Combinatorial approaches for the design of metallic alloys

Alexis Deschamps; Franck Tancret; Imed-Eddine Benrabah; Frédéric De Geuser; Hugo P. Van Landeghem

doi:10.1016/j.crhy.2018.08.001

Combinatorial approaches for the design of metallic alloys
[Approches combinatoires pour la conception d'alliages métalliques]

Alexis Deschamps ¹ ; Franck Tancret ² ; Imed-Eddine Benrabah ¹ ; Frédéric De Geuser ¹ ; Hugo P. Van Landeghem ¹

¹ Université Grenoble Alpes, CNRS, Grenoble INP, SIMaP, 38000 Grenoble, France
² Université de Nantes, Institut des matériaux de Nantes Jean-Rouxel (IMN), CNRS UMR 6502, Polytech Nantes, rue Christian-Pauc, BP 50609, 44306 Nantes cedex 3, France

Comptes Rendus. Physique, New trends in metallic alloys / Alliages métalliques : nouvelles tendances, Volume 19 (2018) no. 8, pp. 737-754.

Résumé
Abstract

La conception de nouveaux alliages métalliques est confrontée au défi d'une complexité croissante de leur composition, du traitement et des microstructures résultantes nécessaires pour répondre à de multiples objectifs quant à leurs propriétés, ainsi qu'à l'exigence d'une étape de conception plus rapide et moins coûteuse. Cet article montre que les méthodes combinatoires, associant des approches numériques et expérimentales, peuvent être appliquées aux exigences spécifiques de la conception des alliages et conduire à une meilleure compréhension des processus fondamentaux de la métallurgie physique, tels que la précipitation, ainsi qu'à des compositions et des traitements d'alliages améliorés.

The design of new metallic alloys is faced with the challenge of an increasing complexity of the alloys composition, processing and resulting microstructures necessary to answer to multiple property targets, together with a requirement that the design stage be faster and less expensive. This paper shows that combinatorial methods, combining numerical and experimental approaches, can be applied to the specific requirements of alloy design and lead to improved understanding of fundamental processes of physical metallurgy, such as precipitation, together with improved alloy compositions and processing.

Publié le : 2018-09-13

DOI : 10.1016/j.crhy.2018.08.001

Keywords: Alloy design, Combinatorial approaches, High-throughput metallurgy
Mots-clés : Conception d'alliages, Approches combinatoires, Métallurgie à haut débit

Affiliations des auteurs :

Alexis Deschamps ¹ ; Franck Tancret ² ; Imed-Eddine Benrabah ¹ ; Frédéric De Geuser ¹ ; Hugo P. Van Landeghem ¹

¹ Université Grenoble Alpes, CNRS, Grenoble INP, SIMaP, 38000 Grenoble, France
² Université de Nantes, Institut des matériaux de Nantes Jean-Rouxel (IMN), CNRS UMR 6502, Polytech Nantes, rue Christian-Pauc, BP 50609, 44306 Nantes cedex 3, France

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     author = {Alexis Deschamps and Franck Tancret and Imed-Eddine Benrabah and Fr\'ed\'eric De Geuser and Hugo P. Van Landeghem},
     title = {Combinatorial approaches for the design of metallic alloys},
     journal = {Comptes Rendus. Physique},
     pages = {737--754},
     publisher = {Elsevier},
     volume = {19},
     number = {8},
     year = {2018},
     doi = {10.1016/j.crhy.2018.08.001},
     language = {en},
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Alexis Deschamps; Franck Tancret; Imed-Eddine Benrabah; Frédéric De Geuser; Hugo P. Van Landeghem. Combinatorial approaches for the design of metallic alloys. Comptes Rendus. Physique, New trends in metallic alloys / Alliages métalliques : nouvelles tendances, Volume 19 (2018) no. 8, pp. 737-754. doi : 10.1016/j.crhy.2018.08.001. https://comptes-rendus.academie-sciences.fr/physique/articles/10.1016/j.crhy.2018.08.001/

Bibliographie
Cité par

[1] R. Potyrailo; K. Rajan; K. Stoewe; I. Takeuchi; B. Chisholm; H. Lam Combinatorial and high-throughput screening of materials libraries: review of state of the art, ACS Comb. Sci., Volume 13 (2011), pp. 579-633 | DOI

[2] M.L. Green; I. Takeuchi; J.R. Hattrick-Simpers Applications of high throughput (combinatorial) methodologies to electronic, magnetic, optical, and energy-related materials, J. Appl. Phys., Volume 113 (2013) (UNSP 231101) | DOI

[3] G.B. Olson Genomic materials design: the ferrous frontier, Acta Mater., Volume 61 (2013), pp. 771-781 | DOI

[4] G.B. Olson; C.J. Kuehmann Materials genomics: from CALPHAD to flight, Scr. Mater., Volume 70 (2014), pp. 25-30 | DOI

[5] S. Curtarolo; W. Setyawan; G.L.W. Hart; M. Jahnatek; R.V. Chepulskii; R.H. Taylor; S. Wanga; J. Xue; K. Yang; O. Levy; M.J. Mehl; H.T. Stokes; D.O. Demchenko; D. Morgan AFLOW: an automatic framework for high-throughput materials discovery, Comput. Mater. Sci., Volume 58 (2012), pp. 218-226 | DOI

[6] S. Curtarolo; W. Setyawan; S. Wang; J. Xue; K. Yang; R.H. Taylor; L.J. Nelson; G.L.W. Hart; S. Sanvito; M. Buongiorno-Nardelli; N. Mingo; O. Levy AFLOWLIB.ORG: a distributed materials properties repository from high-throughput ab initio calculations, Comput. Mater. Sci., Volume 58 (2012), pp. 227-235 | DOI

[7] S. Curtarolo; G.L.W. Hart; M.B. Nardelli; N. Mingo; S. Sanvito; O. Levy The high-throughput highway to computational materials design, Nat. Mater., Volume 12 (2013), pp. 191-201 | DOI

[8] O.N. Senkov; J.D. Miller; D.B. Miracle; C. Woodward Accelerated exploration of multi-principal element alloys with solid solution phases, Nat. Commun., Volume 6 (2015), p. 6529 | DOI

[9] S. Kirkpatrick; C. Gelatt; M. Vecchi Optimization by simulated annealing, Science, Volume 220 (1983), pp. 671-680 | DOI

[10] J. Kennedy; R. Eberhart Particle swarm optimization, Proc. IEEE Int. Conf. Neural Netw., Institute of Electrical & Electronics Engineers, New York, USA, 1995, pp. 1942-1948

[11] X.-S. Yang; S. Deb Cuckoo search via Levey flights, Nabic, 2009 (A. Abraham; F. Herrera; A. Carvalho; V. Pai, eds.), IEEE, New York (2009), p. 210

[12] D. Goldberg Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Indianapolis, USA, 1989

[13] K. Deb; A. Pratap; S. Agarwal; T. Meyarivan A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., Volume 6 (2002), pp. 182-197 | DOI

[14] U.R. Kattner The thermodynamic modeling of multicomponent phase equilibria, J. Miner. Met. Mater. Soc., Volume 49 (1997), pp. 14-19 | DOI

[15] W. Xu; P.E.J. Rivera-Diaz-del-Castillo; S. van der Zwaag Designing nanoprecipitation strengthened UHS stainless steels combining genetic algorithms and thermodynamics, Comput. Mater. Sci., Volume 44 (2008), pp. 678-689 | DOI

[16] F. Tancret Computational thermodynamics and genetic algorithms to design affordable gamma′-strengthened nickel–iron based superalloys, Model. Simul. Mater. Sci. Eng., Volume 20 (2012) | DOI

[17] W. Xu; S. van der Zwaag Property and cost optimisation of novel UHS stainless steels via a genetic alloy design approach, ISIJ Int., Volume 51 (2011), pp. 1005-1010 | DOI

[18] H. Bhadeshia Neural networks in materials science, ISIJ Int., Volume 39 (1999), pp. 966-979 | DOI

[19] J.F. Pei; C.Z. Cai; X.J. Zhu; G.L. Wang Investigation on the processing-properties of hot deformed TA15 titanium alloy via support vector regression (E. Han; G.H. Lu; X.L. Shu, eds.), Mater. Model. Simul. Charact, Trans. Tech. Publications Ltd., Stafa-Zurich, 2011, pp. 134-143

[20] R. Jha; F. Pettersson; G.S. Dulikravich; H. Saxen; N. Chakraborti Evolutionary design of nickel-based superalloys using data-driven genetic algorithms and related strategies, Mater. Manuf. Process., Volume 30 (2015), pp. 488-510 | DOI

[21] C.A.L. Bailer-Jones; H. Bhadeshia; D.J.C. MacKay Gaussian process modelling of austenite formation in steel, Mater. Sci. Technol., Volume 15 (1999), pp. 287-294 | DOI

[22] F. Tancret; H. Bhadeshia; D.J.C. MacKay Comparison of artificial neural networks with Gaussian processes to model the yield strength of nickel-base superalloys, ISIJ Int., Volume 39 (1999), pp. 1020-1026 | DOI

[23] M. Mahfouf Optimal design of alloy steels using genetic algorithms, Adv. Comput. Intell. Learn. Methods Appl., Springer, 2002, pp. 425-436

[24] P. Das; S. Mukherjee; S. Ganguly; B.K. Bhattacharyay; S. Datta Genetic algorithm based optimization for multi-physical properties of HSLA steel through hybridization of neural network and desirability function, Comput. Mater. Sci., Volume 45 (2009), pp. 104-110 | DOI

[25] M. Mahfouf; M. Jamei; D.A. Linkens Optimal design of alloy steels using multiobjective genetic algorithms, Mater. Manuf. Process., Volume 20 (2005), pp. 553-567 | DOI

[26] R. Rettig; N.C. Ritter; H.E. Helmer; S. Neumeier; R.F. Singer Single-crystal nickel-based superalloys developed by numerical multi-criteria optimization techniques: design based on thermodynamic calculations and experimental validation, Model. Simul. Mater. Sci. Eng., Volume 23 (2015) | DOI

[27] F. Tancret Computational thermodynamics, Gaussian processes and genetic algorithms: combined tools to design new alloys, Model. Simul. Mater. Sci. Eng., Volume 21 (2013) | DOI

[28] E. Menou; G. Ramstein; E. Bertrand; F. Tancret Multi-objective constrained design of nickel-base superalloys using data mining- and thermodynamics-driven genetic algorithms, Model. Simul. Mater. Sci. Eng., Volume 24 (2016) | DOI

[29] E. Menou, Université de Nantes, France, 2016 (PhD Thesis)

[30] F. Tancret; C. Pineau; E. Menou; E. Bertrand; G. Ramstein; A. Devaux; C. Crozet Validation of a genetic algorithm alloy grade optimisation method: case study over superalloy AD730 composition span, Paris Fr. (2018)

[31] J.W. Yeh; S.K. Chen; S.J. Lin; J.Y. Gan; T.S. Chin; T.T. Shun; C.H. Tsau; S.Y. Chang Nanostructured high-entropy alloys with multiple principal elements: novel alloy design concepts and outcomes, Adv. Eng. Mater., Volume 6 (2004), pp. 299-303 | DOI

[32] D.B. Miracle; O.N. Senkov A critical review of high entropy alloys and related concepts, Acta Mater., Volume 122 (2017), pp. 448-511 | DOI

[33] I. Toda-Caraballo; P.E.J. Rivera-Diaz-del-Castillo Modelling solid solution hardening in high entropy alloys, Acta Mater., Volume 85 (2015), pp. 14-23 | DOI

[34] I. Toda-Caraballo; P.E.J. Rivera-Diaz-del-Castillo A criterion for the formation of high entropy alloys based on lattice distortion, Intermetallics, Volume 71 (2016), pp. 76-87 | DOI

[35] F. Tancret; I. Toda-Caraballo; E. Menou; P.E.J. Rivera Díaz-Del-Castillo Designing high entropy alloys employing thermodynamics and Gaussian process statistical analysis, Mater. Des., Volume 115 (2017), pp. 486-497 | DOI

[36] E. Menou; I. Toda-Caraballo; P.E.J. Rivera-Diaz-del-Castillo; C. Pineau; E. Bertrand; G. Ramstein; F. Tancret Evolutionary design of strong and stable high entropy alloys using multi-objective optimisation based on physical models, statistics and thermodynamics, Mater. Des., Volume 143 (2018), pp. 185-195 | DOI

[37] A. Agrawal; A. Choudhary Perspective: materials informatics and big data: realization of the “fourth paradigm” of science in materials science, APL Mater., Volume 4 (2016) | DOI

[38] H. Springer; D. Raabe Rapid alloy prototyping: compositional and thermo-mechanical high throughput bulk combinatorial design of structural materials based on the example of 30Mn-1.2C-xAl triplex steels, Acta Mater., Volume 60 (2012), pp. 4950-4959 | DOI

[39] H. Springer; M. Beide; D. Raabe Bulk combinatorial design of ductile martensitic stainless steels through confined martensite-to-austenite reversion, Mater. Sci. Eng. A, Struct. Mater.: Prop. Microstruct. Process., Volume 582 (2013), pp. 235-244 | DOI

[40] T. Gebhardt; D. Music; T. Takahashi; J.M. Schneider Combinatorial thin film materials science: from alloy discovery and optimization to alloy design, Thin Solid Films, Volume 520 (2012), pp. 5491-5499 | DOI

[41] S. Vives; P. Bellanger; S. Gorsse; C. Wei; Q. Zhang; J.-C. Zhao Combinatorial approach based on interdiffusion experiments for the design of thermoelectrics: application to the Mg–2(Si, Sn) alloys, Chem. Mater., Volume 26 (2014), pp. 4334-4337 | DOI

[42] S. Ding; Y. Liu; Y. Li; Z. Liu; S. Sohn; F.J. Walker; J. Schroers Combinatorial development of bulk metallic glasses, Nat. Mater., Volume 13 (2014), pp. 494-500 | DOI

[43] S. Hamann; M.E. Gruner; S. Irsen; J. Buschbeck; C. Bechtold; I. Kock; S.G. Mayr; A. Savan; S. Thienhaus; E. Quandt; S. Faehler; P. Entel; A. Ludwig The ferromagnetic shape memory system Fe–Pd–Cu, Acta Mater., Volume 58 (2010), pp. 5949-5961 | DOI

[44] C. Hutchinson A novel experimental approach to identifying kinetic transitions in solid state phase transformations, Scr. Mater., Volume 50 (2004), pp. 285-290 | DOI

[45] C.W. Sinclair; C.R. Hutchinson; Y. Brechet The effect of nb on the recrystallization and grain growth of ultra-high-purity alpha-Fe: a combinatorial approach, Metall. Mater. Trans. A, Phys. Metall. Mater. Sci., Volume 38A (2007), pp. 821-830 | DOI

[46] E. Contreras-Piedras; H.J. Dorantes-Rosales; V.M. Lopez-Hirata; F. Hernandez Santiago; J.L. Gonzalez-Velazquez; F.I. Lopez-Monrroy Analysis of precipitation in Fe-rich Fe–Ni–Al alloys by diffusion couples, Mater. Sci. Eng. A, Struct. Mater.: Prop. Microstruct. Process., Volume 558 (2012), pp. 366-370 | DOI

[47] T. Miyazaki Development of “Macroscopic Composition Gradient Method” and its application to the phase transformation, Prog. Mater. Sci., Volume 57 (2012), pp. 1010-1060 | DOI

[48] R.K.W. Marceau; C. Qiu; S.P. Ringer; C.R. Hutchinson A study of the composition dependence of the rapid hardening phenomenon in Al–Cu–Mg alloys using diffusion couples, Mater. Sci. Eng. A, Struct. Mater.: Prop. Microstruct. Process., Volume 546 (2012), pp. 153-161 | DOI

[49] E. Gumbmann; F. De Geuser; A. Deschamps; W. Lefebvre; F. Robaut; C. Sigli A combinatorial approach for studying the effect of Mg concentration on precipitation in an Al–Cu–Li alloy, Scr. Mater., Volume 110 (2016), pp. 44-47 | DOI

[50] T. Dorin; A. Deschamps; F. De Geuser; C. Sigli Quantification and modelling of the microstructure/strength relationship by tailoring the morphological parameters of the T1 phase in an Al–Cu–Li alloy, Acta Mater., Volume 75 (2014), pp. 134-146 | DOI

[51] L. Couturier; A. Deschamps; F. De Geuser; F. Fazeli; W.J. Poole An investigation of the strain dependence of dynamic precipitation in an Al–Zn–Mg–Cu alloy, Scr. Mater., Volume 136 (2017), pp. 120-123 | DOI

[52] J.M. Gregoire; D.G. Van Campen; C.E. Miller; R.J.R. Jones; S.K. Suram; A. Mehta High-throughput synchrotron X-ray diffraction for combinatorial phase mapping, J. Synchrotron Radiat., Volume 21 (2014), pp. 1262-1268 | DOI

[53] Z. Xiong; Y. He; J.R. Hattrick-Simpers; J. Hu Automated phase segmentation for large-scale X-ray diffraction data using a graph-based phase segmentation (GPhase) algorithm, ACS Comb. Sci., Volume 19 (2017), pp. 137-144 | DOI

[54] F. Ren; R. Pandolfi; D. Van Campen; A. Hexemer; A. Mehta On-the-fly data assessment for high-throughput X-ray diffraction measurements, ACS Comb. Sci., Volume 19 (2017), pp. 377-385 | DOI

[55] V.A. Esin; B. Denand; Q. Le Bihan; M. Dehmas; J. Teixeira; G. Geandier; S. Denis; T. Sourmail; E. Aeby-Gautier In situ synchrotron X-ray diffraction and dilatometric study of austenite formation in a multi-component steel: influence of initial microstructure and heating rate, Acta Mater., Volume 80 (2014), pp. 118-131 | DOI

[56] F. De Geuser; A. Deschamps Precipitate characterisation in metallic systems by small-angle X-ray or neutron scattering, C. R. Physique, Volume 13 (2012), pp. 246-256 | DOI

[57] X. Boulnat; N. Sallez; M. Dade; A. Borbely; J.-L. Bechade; Y. de Carlan; J. Malaplate; Y. Brechet; F. de Geuser; A. Deschamps; P. Donnadieu; D. Fabregue; M. Perez Influence of oxide volume fraction on abnormal growth of nanostructured ferritic steels during non-isothermal treatments: an in situ study, Acta Mater., Volume 97 (2015), pp. 124-130 | DOI

[58] R. Ivanov; A. Deschamps; F. De Geuser High throughput evaluation of the effect of Mg concentration on natural ageing of Al–Cu–Li–(Mg) alloys, Scr. Mater., Volume 150 (2018), pp. 156-159 | DOI

[59] F. De Geuser; M.J. Styles; C.R. Hutchinson; A. Deschamps High-throughput in-situ characterization and modeling of precipitation kinetics in compositionally graded alloys, Acta Mater., Volume 101 (2015), pp. 1-9 | DOI

[60] M. Perez; M. Dumont; D. Acevedo-Reyes Implementation of classical nucleation and growth theories for precipitation, Acta Mater., Volume 56 (2008), pp. 2119-2132

Tesfaye T. Molla Computational Design of Materials for Sintering: Challenges and Prospects, Journal of the Japan Society of Powder and Powder Metallurgy, Volume 72 (2025) no. Supplement, p. S243 | DOI:10.2497/jjspm.14f-t12-02
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Qingze Na; Jiachen Zhang; Pengfa Feng; Yufei Zuo; Tiantian Ma; Fan Lu; Bin Li; Guojun Zhang; Haijun Su; Lin Liu Revealing the Ta effect on γ' phase coarsening behavior and mechanical properties in CoNi-based superalloys, Materials Characterization (2025), p. 114989 | DOI:10.1016/j.matchar.2025.114989
Tobias Gaag; Maximilian Heidowitzsch; Florian Galgon; Carolin Körner; Christopher H. Zenk A Novel Approach for Rapid Material Library Generation Using Laser‐Remelting, Advanced Engineering Materials, Volume 26 (2024) no. 3 | DOI:10.1002/adem.202301316
Mariam Assi; Julien Favre; Marcin Brykala; Franck Tancret; Anna Fraczkiewicz Design and Assessment of an Austenitic Stainless Alloy for Laser Powder Bed Additive Manufacturing, Applied Sciences, Volume 14 (2024) no. 19, p. 8649 | DOI:10.3390/app14198649
Muni Kumar Kandula; Mahander Pratap Singh; Esakkiraja Neelamegan; Aloke Paul; Chattopadhyay Kamanio High-throughput pseudo-binary diffusion couple approach for alloy design in cobalt-based superalloys, Materials Characterization, Volume 210 (2024), p. 113842 | DOI:10.1016/j.matchar.2024.113842
Yuanxun Zhou; Jiantao Fan; Biao Wu; Cai Chen; Jianhao Wang; Hongwei Du; Bingbing Zhao; Lanting Zhang; Hong Wang Efficient Modeling of Composition-Dependent Lattice Constants of Multicomponent Metallic Solid Solutions Based on High-Throughput Experiment, Metallurgical and Materials Transactions A, Volume 55 (2024) no. 4, p. 1243 | DOI:10.1007/s11661-024-07324-9
Ninad Bhat; Amanda S. Barnard; Nick Birbilis Inverse Design of Aluminium Alloys Using Genetic Algorithm: A Class-Based Workflow, Metals, Volume 14 (2024) no. 2, p. 239 | DOI:10.3390/met14020239
Andreas Greul; Dominik Knapic; Gianina Popescu‐Pelin; Iuliana Urzica; Gabriel Socol; Andrei Ionut Mardare; Achim Walter Hassel Combinatorial Property Mapping of Titanium‐Zirconium Thin Film Libraries as Screening for Medical Material Candidates, physica status solidi (a), Volume 221 (2024) no. 19 | DOI:10.1002/pssa.202400004
Camilo A. F. Salvador; Hugo P. Van Landeghem; Renato A. Antunes Selection of Ti Alloys for Bio‐Implants: An Application of the Ashby Approach with Conflicting Objectives, Advanced Engineering Materials, Volume 25 (2023) no. 22 | DOI:10.1002/adem.202301169
Panos Tsakiropoulos A Perspective of the Design and Development of Metallic Ultra-High Temperature Materials: Refractory Metal Intermetallic Composites, Refractory Complex Concentrated Alloys and Refractory High Entropy Alloys, Alloys, Volume 2 (2023) no. 3, p. 184 | DOI:10.3390/alloys2030014
Tesfaye T. Molla Computational Efficient Modeling of Supersolidus Liquid Phase Sintering in Multi-component Alloys for ICME Applications, Integrating Materials and Manufacturing Innovation, Volume 12 (2023) no. 2, p. 105 | DOI:10.1007/s40192-023-00294-7
Elise Garel; Jean-Luc Parouty; Hugo Van Landeghem; Marc Verdier; Florence Robaut; Stéphane Coindeau; Raphaël Boichot Coupling mixture designs, high-throughput experiments and machine learning for accelerated exploration of multinary systems, Materials Design, Volume 231 (2023), p. 112055 | DOI:10.1016/j.matdes.2023.112055
Adilson Vitor Rodrigues; Rafael Kakitani; Cássio Silva; Leonardo Giovanetti; Marcelino Dias; Hani Henein; Amauri Garcia; Noé Cheung Influence of Minor Additions of Be on the Eutectic Modification of an Al-33wt. | DOI:10.3390/met13010094
Reinol Josef Compañero; Andreas Feldmann; Peter Samuelsson; Anders Tilliander; Pär Göran Jönsson; Rutger Gyllenram Appraising the value of compositional information and its implications to scrap-based production of steel, Mineral Economics, Volume 36 (2023) no. 3, p. 463 | DOI:10.1007/s13563-022-00361-z
Pascal Brault; Anne-Lise Thomann; Marjorie Cavarroc Theory and molecular simulations of plasma sputtering, transport and deposition processes, The European Physical Journal D, Volume 77 (2023) no. 2 | DOI:10.1140/epjd/s10053-023-00592-x
Alixe Dreano; Julien Favre; Christophe Desrayaud; Pauline Chanin-Lambert; Andreas Wimmer; Michael F. Zaeh Computational design of a crack-free aluminum alloy for additive manufacturing, Additive Manufacturing, Volume 55 (2022), p. 102876 | DOI:10.1016/j.addma.2022.102876
Freddie Markanday; Gareth Conduit; Bryce Conduit; Julia Pürstl; Katerina Christofidou; Lova Chechik; Gavin Baxter; Christopher Heason; Howard Stone Design of a Ni-based superalloy for laser repair applications using probabilistic neural network identification, Data-Centric Engineering, Volume 3 (2022) | DOI:10.1017/dce.2022.31
Lorenzo Malerba; Abderrahim Al Mazouzi; Marjorie Bertolus; Marco Cologna; Pål Efsing; Adrian Jianu; Petri Kinnunen; Karl-Fredrik Nilsson; Madalina Rabung; Mariano Tarantino Materials for Sustainable Nuclear Energy: A European Strategic Research and Innovation Agenda for All Reactor Generations, Energies, Volume 15 (2022) no. 5, p. 1845 | DOI:10.3390/en15051845
T. T. Molla; A. Atthapreyangkul; G. B. Schaffer Computational Alloy Design for Process-Related Uncertainties in Powder Metallurgy, Integrating Materials and Manufacturing Innovation, Volume 11 (2022) no. 2, p. 172 | DOI:10.1007/s40192-022-00255-6
Diaa Mereib; Judith Monnier; Loïc Perrière; Benjamin Villeroy; Mathilde Laurent-Brocq Chemically architectured alloys: How interphase width influences the strengthening, Journal of Alloys and Compounds, Volume 904 (2022), p. 163997 | DOI:10.1016/j.jallcom.2022.163997
I.-E. Benrabah; H.P. Van Landeghem; F. Bonnet; B. Denand; G. Geandier; A. Deschamps High-throughput investigation of ferrite growth kinetics in graded ternary Fe-C-X alloys, Materialia, Volume 24 (2022), p. 101480 | DOI:10.1016/j.mtla.2022.101480
J. F. S. Markanday Applications of alloy design to cracking resistance of additively manufactured Ni-based alloys, Materials Science and Technology, Volume 38 (2022) no. 16, p. 1300 | DOI:10.1080/02670836.2022.2068759
Yannick Champion New Material Developments/High-Entropy Alloys, Metallurgy in Space (2022), p. 473 | DOI:10.1007/978-3-030-89784-0_21
Hooman Danesh; Mahdi Javanbakht; Sam Mirzakhani Nonlocal integral elasticity based phase field modelling and simulations of nanoscale thermal- and stress-induced martensitic transformations using a boundary effect compensation kernel, Computational Materials Science, Volume 194 (2021), p. 110429 | DOI:10.1016/j.commatsci.2021.110429
M.A. Ackers; O.M.D.M. Messé; U. Hecht Novel approach of alloy design and selection for additive manufacturing towards targeted applications, Journal of Alloys and Compounds, Volume 866 (2021), p. 158965 | DOI:10.1016/j.jallcom.2021.158965
Sangqi Xiong; Xin Li; Xiangwei Wu; Jin Yu; Oleg I. Gorbatov; Igor Di Marco; Paul R.C. Kent; Weiwei Sun A combined machine learning and density functional theory study of binary Ti-Nb and Ti-Zr alloys: Stability and Young’s modulus, Computational Materials Science, Volume 184 (2020), p. 109830 | DOI:10.1016/j.commatsci.2020.109830
V A Kechin; A V Kireev; E Y Lyublinski Principles of creating new alloy compositions for the production of cast protectors, IOP Conference Series: Materials Science and Engineering, Volume 896 (2020) no. 1, p. 012097 | DOI:10.1088/1757-899x/896/1/012097
Praveen Sreeramagiri; Ajay Bhagavatam; Abhishek Ramakrishnan; Husam Alrehaili; Guru Prasad Dinda Design and development of a high-performance Ni-based superalloy WSU 150 for additive manufacturing, Journal of Materials Science Technology, Volume 47 (2020), p. 20 | DOI:10.1016/j.jmst.2020.01.041
Chennakesava Sai Pitchi; Amrita Priyadarshini; Ganesh Sana; Suresh Kumar Reddy Narala A review on alloy composition and synthesis of β-Titanium alloys for biomedical applications, Materials Today: Proceedings, Volume 26 (2020), p. 3297 | DOI:10.1016/j.matpr.2020.02.468
Tesfaye T. Molla; J. Z. Liu; G. B. Schaffer Computational Efficient Modeling of Sintering in Multi-component Alloys for ICME Applications, Metallurgical and Materials Transactions B, Volume 51 (2020) no. 1, p. 54 | DOI:10.1007/s11663-019-01755-1
Jose Martin Herrera Ramirez; Raul Perez Bustamante; Cesar Augusto Isaza Merino; Ana Maria Arizmendi Morquecho Manufacturing Processes of Light Metals and Composites, Unconventional Techniques for the Production of Light Alloys and Composites (2020), p. 13 | DOI:10.1007/978-3-030-48122-3_2
Tesfaye T. Molla; J. Z. Liu; G. B. Schaffer Computational Design of Functionally Graded Materials from Sintered Powders, Integrating Materials and Manufacturing Innovation, Volume 8 (2019) no. 2, p. 82 | DOI:10.1007/s40192-019-00127-6
Imed-Eddine Benrabah; Hugo Paul Van Landeghem; Frédéric Bonnet; Florence Robaut; Alexis Deschamps Use of Space-Resolved in-Situ High Energy X-ray Diffraction for the Characterization of the Compositional Dependence of the Austenite-to-Ferrite Transformation Kinetics in Steels, Quantum Beam Science, Volume 4 (2019) no. 1, p. 1 | DOI:10.3390/qubs4010001
Christophe Sigli; Frédéric De Geuser; Alexis Deschamps; Joël Lépinoux; Michel Perez Recent advances in the metallurgy of aluminum alloys. Part II: Age hardening, Comptes Rendus. Physique, Volume 19 (2018) no. 8, p. 688 | DOI:10.1016/j.crhy.2018.10.012

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