up to the third level estimation where the errors grow bigger That is why we try to adopt a model that is developed horizontally instead to serial sequences based model where the errors are amplified with each step

The heat source is moving in respect to the parent material with the welding speed vs that would produce a thermal field in the material that is not changing for an observer that is moving with the heat source The heat flow and the temperature distribution in the weld together with the cooling rates especially between 800-500 ordmC there are the main factors that will shape the HAZ width and hardness the amount and types of the transformation phases that occurs in the cooling stage of the weld

The net heat input Hnet [Jm-1] represent an important process parameter that has a great influence on the heat flow during welding eq1 [2]

(1)

where Qj [J] represent the amount of heat that enters to the joint U [V] and I [A] are welding current parameters f1 is a coefficient that takes in account the heat transfer efficiency from the electrode tip to the joint and f2 is a melting efficiency factor that takes into account the heat losses in the base material due to conduction convection and radiation and vs is the travel speed [msec-1]

High peak temperature in the weld would lead to the coarsening of the γ grains size that in turn would increase the hardenability of the HAZ of the weld To counteract that effect were developed new steels for welding by micro alloying additions like Nb V Ti Al which form with N precipitate particles that prevent the grain coarsening at high temperatures Other way to promote the nucleation of the acicular ferrite within coarse γ grains is by using non metallic inclusions like Ti2O3 that are more effective than other undissolved inclusions [3]

The model proposed by us is using the 4 Logistic function for estimating primary ferrite acicular ferrite and by difference the ferrite with second phases Carbon equivalent is considered the main variable the effect of micro alloying additions with Nb V Ti Al and oxygen content is quantified in the model by using some corrections applied over carbon equivalent formula as is presented in the diagram from Fig1

The cooling rate is dependent on the heat input plate thickness and plate initial temperatures respectively on the preheating conditions The cooling rate R [ordmCsec] is maximum at the weld centerline and because at the fusion boundary is only a few percent lower consequently the equation for the cooling rate could be applied for the entire weld and the HAZ However we should take into account the critical plate thickness tc which is dimensionless variable given in eq (2) over which the crossover between the 2-D and 3-D conditions of heat flows takes place Thus we would have two set equations for the cooling rate accordingly to the critical plate thickness eq (3) and (4) [23]


(2)


(3)


(4)



(5)

(6)



CALCULATING Ceq





Nb Ti Al V





(7)

a)b)


2 Experimental









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4L Primary ferrite


a) b)





(8)


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4 Conclusion







5 References












SHORT BIOGRAPHIES










(2)


(3)


(4)



(5)

(6)



CALCULATING Ceq





Nb Ti Al V





(7)

a)b)


2 Experimental









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a) b)





(8)


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4 Conclusion







5 References












SHORT BIOGRAPHIES












(7)

a)b)


2 Experimental









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(8)


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4 Conclusion







5 References












SHORT BIOGRAPHIES















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(8)


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4 Conclusion







5 References












SHORT BIOGRAPHIES












(8)


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MMA welding





4 Conclusion







5 References












SHORT BIOGRAPHIES













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MMA welding





4 Conclusion







5 References












SHORT BIOGRAPHIES










4 Conclusion







5 References












SHORT BIOGRAPHIES














SHORT BIOGRAPHIES














Documents

A STATISTICAL INSIGHT OVER HAZ HARDNESS IN MMA WELDING