Design of Re-free nickel-base single crystal superalloys using modelling and experimental validations

Zhang, Longfei; Huang, Zaiwang; Pan, Yanming; Jiang, Liang

doi:10.1088/1361-651x/ab2031

Cited by 16 publications

(5 citation statements)

References 42 publications

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“…Where and ( ) are the atomic fraction and Md value of ith elements, respectively. The �� value is then successfully used for predicting the propensity of TCP phase formation, and more useful, alloy design of nickel-based superalloy [204]. From the physical meaning, the Md value correlates with the electronegativity, which represents the tendency of electrons being attracted by an atom.…”

Section: Phase Stability Modelmentioning

confidence: 99%

Alloy Design and Characterization of γ′ Strengthened Nickel-based Superalloys for Additive Manufacturing

2021

Linköping Studies in Science and Technology. Licentiate Thesis

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Nickel-based superalloys, an alloy system bases on nickel as the matrix element with the addition of up to 10 more alloying elements including chromium, aluminum, cobalt, tungsten, molybdenum, titanium, and so on. Through the development and improvement of nickel-based superalloys in the past century, they are well proved to show excellent performance at the elevated service temperature. Owing to the combination of extraordinary high-temperature mechanical properties, such as monotonic and cyclic deformation resistance, fatigue crack propagation resistance; and high-temperature chemical properties, such as corrosion and oxidation resistance, phase stability, nickel-based superalloys are widely used in the critical hot-section components in aerospace and energy generation industries. The success of nickel-based superalloy systems attributes to both the well-tailored microstructures with the assistance of carefully doped alloying elements, and the intently developed manufacturing processes. The microstructure of the modern nickel-based superalloys consists of a two-phase configuration: the intermetallic precipitates (Ni,Co)3(Al,Ti,Ta) known as γ′ phase dispersed into the austenite γ matrix, which is firstly introduced in the 1940s. The recently developed additive manufacturing (AM) techniques, acting as the disruptive manufacturing process, offers a new avenue for producing the nickel-based superalloy components with complicated geometries. However, γ′ strengthened nickel-based superalloys always suffer from the micro-cracking during the AM process, which is barely eliminated by the process optimization. On this basis, the new compositions of γ′ strengthened nickel-based superalloy adapted to the AM process are of great interest and significance. This study sought to design novel γ′ strengthened nickel-based superalloys readily for AM process with limited cracking susceptibility, based on the understanding of the cracking mechanisms. A two-parameter model is developed to predict the additive manufacturability for any given composition of a nickel-based superalloy. One materials index is derived from the comparison of the deformation-resistant capacity between dendritic and interdendritic regions, while another index is derived from the difference of heat resistant capacity of these two spaces. By plotting the additive manufacturability diagram, the superalloys family can be categorized into the easy-to-weld, fairly-weldable, and non-weldable regime with the good agreement of the existed knowledge. To design a novel superalloy, a Cr-Co-MoW -Al-Ti-Ta-Nb-Fe-Ni alloy family is proposed containing 921,600 composition recipes in total. Through the examination of additive manufacturability, undesired phase formation propensity, and the precipitation fraction, one composition of superalloy, MAD542, out of the 921,600 candidates is selected. Validation of additive manufacturability of MAD542 is carried out by laser powder bed fusion (LPBF). By optimizing the LPBF process parameters, the crack-free MAD542 part is achieved. I...

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Section: Phase Stability Modelmentioning

confidence: 99%

Alloy Design and Characterization of γ′ Strengthened Nickel-based Superalloys for Additive Manufacturing

2021

Linköping Studies in Science and Technology. Licentiate Thesis

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“…For this reason the full set of descriptors were used throughout. We focus on pure γ/γ alloys and therefore circumvent consideration of any carbides and secondary phases that could form [2,41,42,43,44,45,46].…”

Section: Data Processingmentioning

confidence: 99%

Machine learning predictions of superalloy microstructure

Taylor¹,

Conduit²

2021

Preprint

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Gaussian process regression machine learning with a physically-informed kernel is used to model the phase compositions of nickel-base superalloys. The model delivers good predictions for laboratory and commercial superalloys, with R 2 > 0.8 for all but two components of each of the γ and γ phases, and R 2 = 0.924 (RMSE = 0.063) for the γ fraction. For four benchmark SX-series alloys the methodology predicts the γ phase composition with RMSE = 0.006 and the fraction with RMSE = 0.020, superior to the 0.007 and 0.021 respectively from CALPHAD. Furthermore, unlike CALPHAD Gaussian process regression quantifies the uncertainty in predictions, and can be retrained as new data becomes available.

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“…Computational alloy design often employs machine learning methods [8][9][10] or CALPHAD (CALculation of PHase Diagrams) calculations to predict alloy properties. [11,12] Machine learning allows the prediction of phase stability or of phase transformation temperatures, density, and other properties of interest with high confidence, especially when combined with high-throughput experiments. [13,14] Such experiments are necessary to obtain the large amount of data necessary for reliable predictions.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, an alloy is selected by screening the calculated alloy properties for the composition which best fulfills the design requirements. [9,12,15] Alternatively, optimization algorithms may be used to evolve a population of compositions towards the desired properties. [16,17] In this study, we employ the latter approach by coupling CALPHAD calculations to genetic multi-criteria optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

Numerical Design of CoNi-Base Superalloys With Improved Casting Structure

Wahlmann

Bandorf

Volz

et al. 2022

Metall Mater Trans A

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Numerical methods can accelerate the design of alloys with improved material properties. One approach is the coupling of multi-criteria optimization with CALPHAD-based models of alloy properties. While this technique has already yielded promising new Nickel-base superalloys, the applicability to CoNi-base alloys has not yet been investigated. These alloys show promising properties for application as wrought high-temperature materials. We designed three CoNi-base superalloys, which were optimized for either high strength or high chemical homogeneity. The alloys were cast, and mechanical and thermophysical properties were characterized. The alloy optimized for strength showed creep performance inferior to a conventionally designed CoNi-alloy but had a much lower density. For developing highly homogeneous alloys, Scheil calculations were implemented in the optimization routine to quantify the severity of segregation. Non-equilibrium phases could be predicted successfully, resulting in a degree of homogeneity that rivaled that of a low-segregation ternary Co-base alloy. A comparison of elemental partitioning behavior and phase transition temperatures with CALPHAD calculations showed that trends are well represented for the most part. Finally, the applicability of the alloy design approach for Co-rich superalloys is evaluated, and possible applications for the optimized alloys are discussed.

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Design of Re-free nickel-base single crystal superalloys using modelling and experimental validations

Cited by 16 publications

References 42 publications

Alloy Design and Characterization of γ′ Strengthened Nickel-based Superalloys for Additive Manufacturing

Alloy Design and Characterization of γ′ Strengthened Nickel-based Superalloys for Additive Manufacturing

Machine learning predictions of superalloy microstructure

Numerical Design of CoNi-Base Superalloys With Improved Casting Structure

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