Svoboda | Graniru | BBC Russia | Golosameriki | Facebook
Next Article in Journal
Treatment of Oily Effluents Using a Bacterial Cellulose Membrane as the Filter Bed
Previous Article in Journal
Developing Lead Compounds of eEF2K Inhibitors Using Ligand–Receptor Complex Structures
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Processes
Volume 12
Issue 7
10.3390/pr12071541
processes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Optimizing Elemental Transfer Predictions in Submerged Arc Welding via CALPHAD Technology under Varying Heat Inputs: A Case Study into SiO2-Bearing Flux

by
Jun Fan
1,†,
Jin Zhang
1,2,*,† and
Dan Zhang
1
1
School of Mechanical and Electrical Engineering, Suqian University, Suqian 223800, China
2
School of Metallurgy, Northeastern University, Shenyang 110819, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Processes 2024, 12(7), 1541; https://doi.org/10.3390/pr12071541
Submission received: 1 July 2024 / Revised: 18 July 2024 / Accepted: 20 July 2024 / Published: 22 July 2024
(This article belongs to the Section Materials Processes)
Browse Figures
Versions Notes

Abstract

:
With the advancement of the manufacturing industry, performing submerged arc welding subject to varying welding heat inputs has become essential. However, traditional thermodynamic models are insufficient for predicting the effect of welding heat input on elemental transfer behavior. This study aims to develop a model via CALPHAD technology to predict the influence of heat input on essential elements such as O, Si, and Mn when typical SiO2-bearing fluxes are employed. The predicted data demonstrate that the proposed model effectively forecasts changes in elemental transfer behavior induced by varying welding heat inputs. Furthermore, the study discusses the thermodynamic factors affecting elemental transfer behavior under different heat inputs, supported by both measured compositions and thermodynamic data. These insights may provide theoretical and technical support for flux design, welding material matching, and composition prediction under various heat input conditions subject to submerged arc welding processes when SiO2-bearing fluxes are employed.

1. Introduction

Since its introduction to industrial production, submerged arc welding (SAW) has have been applied widely in the fields of manufacture [1,2]. Due to its exceptional welding quality and high deposition rate, SAW holds particular significance in heavy industries [3,4].
SAW is widely recognized as an extremely high-temperature and multiphase metallurgical system, including metal, flux (slag), and arc plasma, where various chemical reactions occur among different phases [1,2,5]. During such a process, the molten weld pool, arc cavity, and slag are shielded by the flux, creating a significant “black box” characteristic for the entire system [2]. Such a characteristic hinders researchers in further exploring the metallurgical process of SAW [6].
Achieving equilibrium, except in small volumes, is impossible, even at the elevated temperatures [7]. This is due to high temperature and density gradients, the coexistence of different phases, the presence of large electric currents, and significant radiative energy transferred from the arc [8]. Although deviations from equilibrium are expected, equilibrium principles can still be applied to analyze the chemical interactions and mechanisms in the arc welding process, since elevated temperatures and high surface-to-volume ratio help to offset the limited reaction time [9,10,11].
It is widely acknowledged that controlling the weld metal (WM) composition is pivotal for SAW, as it fundamentally governs the service performance of the weldment [3,12]. However, within the field of chemical metallurgy, the prediction of WM compositions predominantly relies on theories derived from steelmaking [13,14]. Tuliani et al. [15] initially applied the concept of the basicity index (BI) from steelmaking to SAW and developed an empirical model to predict O content in WM, based upon the BI value [15]. Subsequently, Chai et al. [16,17] integrated the BI model with equilibrium constants of chemical reactions involving Si, Mn, and O, thereby developing the “slag-metal equilibrium” model. This model is based on steelmaking theory and predicts Si and Mn contents in metal using the estimated O content via the BI. However, this model does not consider the effect of gas formation on compositions [18]. Later on, researchers updated the “slag-metal equilibrium” model to “gas–slag-metal equilibrium” model by incorporating the consideration of the gas phase via CALPHAD technology [18]. However, the “gas–slag-metal equilibrium” model neglects the cross-zone character and the impact of heat input on the elemental transfer behaviors [19].
In recent years, SAW has been widely adopted to improve welding efficiency and the elevated heat input poses challenges to the control for the WM composition [19,20]. As such, this study aims to evaluate the influence of heat input on the transfer behaviors and compositions of essential elements, including O, Si, and Mn, in the metal subjected to the SAW process from a thermodynamic standpoint. Within this framework, a model has been developed by employing the concept of “effective equilibrium temperature” (EET) subject to both droplet and weld pool zones via CALPHAD technology. Then, based on the model data, the thermodynamic factor that governs the transfer behavior of O, Si, and Mn are discussed, which may provide theoretical and technical support for flux design, welding material matching, and composition prediction under different heat inputs in SAW when SiO2-bearing fluxes are employed.

2. Materials and Methods

The study focuses on the CaF2-Na2O-SiO2 system (a typical fluoride-basic agglomerated flux) [7]. Regarding the flux design, CaF2 serves as a diluent, with the SiO2 content ranging from 10 to 50 wt % [14]. The formulated flux compositions are summarized in Table 1.
For each agglomerated-flux formulation, reagent-grade powders are initially dry-mixed. These powders are then bonded using a sodium silicate solution. The resulting mixture is pelletized and dried in a muffle furnace at 700 °C for 3 h to eliminate the water content from the sodium silicate, thereby allowing the powders to bond into solid particles [12]. This procedure introduces Na2O into the flux, which helps stabilize the arc plasma [12]. To determine the flux compositions, X-ray fluorescence (XRF) analysis was utilized [20]. The analytical flux compositions are summarized in Table 2.
The base metal (BM) used for the experiment was a typical low-alloy grade steel; the compositions of BM and electrode have been given in Table 3 [20]. Bead-on-plate double-electrode single-pass SAW was performed with heat inputs of 60 kJ/cm and 20 kJ/cm, respectively; more information on welding and materials are detailed elsewhere [20].

3. Assumptions and Modeling

3.1. Scientific Assumptions

As mentioned earlier, attaining equilibrium, except for confined spaces, is highly unlikely even at the elevated temperatures characteristic of the SAW. To address such issues, the concept of EET has been employed to facilitate thermodynamic calculations [9,10,21]. EET implies the temperature at which the experimentally determined mass-action index coincides with the equilibrium constant, rather than the actual measured temperature [13,16,17].
In prior study, the influence of heat input on elemental transfer behavior was assessed by adjusting the EET within the gas–slag-metal equilibrium model; the study reveals that considering the gas–slag-metal equilibrium, utilizing only the EET of 2000 or 2100 °C, may be insufficient to accurately constrain the transfer behavior of Mn during the SAW process [22]. In this study, the previously proposed “cross-zone model” is integrated to consider EETs in both droplet reaction and weld-pool reaction zones. The reaction temperatures in the droplet zone and weld-pool zone are denoted as EET-D and EET-W, respectively.
The process of model development is illustrated in Figure 1. CALPHAD (Computer Coupling of Phase Diagrams and Thermochemistry) is an approach that creates models to depict the thermodynamic properties of different phases, enabling the prediction of properties in multicomponent systems from those of simple binary and ternary systems [23]. While direct measurement of thermodynamic data during arc welding is infeasible due to the extreme temperatures, the thermodynamic models employed in the CALPHAD method have demonstrated reliability in acquiring reliable thermodynamic data [18].
FactSage is employed to perform the modeling and calculation. FactSage integrates databases, calculation modules, and an interactive interface to perform the study of chemical reactions, phase equilibria, and thermochemical modeling [23].
Within this study, Equilib Module is applied to perform the equilibrium calculations, the details of which are summarized in Section 3.2. During the modeling, FactPS, Fstel, and FToxide are employed, as shown by Figure 1. FactPS is a database containing thermodynamic data for pure substances. Fstel is a database offering thermodynamic data for steel and its related alloys. FToxide contains thermodynamic data for various oxide systems [18,23]. In this work, the FactPS database is utilized to simulate gas formation, the Fstel database to simulate metals, and the FToxide database to simulate the slag.
Based on previous study, the range of EET-W is 1700 to 2100 °C [22]. Considering this EET-W range and the thermodynamic model of the weld-pool zone, the effect of heat input on elemental transfer behavior was evaluated [22]. However, such evaluation only considers the thermodynamic equilibrium within the weld-pool zone, neglecting the cross-zone characters of element transfer behavior in SAW, especially the significant O uptake in the droplet. In this study, the EET of the molten-droplet zone is combined with the recently proposed “cross-zone model” to develop thermodynamic models for predicting the effect of heat input on elemental transfer behavior [24].
Then, the EET of the droplet-reaction zone, referred to as EET-D, is poised to be determined. Based on previous studies, the temperature range for EET-D is set between 2300 and 2500 °C [1,7,18].
The reaction zones that control the metal compositions are illustrated in Figure 2. Based on scientific hypotheses, the temperatures for EET-D and EET-W are set to determine the impact of heat input on the transfer behavior of essential elements, namely O, Si, and Mn. In the modeling process, the Equilib module is utilized to predict the metal composition using EET as the input. For the droplet reaction, EET-D is set as the input temperature (Figure 2b), and for the weld-pool reaction, EET-W is set as the input temperature (Figure 2c).

3.2. Modeling Processes

3.2.1. Droplet Zone (Figure 2b) [24]

  • Step 1: Database selection and phase simulation.
    • The databases chosen for the model include FToxid, Fstel, and FactPS.
    • These databases were set up within the Equilib module for phase simulation purposes.
  • Step 2: Setting the equilibrium temperature for the SAW process.
    • For accurate modeling of the SAW process, the equilibrium temperature was set at 2300 and 2500 °C, which corresponds to boundary the EET of the arc plasma.
    • The input metal compositions were obtained from the BM chemistries.
To estimate the O concentration in the droplet, an equilibrium calculation was performed using Fe and O as the input metal compositions. The PO2 value from Table 4 was used for this calculation. Table 4 shows the O concentration in molten droplets, along with the corresponding equilibrium partial pressure of O2.

3.2.2. Weld-Pool Zone

As illustrated in Figure 2c, the arc plasma induces the evaporation of Si and Mn prior to the weld pool being covered by the slag. Since Si and Mn are essential dioxides for the weld pool, the evaporation of these elements must be considered. It is well known that the high temperatures generated by the arc plasma can lead to the evaporation of certain alloy elements, such as Si and Mn [2]. Currently, the evaporation mechanisms underlying such evaporation are not yet fully understood [25]. Previous attempts by researchers to predict the level of evaporation using thermodynamic methods have resulted in significant errors [25]. In this study, Zhu’s models, which are based on the Langmuir Equation and have shown good predictive accuracy, are employed to estimate the loss of Mn and Si at the plasma–metal interface; Equations (1) and (2) illustrate Zhu’s models, where η represents the mass percentage and m denotes the nominal compositions (considering only the dilution effect of the metal) of Mn and Si [24,26].
η Mn = 49.04 − 0.23 m Mn − 0.49 m Si
η Si = 38.55 − 0.33 m Mn − 0.58 m Si
According to previous research, the dilution value was set to 0.5 [16,17]. Utilizing Equations (1) and (2), along with the droplet O content from Table 5, the nominal compositions were calculated and set as the input stream for the model, subject to the weld-pool zone. Then, the gas–slag-metal equilibrium model was applied to predict the compositions of submerged arc-welded metal. The Equilib module was utilized to conduct gas–slag-metal equilibrium calculations [18]:
  • The FToxid, Fstel, and FactPS databases were selected for this study. Solution phases from ASlag-liq (all oxides), S (FToxid-SLAGA), and LIQUID (FStel-Liqu) were chosen to model the molten-slag and steel phases.
  • Temperatures were set as 1700 and 2100 °C.
  • Nominal compositions were used as the input metal chemistries, and the flux compositions from Table 2 were utilized as the flux compositional input.

3.2.3. Quantization of Elemental Transfer

Using Equation (3), the transfer behaviors of O, Si, and Mn were calculated. In Equation (3), MWM represents the measured composition of the WM, while MN represents the nominal composition [1].
Δ = M WM − M N
As to Δ, a positive Δ indicates an elemental gain induced by the flux, whereas a negative Δ indicates an elemental loss induced by the flux. The level of Δ represents the extent of elemental transfer [18]. The simulated value of ΔO, ΔSi, and ΔMn have been summarized in Table 6.

4. Results and Discussion

4.1. Transfer Behavior of O

The control for O content is essential, as it significantly affects the mechanical properties of the weldment [27]. Excessive O may decrease toughness and reduce hardenability, while submerged arc-welded metal with low O levels exhibits poor mechanical properties, particularly the toughness, due to the lack of inclusions for promoting the formation of acicular ferrite (AF) [28].
The transfer behavior of O under low and high heat input has been plotted in Figure 3 as a function of SiO2 content in the flux. In Figure 3, blue markers represent the values of ΔO under low heat input, while red markers represent the values of ΔO under high heat input. The star-shaped markers denote the predicted ΔO, and the circular markers denote the actual ΔO.
By observing Figure 3, for the actual values of ΔO, the value of ΔO shows an upward trend with the increase in heat input. The predicted ΔO values also increase with the increase in heat input, as shown by the star-shaped markers in Figure 3. Additionally, all ΔO values are positive, indicating that the model accurately predicted the direction of O transfer.
As mentioned earlier, the O content is an essential factor affecting the mechanical properties of the WM, with the flux being the primary determinant of the O potential of the overall SAW process [29]. However, the SAW process exhibits black-box characteristics; that is, during the SAW process, the molten weld pool and the arc cavity are covered by flux, making it impossible to make an experimental analysis [2]. Therefore, researchers have proposed hypotheses to analyze the thermodynamic factors that govern the flux O potential. Indacochea et al. [8,30] and Lau et al. [27,31] propose that the primary factor that determines the O potential during SAW is the partial pressure of O2. This is because, during the SAW process, the high temperature of the arc plasma induces the decomposition of oxides in the flux, releasing O2 and elevating the O content in the hot metal [14]. To verify such a hypothesis, the equilibrium O2 partial pressure predicted by the model is plotted in Figure 4.
As shown in Figure 4, the partial pressure of O2 in the arc cavity increases with the improvement in heat input. According to previous studies, for SiO2-bearing flux, SiO2 tends to decompose and release O2 under the influence of the arc cavity, via Reaction (4) [25].
( SiO 2 ) = SiO ( g ) + 1 2 O 2 ( g )
Therefore, for the same flux, i.e., with the same SiO2 content, the increase in heat input promotes the decomposition of SiO2. This phenomenon is consistent with the results of previous studies [27,31]. Meanwhile, for the O content in the droplets, high heat input also promotes an increase in its content (see Table 4 and Table 5). Thus, whether in the droplet zone or the weld-pool zone, this model is able to predict the impact of heat input on O transfer behavior.

4.2. Transfer Behavior of Si

It is well-known that Si is an essential element in WM that must be carefully controlled, as high Si concentrations may lead to a decrease in both elongation and toughness [17]. Early research on SAW revealed that an increase in heat input is usually accompanied by an increase in WM Si content [32]. However, despite the development of thermodynamic equilibrium models, such as the slag-metal and gas–slag-metal models, researchers have been unable to determine the effect of heat input on the transfer behavior of Si by thermodynamic mechanism [18].
Figure 5 illustrates the quantified transfer behavior of the Si under different heat inputs. The red markers represent the predicted and actual ΔSi at high heat input, while the blue markers represent the predicted and actual ΔSi at low heat input. As shown in Figure 5, the predicted and actual ΔSi are both positive, indicating that the model is able to predict the transfer direction of the Si element, i.e., from the flux to the WM.
Previous research has predicted the content and transfer behavior of Si via a slag-metal equilibrium model based on Reaction (5) at the slag-metal interface, which governs the transfer behaviors of Si in SAW [16,17].
  ( SiO 2 ) = [ Si ] + 2 [ O ]
[ pct   Si ] = α SiO 2 73 . 6 ⋅ [ pct   O ] 2
In Equation (6), αSiO2 represents the activity of SiO2, and [%O] represents the O content predicted by the flux BI model. However, in the slag-metal equilibrium model, αSiO2 typically represents the activity value of SiO2 in the flux (slag) at 2000 °C, and the BI model does not consider the effects of EET and heat input of SAW [15]. Therefore, the model proposed in this study provides a new approach for predicting the change in transfer behavior of Si elements subject to different heat inputs.

4.3. Transfer Behavior of Mn

Mn is an important element for promoting the formation of AF [28,33]. AF’s interlocking structure and fine grain size provide significant resistance to crack propagation [34]. Previous studies have shown that increasing the Mn content from 0.6 to 1.8 wt % is able to progressively enhance the AF formation in WM [28,33].
Figure 6 shows the effect of heat input on the transfer behavior of the Mn element, where Figure 6a represents the actual effect of heat input on the transfer behavior of Mn, and Figure 6b represents the predicted effect of heat input on the transfer behavior of Mn. Since the initial flux does not contain MnO, as shown by Table 2, the Mn element will be lost into the slag by shifting the Reaction (7) to the left side, which can be reflected by the green shaded areas in Figure 6a,b.
  ( MnO ) = [ Mn ] + [ O ]
The traditional thermodynamic model, on the other hand, predicts the transfer behavior of the Mn via Equation (8) based upon Reaction (7) at the slag-metal interface [16,17].
[ pct   Mn ] = α MnO 0 . 86 · [ pct   O ]
In Equation (8), αMnO represents MnO activity subject to the initial flux [16,17]. In this study, since the initial flux does not contain MnO, the transfer behavior of Mn cannot be predicted by the traditional model. Although the subsequently proposed gas–slag-metal equilibrium model is able to predict the transfer behavior of Mn when no MnO is contained in the initial flux, it is unable to predict the effect of heat input on the transfer behavior of the Mn [35]. In this study, by combining the definition of EET and the cross-zone thermodynamic model, the effect of heat input on the transfer behavior of Mn is predicted. As shown in Figure 6, with the increase in heat input, the level of Mn loss from the weld pool to the slag decreases.
It should be noted that, although the proposed model in this study is able to predict the effect of welding heat input on the transfer behavior of the O, Si, and Mn elements, there are still some errors, due to the following reasons:
  • During the SAW process, achieving complete thermodynamic equilibrium is not feasible, leading to potential errors.
  • In addition to thermodynamic factors, physical factors such as slag entrapment during the SAW process may affect the accuracy of composition predictions.
  • The concept of EET is currently not fully developed. More data are necessary to establish a comprehensive model for EET and welding heat input to further enhance prediction accuracy.
In recent years, with the development of processes, there is a need to conduct SAW under different heat inputs. For the manufacture of steel materials in SAW, the transfer behavior of O, Si, and Mn needs to be strictly controlled, as their contents are essential in determining the metallurgical properties of the weldment [18]. From such a perspective, the significance of this study in material design and matching for SAW lies in the following facts:
  • This model can predict the impact of welding heat input on the transfer behaviors of O, Si, and Mn elements, which may guide the design of flux formulations, enabling their optimization under varying welding heat inputs.
  • This model may help in matching the flux, for instance, as the heat input increases, more O and Si tend to transfer from the flux to the WM. As such, it is advisable to use BMs and electrodes with lower O and Si contents for high-heat-input SAW.

5. Conclusions

This study proposes a model for predicting the transfer behavior of O, Si, and Mn elements under different heat inputs by defining the EET and utilizing cross-zone thermodynamic equilibrium models via CALPHAD technology. Using a typical SiO2-bearing flux, specifically the CaF2-SiO2 system, as an example, this study predicts the impact of heat input on the transfer behavior of O, Si, and Mn elements and discusses the thermodynamic factors influencing the transfer behaviors. The conclusions of this study are as follows:
  • The proposed model can predict the flux O potential under different heat inputs. The predicted data demonstrate that the flux O potential increases with higher heat input. Thermodynamic analysis indicates that such enhancement is induced by the rise in the equilibrium partial pressure of O2.
  • With increasing heat input, a higher level of Si is transferred from the flux into the metal, as predicted by the proposed model in this study.
  • Due to the absence of MnO in the initial flux, Mn is lost from the weld pool. Traditional slag-metal equilibrium thermodynamic models are unable to predict such a transfer direction, whereas the model proposed in this study is capable of forecasting the transfer behavior of Mn elements under such conditions. Moreover, as heat input increases, the loss of Mn decreases, which can be predicted by the proposed model.
However, it should be noted that while this study can predict the effect of heat input on the transfer behavior of O, Si, and Mn, there is still room for improvement in prediction accuracy. Specifically, further optimization of the model is required, particularly in establishing the relationship between EET and heat input. This will be a focus of future work.

Author Contributions

Conceptualization, J.Z., J.F. and D.Z.; funding acquisition, J.F. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported by the National Natural Science Foundation of China (No. 50474085), the Initial Fund of Suqian University (No. 2022XRC040), Suqian Science & Technology Project (No. K202239).

Data Availability Statement

The data presented in this study are available in article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Natalie, C.A.; Olson, D.L.; Blander, M. Physical and Chemical Behavior of Welding Fluxes. Annu. Rev. Mater. Sci. 1986, 16, 389–413. [Google Scholar] [CrossRef]
  2. Sengupta, V.; Havrylov, D.; Mendez, P. Physical Phenomena in the Weld Zone of Submerged Arc Welding—A Review. Weld. J. 2019, 98, 283–313. [Google Scholar]
  3. Xu, K.; Fang, T.; Zhao, L.; Cui, H.; Lu, F. Effect of Trace Element on Microstructure and Fracture Toughness of Weld Metal. Acta Metall. Sin. Engl. Lett. 2020, 33, 425–436. [Google Scholar] [CrossRef]
  4. Sharma, L.; Chhibber, R.; Kumar, V.; Khan, W.N. Element Transfer Investigations on Silica Based Submerged Arc Welding Fluxes. Silicon 2023, 15, 305–319. [Google Scholar] [CrossRef]
  5. Liu, Z.-K. Thermodynamics and its prediction and CALPHAD modeling: Review, state of the art, and perspectives. Calphad 2023, 82, 102580. [Google Scholar] [CrossRef]
  6. Cho, D.-W.; Song, W.-H.; Cho, M.-H.; Na, S.-J. Analysis of submerged arc welding process by three-dimensional computational fluid dynamics simulations. J. Mater. Process. Technol. 2013, 213, 2278–2291. [Google Scholar] [CrossRef]
  7. Olson, D.; Liu, S.; Frost, R.; Edwards, G.; Fleming, D. Nature and Behavior of Fluxes Used for Welding; ASM Handbook; ASM International: Cleveland, OH, USA, 1993; Volume 6, pp. 55–63. [Google Scholar]
  8. Indacochea, J.E.; Blander, M.; Christensen, N.; Olson, D.L. Chemical Reactions During Submerged Arc Welding with FeO-MnO-SiO2 Fluxes. Metall. Mater. Trans. B 1985, 16, 237–245. [Google Scholar] [CrossRef]
  9. Mitra, U.; Eagar, T. Slag-metal Reactions During Welding: Part II. Theory. Metall. Trans. B 1991, 22, 73–81. [Google Scholar] [CrossRef]
  10. Mitra, U.; Eagar, T. Slag-metal Reactions during Welding: Part I. Evaluation and Reassessment of Existing Theories. Metall. Trans. B 1991, 22, 65–71. [Google Scholar] [CrossRef]
  11. Liby, A.; Dixon, R.; Olson, D. Welding: Theory and Practice; Elsevier Science Publishers B. V: Amsterdam, The Netherlands, 1990; Volume 389. [Google Scholar]
  12. Singh, B.; Khan, Z.A.; Siddiquee, A.N.; Maheshwari, S. Experimental Study on Effect of Flux Composition on Element Transfer During Submerged Arc Welding. Weld. J. 2018, 43, 26. [Google Scholar] [CrossRef]
  13. Chai, C.; Eagar, T. Prediction of Weld-metal Composition during Flux-shielded Welding. J. Mater. Energy Syst. 1983, 5, 160–164. [Google Scholar] [CrossRef]
  14. Chai, C.; Eagar, T. Slag Metal Reactions in Binary CaF2-Metal Oxide Welding Fluxes. Weld. J. 1982, 61, 229–232. [Google Scholar]
  15. Tuliani, S.; Boniszewski, T.; Eaton, N. Notch Toughness of Commercial Submerged Arc Weld Metal. Weld. Met. Fabr. 1969, 37, 327–339. [Google Scholar]
  16. Chai, C.; Eagar, T. Slag-metal Equilibrium during Submerged Arc Welding. Metall. Trans. B 1981, 12, 539–547. [Google Scholar] [CrossRef]
  17. Chai, C.-S. Slag-metal Reactions during Flux Shielded Arc Welding. Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, MA, USA, 1980. [Google Scholar]
  18. Zhang, J.; Shao, G.; Fan, J.; Wang, L.; Zhang, D. A Review on Parallel Development of Flux Design and Thermodynamics Subject to Submerged Arc Welding. Processes 2022, 10, 2305. [Google Scholar] [CrossRef]
  19. Ghosh, A.; Chattopadhyaya, S.; Das, R. Effect of Heat Input on Submerged Arc Welded Plates. Procedia Eng. 2011, 10, 2791–2796. [Google Scholar] [CrossRef]
  20. Zhang, D.; Zhang, J.; Yang, S.; Shao, G.; Liu, Z. Element Transfer Behavior for CaF2-Na2O-SiO2 Agglomerated Flux Subject in Submerged Arc Welding Process. Processes 2022, 10, 1847. [Google Scholar] [CrossRef]
  21. Mitra, U.; Eagar, T. Slag-metal Reactions during Welding: Part III. Verification of the Theory. Metall. Trans. B 1991, 22, 83–100. [Google Scholar] [CrossRef]
  22. Zhang, D.; Shao, G.; Zhang, J.; Liu, Z. On the Moving of Neutral Point for Mn Subject to Submerged Arc Welding under Various Heat Inputs: Case Study into CaF2-SiO2-Na2O-MnO Agglomerated Fluxes. Processes 2022, 10, 1888. [Google Scholar] [CrossRef]
  23. Bale, C.W.; Bélisle, E.; Chartrand, P.; Decterov, S.; Eriksson, G.; Gheribi, A.; Hack, K.; Jung, I.-H.; Kang, Y.-B.; Melançon, J. Reprint of: FactSage thermochemical software and databases, 2010–2016. Calphad 2016, 55, 1–19. [Google Scholar] [CrossRef]
  24. Zhang, J.; Liu, P.; Zhang, D. Advancing Manganese Content Prediction in Submerged Arc Welded Metal: Development of a Multi-Zone Model via the Calphad Technique. Processes 2023, 11, 1265. [Google Scholar] [CrossRef]
  25. Kou, S. Welding Metallurgy, 3rd ed.; JohnWiley & Sons, Inc.: Hoboken, NJ, USA, 2003; pp. 22–122. [Google Scholar]
  26. Zhu, J.; Wang, Y.; Shi, H.; Huang, L.; Mao, Z. Element Loss Behavior and Compensation in Additive Manufacturing of Memory Alloys. Trans. China Weld. Inst. 2022, 43, 50–55. [Google Scholar]
  27. Lau, T.; Weatherly, G.; McLean, A. The sources of oxygen and nitrogen contamination in submerged arc welding using CaO-Al2O3 based fluxes. Weld. J. 1985, 64, 343–347. [Google Scholar]
  28. Mills, A.; Thewlis, G.; Whiteman, J. Nature of Inclusions in Steel Weld Metals and Their Influence on Formation of Acicular Ferrite. Mater. Sci. Technol. 1987, 3, 1051–1061. [Google Scholar] [CrossRef]
  29. Eagar, T. Sources of Weld Metal Oxygen Contamination during Submerged Arc Welding. Weld. J. 1978, 57, 76–80. [Google Scholar]
  30. Indacochea, J.; Olson, D. Relationship of Weld-Metal Microstructure and Penetration to Weld-Metal Oxygen Content. J. Mater. Energy Syst. 1983, 5, 139–148. [Google Scholar] [CrossRef]
  31. Lau, T.; Weatherly, G.; McLean, A. Gas/metal/slag Reactions in Submerged Arc Welding Using CaO-Al2O3 Based Fluxes. Weld. J. 1986, 65, 31–38. [Google Scholar]
  32. Jackson, C. Flux and Slag in Welding; Welding Research Council: New York, NY, USA, 1973; No. 190. [Google Scholar]
  33. Ricks, R.; Howell, P.; Barritte, G. The Nature of Acicular Ferrite in HSLA Steel Weld Metals. J. Mater. Sci. 1982, 17, 732–740. [Google Scholar] [CrossRef]
  34. Loder, D.; Michelic, S.K.; Mayerhofer, A.; Bernhard, C. On the Capability of Nonmetallic Inclusions to Act as Nuclei for Acicular Ferrite in Different Steel Grades. Metall. Mater. Trans. B 2017, 48, 1992–2006. [Google Scholar] [CrossRef]
  35. Zhang, J.; Wang, L. Upgrading the Prediction Model for Mn Content in Submerged Arc Welded Metal via CALPHAD Technology: Case Study into Typical Acidic and Basic Fluxes. Ceram. Int. 2022, 49, 6573–6579. [Google Scholar] [CrossRef]
Processes 12 01541 g001
Figure 1. Modeling process diagram.
Figure 1. Modeling process diagram.
Processes 12 01541 g001
Processes 12 01541 g002
Figure 2. Zones influencing WM compositions: (a) comprehensive schematic of SAW process, (b) chemical reactions occurring in the droplet zone, (c) chemical reactions in the weld pool and solidification zones [24].
Figure 2. Zones influencing WM compositions: (a) comprehensive schematic of SAW process, (b) chemical reactions occurring in the droplet zone, (c) chemical reactions in the weld pool and solidification zones [24].
Processes 12 01541 g002
Processes 12 01541 g003
Figure 3. Predicted and actual data subject to the impact of heat input of the transfer behavior of O.
Figure 3. Predicted and actual data subject to the impact of heat input of the transfer behavior of O.
Processes 12 01541 g003
Processes 12 01541 g004
Figure 4. Predicted equilibrium O2 partial pressure subject to different heat inputs.
Figure 4. Predicted equilibrium O2 partial pressure subject to different heat inputs.
Processes 12 01541 g004
Processes 12 01541 g005
Figure 5. Predicted and real data subject to the impact of heat input on the transfer behavior of Si: (a) the actual Si transfer behavior, (b) the predicted Si transfer behavior.
Figure 5. Predicted and real data subject to the impact of heat input on the transfer behavior of Si: (a) the actual Si transfer behavior, (b) the predicted Si transfer behavior.
Processes 12 01541 g005
Processes 12 01541 g006
Figure 6. Predicted and real data subject to the impact of heat input on the transfer behavior of Mn: (a) the actual Mn transfer behavior, (b) the predicted Mn transfer behavior.
Figure 6. Predicted and real data subject to the impact of heat input on the transfer behavior of Mn: (a) the actual Mn transfer behavior, (b) the predicted Mn transfer behavior.
Processes 12 01541 g006
Table 1. Designed flux formula (wt %) [20].
Table 1. Designed flux formula (wt %) [20].
FluxCaF2SiO2
F-19010
F-28020
F-37030
F-46040
F-55050
Table 2. Measured Flux Composition (wt %) [20].
Table 2. Measured Flux Composition (wt %) [20].
FluxCaF2Na2OSiO2
F-188.771.2110.02
F-278.871.2419.89
F-368.671.2330.10
F-458.621.1940.19
F-548.841.2249.94
Table 3. Measured chemical compositions of BM and electrode (wt %) [20].
Table 3. Measured chemical compositions of BM and electrode (wt %) [20].
CSiMnTiCrO
BM0.1120.1421.5400.0150.0180.003
Electrode0.1270.0491.6500.0150.0150.003
Table 4. Equilibrium partial pressure of O2 subject to the droplet zone of SAW (atm.).
Table 4. Equilibrium partial pressure of O2 subject to the droplet zone of SAW (atm.).
Flux2300 °C2500 °C
F-11.09 × 10-72.96 × 10-6
F-21.64 × 10-75.05 × 10-6
F-32.01 × 10-76.49 × 10-6
F-42.28 × 10-77.55 × 10-6
F-52.56 × 10-78.57 × 10-6
Table 5. Equilibrium O content in the droplet (wt %).
Table 5. Equilibrium O content in the droplet (wt %).
Flux2300 °C2500 °C
F-10.0710.227
F-20.0870.296
F-30.0960.336
F-40.1030.362
F-50.1090.386
Table 6. Predicted data subject to the transfer behavior of O, Si, and Mn (wt %).
Table 6. Predicted data subject to the transfer behavior of O, Si, and Mn (wt %).
EET-W F-1F-2F-3F-4F-5
1700 °CΔO0.00330.00320.00310.00290.0029
ΔSi0.2390.3650.5210.7200.947
ΔMn−0.425−0.529−0.585−0.620−0.644
2100 °CΔO0.04260.04660.04710.04700.0475
ΔSi0.2980.4170.5980.8521.182
ΔMn−0.299−0.413−0.472−0.508−0.532
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Fan, J.; Zhang, J.; Zhang, D. Optimizing Elemental Transfer Predictions in Submerged Arc Welding via CALPHAD Technology under Varying Heat Inputs: A Case Study into SiO2-Bearing Flux. Processes 2024, 12, 1541. https://doi.org/10.3390/pr12071541

AMA Style

Fan J, Zhang J, Zhang D. Optimizing Elemental Transfer Predictions in Submerged Arc Welding via CALPHAD Technology under Varying Heat Inputs: A Case Study into SiO2-Bearing Flux. Processes. 2024; 12(7):1541. https://doi.org/10.3390/pr12071541

Chicago/Turabian Style

Fan, Jun, Jin Zhang, and Dan Zhang. 2024. "Optimizing Elemental Transfer Predictions in Submerged Arc Welding via CALPHAD Technology under Varying Heat Inputs: A Case Study into SiO2-Bearing Flux" Processes 12, no. 7: 1541. https://doi.org/10.3390/pr12071541

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop