Data-Driven Design of Cast Nickel-Based Superalloy and Precision Forming of Complex Castings

doi:10.11900/0412.1961.2021.00355

Acta Metall Sin

2022, Vol. 58

Issue (1): 89-102 DOI: 10.11900/0412.1961.2021.00355

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Data-Driven Design of Cast Nickel-Based Superalloy and Precision Forming of Complex Castings

WANG Donghong¹, SUN Feng¹^,²(

), SHU Da¹^,²(

), CHEN Jingyang³, XIAO Chengbo³, SUN Baode¹^,²

^1. Shanghai Key Lab of Advanced High-Temperature Materials and Precision Forming and State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
^2. Materials Genome Initiative Center, Shanghai Jiao Tong University, Shanghai 200240, China
^3. Science and Technology on Advanced High Temperature Structural Materials Laboratory, AECC Beijing Institute of Aeronautical Materials, Beijing 100095, China

Cite this article:

WANG Donghong, SUN Feng, SHU Da, CHEN Jingyang, XIAO Chengbo, SUN Baode. Data-Driven Design of Cast Nickel-Based Superalloy and Precision Forming of Complex Castings. Acta Metall Sin, 2022, 58(1): 89-102.

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Abstract

The development of material genomics engineering and intelligent material-processing technology provides new ideas for researching, developing, and manufacturing key thermal superalloy components of aeroengines. Based on the demand for superalloy materials and casting processing, a high-throughput dynamic simulation software system was developed. Combined with the screening criteria of nickel-based casting superalloy, a new nickel-based casting superalloy was selected and developed from more than 5.2 million-component combinations. High-temperature durability at 815^oC and 400 MPa is better than foreign Inconel 939 superalloy. For the precision molding of complex superalloy casting, the data-driven process of the casting deformation is integrated, which reveals the correlation between the process parameters and size precision during solidification deformation. Thus, a data-driven process parameter optimization method is proposed herein. A data-driven casting outlet design method based on the model and algorithm, combined with the test design and multi-target genetic algorithm, which optimized the casting process parameters, was established, and the production rate of the casting process increased by 13.39% after the test verification. The combination of data-driven component design and data model-based process design will accelerate the development and application of aviation materials and components.

Key words: high throughput composition design superalloy data driven intelligent casting

Received: 23 August 2021

ZTFLH:

TG146.3

Fund: National Science and Technology Major Project of China(J2019-VI-0004-0117);National Natural Science Foundation of China(51821001);National Key Research and Development Program of China(2016YFB0701405);Fund of the Science and Technology on Advanced High Temperature Structural Materials Laboratory(6142903200105)

About author: SUN Feng, associate professor, Tel: (021)54748974, E-mail: fsun@sjtu.edu.cn

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	Donghong WANG
	Feng SUN
	Da SHU
	Jingyang CHEN
	Chengbo XIAO
	Baode SUN

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https://www.ams.org.cn/EN/10.11900/0412.1961.2021.00355 OR https://www.ams.org.cn/EN/Y2022/V58/I1/89

Fig.1 Framework of thermodynamic/kinetic simulation software for novel cast nickel-based superalloys

Fig.2 Typical property diagram and illustrated feature parameters (T _m—melting point, TCP—topologically closed-packed, ΔT—solidification temperature range)

Table 1 Composition range for high-throughput computation

Fig.3 Integrated computational materials engineering (ICME) for casting design (CAD—computer aided design, PVT—pressure-specific volume-temperature )

Fig.4 Data-driven framework for prediction of distortion of investment casting (PA—pattern allowance, RSM—response surface methodology, RBF—radial basis function, DOE—design of experiment, AlloyTemp and ShellTemp—temperatures of alloy and shell)

Table 2 Design variables for dimensional error simulation of wax pattern and each range

Table 3 Design variables for solidification deformation simulation and each range

Fig.5 Data-driven framework for prediction of porosity and process design in investment casting (HTC—heat transfer coefficient)

Table 4 Design variables for prediction of porosity and each range

Fig.6 Master alloy ingot (a), testing specimens (b), and shape and dimensions of a typical casting (unit: mm) (c)

Table 5 Screened basic alloy compositions

Fig.7 Creep performance of screened alloys and several typical cast superalloys (evaluated by JMatPro)

Fig.8 SEM images of Alloy 1 (a, b) and Alloy 2 (c, d)
(a, c) as-cast (b, d) after solid solution

Fig.9 Interdendritic eutectics of Alloy 1 (a) and Alloy 2 (b), and EDS analyses (atomic fraction, %) of γ/γ' eutectics (c) and MC carbides of Alloy 1 (d) and Alloy 2 (e)

Fig.10 Typical mechanical property curves of SJTU-1 alloy (R _m —ultimate tensile strength, R _p0.2 —yield strength, A—elongation)
(a) tensile tests at room temperature (RT) and 815^oC
(b) creep at 815^oC and 400 MPa

Fig.11 Optimization results of the injection process of wax pattern (unit: mm)(a) optimization process (b) minimum warpage optimization

Fig.12 The relation matrix among the process parameters of wax pattern

Fig.13 Two-dimensional response surface model among injection process parameters of wax pattern
(a) volume shrinkage response surface of injection speed combine with packing time
(b) volume shrinkage response surface of injection temperature combine with packing time
(c) volume shrinkage response surface of injection speed combine with packing pressure
(d) warpage response surface of injection speed combine with packing time
(e) warpage response surface of injection temperature combine with packing time
(f) warpage response surface of injection speed combine with packing time

Fig.14 The effects of the inputs on the outputs of dimension quality of casting (D_Ave—aveage diameter)

Fig.15 2D interaction effects of the process parameters for average diameter and ovality
(a) average diameter response surface of shell temperature combine with alloy temperature
(b) average diameter response surface of PA combine with shell temperature
(c) average diameter response surface of alloy temperature combine with PA
(d) ovality response surface of shell temperature combine with alloy temperature
(e) ovality response surface of PA combine with shell temperature
(f) ovality response surface of alloy temperature combine with PA

Fig.16 3D interaction effects of the inputs for average diameter (a~c) and ovality of investment casting (d~f)

Fig.17 Effects of the input on the casting yield and safe margin of riser

Fig.18 The measured specimens
(a, c) cross-sectional morphologies of the riser by optimal solution (a) and modulus method (c)
(b) castings by the two methods

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