Next Article in Journal
Optimizing Metallographic Etchants for Ancient Gold and Silver Materials
Next Article in Special Issue
Technological Insights into the Evolution of Bronze Bell Metal Casting on the Korean Peninsula
Previous Article in Journal
The Influence of Laser Power and Scan Speed on the Dimensional Accuracy of Ti6Al4V Thin-Walled Parts Manufactured by Selective Laser Melting
Previous Article in Special Issue
Phase Prediction, Microstructure and Mechanical Properties of Fe–Mn–Ni–Cr–Al–Si High Entropy Alloys
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Editorial

Casting Alloy Design and Characterization

by
Eleani Maria Da Costa
and
Carlos Alexandre Dos Santos
*
Materials Laboratory, School of Technology, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre 90619-900, Brazil
*
Author to whom correspondence should be addressed.
Metals 2022, 12(7), 1228; https://doi.org/10.3390/met12071228
Submission received: 14 July 2022 / Accepted: 18 July 2022 / Published: 20 July 2022
(This article belongs to the Special Issue Casting Alloy Design and Characterization)

Abstract

:
Metal casting processes routinely used in the foundry industry (e.g., gravity or pressure casting) are subject to a wide range of operational parameters. Since there is a close correlation between solidification conditions, microstructure, and properties, the effects of the solidification thermal parameters and alloying elements on microstructure designs and the resulting properties in cast alloys have stimulated new research interest. Thus, this Special Issue aims to collect research articles focused on the design and characterization of cast alloys, especially on the interrelationship between solidification, microstructure, and properties; both experimental and theoretical research are welcome for contribution.

1. Introduction

Casting processes induce a wide-ranging of solidification conditions, affecting the microstructure formations. Grain size and morphology, interdendritic spacing, solute segregation and precipitation, the presence of porosity, and other defects are strongly influenced by the thermal behavior of the metal–mold system during solidification, imposing a close correlation between solidification, the resulting microstructure, and the final properties [1]. Consequently, the temperature–time evolution during solidification is closely related to the structural integrity of shaped castings. Gravity or pressure die casting, continuous casting, and squeeze casting are some of the casting processes where product quality is affected by metal–mold heat-transfer conditions [2,3,4].
One simple, low-cost, and well-established method of investigating and understanding solidification evolution is thermal analysis based on the cooling curve, which can be acquired during the cooling of a molten metal or alloy in specific thermal analysis cups or dedicated solidification apparatus [5,6,7,8]. The technique consists of monitoring the metal during solidification using thermocouples, allowing researchers to obtain temperature–time cooling curves. Thermal solidification parameters, such as the phase-transformation temperature, the solidification range, and the kinetic range, the latent heat release, and the solid or liquid fractions, can be calculated.
However, industrial solidification processes parameters, such as thermal gradient, cooling rate, and interfacial heat transfer coefficient, are more complex than those involved in the thermal analyses. In order to improve casting quality, it is fundamental to understand and control the solidification process. An alternative approach to predicting the solidification behavior of metals or alloys is the use of analytical or numerical simulations by applying thermodynamic models, which incorporate phenomena such as heat and mass transfer, fluid flow, alloy solidification, and solid-state phase transformations [9,10]. There are a number of commercial software packages, such as Thermo-Calc, FactSage, Pandat, and Mat-Calc, known as CALPHAD tools (calculation of phase diagram methodology) [11], which have been used extensively in industry in recent decades. Modelling offers the ability to simulate operating parameters in order to guarantee that optimal conditions for casting processes are determined. In parallel, computer science, automation, and artificial intelligence techniques are being introduced for optimizing the manufacture process, reducing costs, and maximizing the quality of the final product [12,13,14,15]. However, it is important to note that the accuracy of numerical simulations depends on the precision of input information, such as the solidification thermal parameters and the thermophysical properties of the metal–mold system [16,17,18,19,20].
Since the control of microstructures during casting processes involves heat and mass transfer, solidification, and phase transformation, numerous studies have focused on the development of correlations to optimize the operating parameters as a function of a number of process variables. These include metal–mold chemical compositions, material thermophysical properties, mold design, cooling conditions, and others [21,22,23,24,25,26,27]. In addition, the influence of microstructure features (e.g., grain size, dendrite arm spacing, secondary phase, and defects), their influence on subsequent manufacturing processes (e.g., heat treatment and machining) [28,29,30,31,32], and their influence on corrosion and wear responses were performed in [33,34,35].
The flowchart shown in Figure 1 presents the main operational and metallurgical variables involved during solidification, and their intercorrelation with the final quality of the casting.

2. Contributions

This Special Issue is dedicated to works related to casted alloy designs and characterization. Contributions to research on the correlation between processing, properties, and microstructures are welcome. The scope includes, but is not limited to, the following technical topics: casting processes and novel techniques; solidification: experimental and theoretical studies; microstructure and property characterization; numerical and analytical simulations; heat and mass transfer; processing–structure–property relations; industrial applications.

Author Contributions

Conceptualization E.M.D.C. and C.A.D.S.; investigation E.M.D.C. and C.A.D.S.; methodology E.M.D.C. and C.A.D.S.; project administration E.M.D.C. and C.A.D.S.; writing—original draft preparation E.M.D.C. and C.A.D.S.; Writing—Review and editing, E.M.D.C. and C.A.D.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Not applicable.

Acknowledgments

The authors acknowledge the support provided by CNPq (The Brazilian National Council for Scientific and Technological Development), FINEP (Financial Agency for Studies and Projects), FAPERGS (Foundation for Research Support of the State of Rio Grande do Sul), and PUCRS (Pontifical Catholic University of Rio Grande do Sul).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Quaresma, J.M.V.; Santos, C.A.; Garcia, A. Correlation between unsteady-state solidification conditions, dendrite spacings, and mechanical properties of Al-Cu alloys. Metall. Mater. Trans. A 2000, 31, 3167–3178. [Google Scholar] [CrossRef]
  2. Santos, C.A.; Quaresma, J.M.V.; Garcia, A. Determination of transient interfacial heat transfer coefficients in chill mold castings. J. Alloys Compd. 2001, 319, 174–186. [Google Scholar] [CrossRef]
  3. Barcellos, V.K.; Ferreira, C.R.F.; Santos, C.A.; Spim, J.A. Analysis of metal mould heat transfer coefficients during continuous casting of steel. Ironmak. Steelmak. 2010, 37, 47–56. [Google Scholar] [CrossRef]
  4. Spinelli, J.E.; Tosetti, J.P.; Santos, C.A.; Spim, J.A.; Garcia, A. Microstructure and solidification thermal parameters in thin strip continuous casting of a stainless steel. J. Mater. Process. Technol. 2004, 150, 255–262. [Google Scholar] [CrossRef]
  5. Costa, A.M.; Costa, C.E.; Vecchia, F.D.; Ricka, C.; Scherer, M.; Santos, C.A.; Dedavid, B.A. Study of the influence of copper and magnesium additions on the microstructure formation of Zn–Al hypoeutectic alloys. J. Alloys Compd. 2009, 448, 89–99. [Google Scholar] [CrossRef]
  6. Porás, B.R.; França, R.P.; Spim, J.A.; Garcia, A.; Costa, E.M.; Santos, C.A. The effects of dendrite arm spacings (as-cast) and aging time (solution heat-treated) of Al-Cu alloy on hardness. J. Alloys Compd. 2013, 549, 324–335. [Google Scholar] [CrossRef]
  7. Chen, R.; Xu, Q.; Guo, H.; Xia, Z.; Wu, Q.; Liu, B. Correlation of solidification microstructure refining scale, Mg composition and heat treatment conditions with mechanical properties in Al-7Si-Mg cast aluminum alloys. Mater. Sci. Eng. A 2017, 685, 391–402. [Google Scholar] [CrossRef]
  8. Bartex, S.L.T.; Santos, C.A.; Barcellos, V.K.; Schaeffer, L. Effect of solid fraction on microstructures and mechanical properties of a Mg-Al-La-Ca alloy processed by rheocasting. J. Alloys Compd. 2019, 776, 297–305. [Google Scholar] [CrossRef]
  9. Ferreira, I.L.; Garcia, A. The application of numerical and analytical approaches for the determination of thermophysical properties of Al–Si–Cu–Mg alloys. Contin. Mech. Thermodyn. 2020, 32, 1231–1244. [Google Scholar] [CrossRef]
  10. Ferreira, I.; Santos, C.A.; Garcia, A.; Voller, V. Analytical, numerical, and experimental analysis of inverse macrosegregation during upward unidirectional solidification of Al-Cu alloys. Metall. Mater. Trans. B 2004, 35, 285–297. [Google Scholar] [CrossRef] [Green Version]
  11. Jung, I.-H.; Van Ende, M.-A. Computational thermodynamic calculations: FactSage from CALPHAD thermodynamic database to virtual process simulation. Metall. Mater. Trans. B 2020, 51, 1851–1874. [Google Scholar] [CrossRef]
  12. Santos, C.A.; Spim, J.A.; Ierardi, M.C.F.; Garcia, A. The use of artificial intelligence technique for the optimisation of process parameters used in the continuous casting of steel. Appl. Math. Model. 2002, 26, 1077–1092. [Google Scholar] [CrossRef]
  13. Santos, C.A.; Fortaleza, E.L.; Frick, C.R.F.; Spim, J.A.; Garcia, A. A solidification heat transfer model and a neural network based algorithm applied to the continuous casting of steel billets and blooms, Model. Simul. Mater. Sci. Eng. 2005, 13, 1071–1087. [Google Scholar] [CrossRef]
  14. Santos, C.A.; Cheung, N.; Garcia, A.; Spim, J.A. Application of a solidification mathematical model and a genetic algorithm in the optimization of strand thermal profile along the continuous casting of steel. Mater. Manuf. Processes 2005, 20, 421–434. [Google Scholar] [CrossRef]
  15. Cheung, N.; Santos, C.A.; Spim, J.A.; Garcia, A. Application of a heuristic search technique for the improvement of spray zones cooling conditions in continuously cast steel billets. Appl. Math. Model. 2006, 30, 104–115. [Google Scholar] [CrossRef] [Green Version]
  16. Ferreira, I.L.; Siqueira, C.A.; Santos, C.A.; Garcia, A. Influence of Metal/Mold Heat Transfer Coefficient on the Inverse Macrosegregation Profile of an Al-6.2wt% Cu Alloy Unidirectionally Solidified. Mater. Sci. Forum 2004, 455-456, 728–731. [Google Scholar] [CrossRef]
  17. Santos, C.A.; Siqueira, C.A.; Garcia, A.; Quaresma, J.M.V.; Spim, J.A. Metal–mold heat transfer coefficients during horizontal and vertical Unsteady-State solidification of Al–Cu and Sn–Pb Alloys, Inverse Probl. Sci. Eng. 2004, 12, 279–296. [Google Scholar] [CrossRef]
  18. Souza, E.N.; Cheung, N.; Santos, C.A.; Garcia, A. Factors affecting solidification thermal variables along the cross-section of horizontal cylindrical ingots, Mater. Sci. Eng. A 2005, 397, 239–248. [Google Scholar] [CrossRef]
  19. Santos, C.A.; Garcia, A.; Frick, C.R.F.; Spim, J.A. Evaluation of heat transfer coefficients along the secondary cooling zones in the continuous casting of steel billets, Inverse Probl. Sci. Eng. 2006, 14, 687–700. [Google Scholar] [CrossRef]
  20. Melo, M.L.N.; Penhalber, C.L.; Pereira, N.A.; Pelliciari, C.L., Jr.; Santos, C.A. Numerical and experimental analysis of microstructure formation during stainless steels solidification, J. Mater. Sci. 2007, 42, 2267–2275. [Google Scholar] [CrossRef]
  21. Dedavid, B.A.; Costa, E.M.; Ferreira, C.R.F. A Study of precipitates formation in AA 380.0 aluminium alloys modified by the addition of magnesium. J. Therm. Anal. Calorim. 2002, 67, 473–480. [Google Scholar] [CrossRef]
  22. Costa, E.M.; Compani, M.; Floriani, A.R.; Dedadvid, B.A. AA 380 Aluminum alloy-based metal matrix composites reinforced with alumina produced by compocasting process. Acta Microsc. 2003, 12, 253–254. [Google Scholar]
  23. Hönnicke, M.G.; Mazzaro, I.; Manica, J. Structural Characterization of doped GaSb single crystals by X-ray topography. J. Electron. Mater. 2010, 39, 727–731. [Google Scholar] [CrossRef] [Green Version]
  24. Barcellos, V.K.; Ferreira, C.R.F.; Spim, J.A.; Santos, C.A.; Garcia, A. The Interrelation between casting size, steel grade, and temperature evolution along the mold length and at the strand surface during continuous casting of steel. Mater. Manuf. Processes 2011, 26, 113–126. [Google Scholar] [CrossRef]
  25. Streicher, M.; Corregidor, V.; Catarino, N.; Alves, L.C.; Franco, N.; Fonseca, M.; Martins, L.; Alves, E.; Costa, E.M.; Dedavid, B.A. Study of In distribution on GaInSb: Al crystals by ion beam techniques. Nucl. Instrum. Methods Phys. Res. Sect. B Beam Interact. Mater. At. 2016, 371, 278–282. [Google Scholar] [CrossRef]
  26. Guterres, A.M.; Oliveira, C.A.L.; Santos. C.A. Influence of chemical composition, porosity and aging in the mechanical properties of Al-Si-Mg alloys. Tecnol. Metal. Mater. Min. 2019, 16, 316–324. [Google Scholar] [CrossRef]
  27. Ribeiro, T.M.; Catellan, E.; Garcia, A.; Santos, C.A. The effects of Cr addition on microstructure, hardness and tensile properties of as-cast Al–3.8wt.%Cu–(Cr) alloys. J. Mater. Res. Technol. 2020, 9, 6620–6631. [Google Scholar] [CrossRef]
  28. Werlang, A.F.; Souza, B.V.; Santos, C.A. Effect of heat treatments on austempered ductile iron. Mater. Manuf. Processes 2015, 30, 1317–1323. [Google Scholar] [CrossRef]
  29. Filho, A.W.; Souza, B.V.; Santos, C.A. The Influence of austempering conditions on the machinability of a ductile iron. Mater. Manuf. Processes 2016, 31, 1836–1843. [Google Scholar] [CrossRef]
  30. Souza, B.V.; Ribeiro, T.M.; Francois, A.; Santos, C.A. Austempering heat treatments of ductile iron using molten metal baths. Mater. Manuf. Processes 2018, 33, 1667–1673. [Google Scholar] [CrossRef]
  31. Porás, B.R.; Lopes, M.M.; Garcia, A.; Santos, C.A. The correlation of microstructure features, dry sliding wear behavior, hardness and tensile properties of Al-2wt%Mg-Zn alloys. J. Alloys Compd. 2018, 764, 267–278. [Google Scholar] [CrossRef]
  32. Ache, C.T.; Lopes, M.M.; Porás, B.R.; Garcia, A.; Santos, C.A. Dendritic spacing/columnar grain diameter of Al–2Mg–Zn alloys affecting hardness, tensile properties, and dry sliding wear in the as-cast/heat-treated conditions. Adv. Eng. Mater. 2020, 1, 1901145. [Google Scholar] [CrossRef]
  33. Bonetti, I.; Almeida, E.A.S.; Costa, C.E.; Paredes, R.S.C.; Sucharski, G.B.; Costa, E.M.; Franco, E.; Milan, J.C. Effect of flame spray deposition parameters on the microstructure, microhardness and corrosion resistance of FeNbC coatings on AISI 1020 steel. Mater. Res. Express 2019, 6, 086530. [Google Scholar] [CrossRef]
  34. Rosso, E.; Santos, C.A.; Garcia, A. Microstructure, hardness, tensile strength, and sliding wear of hypoeutectic Al–Si cast alloys with small Cr additions and Fe-impurity content. Adv. Eng. Mater. 2021, 1, 2001552. [Google Scholar] [CrossRef]
  35. Machado, P.A.B.; Quaresma, J.M.V.; Garcia, A.; Santos, C.A. Investigation on machinability in turning of as-cast and T6 heat-treated Al-(3, 7, 12%)Si-0.6%Mg alloys. J. Manuf. Processes 2022, 75, 514–526. [Google Scholar] [CrossRef]
Figure 1. Operational and metallurgical variables during casting processes.
Figure 1. Operational and metallurgical variables during casting processes.
Metals 12 01228 g001
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Da Costa, E.M.; Dos Santos, C.A. Casting Alloy Design and Characterization. Metals 2022, 12, 1228. https://doi.org/10.3390/met12071228

AMA Style

Da Costa EM, Dos Santos CA. Casting Alloy Design and Characterization. Metals. 2022; 12(7):1228. https://doi.org/10.3390/met12071228

Chicago/Turabian Style

Da Costa, Eleani Maria, and Carlos Alexandre Dos Santos. 2022. "Casting Alloy Design and Characterization" Metals 12, no. 7: 1228. https://doi.org/10.3390/met12071228

APA Style

Da Costa, E. M., & Dos Santos, C. A. (2022). Casting Alloy Design and Characterization. Metals, 12(7), 1228. https://doi.org/10.3390/met12071228

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