Effect of Ti and Ta content on the oxidation resistance of Co-Ni-based superalloys

Co-Ni-based superalloys are known for their capability to function at elevated temperatures and superior hot corrosion and thermal fatigue resistance.Therefore,these alloys show potential as crucial high-temperature structural materials for aeroengine and gas turbine hot-end components.Our previous work elucidated the influence of Ti and Ta on the high-temperature mechanical properties of alloys.However,the intricate interaction among elements considerably affects the oxidation resistance of alloys.In this paper,Co-35Ni-10Al-2W-5Cr-2Mo-1Nb-xTi-(5−x)Ta alloys(x=1,2,3,4)with varying Ti and Ta contents were designed and compounded,and their oxidation resistance was investigated at the temperature range from 800 to 1000℃.After oxidation at three test conditions,namely,800℃for 200 h,900℃for 200 h,and 1000℃for 50 h,the main structure of the oxide layer of the alloy consisted of spinel,Cr_(2)O_(3),and Al_(2)O_(3)from outside to inside.Oxides consisting of Ta,W,and Mo formed below the Cr_(2)O_(3)layer.The interaction of Ti and Ta imparted the highest oxidation resistance to 3Ti2Ta alloy.Conversely,an excessive amount of Ti or Ta resulted in an adverse effect on the oxidation resistance of the alloys.This study reports the volatilization of W and Mo oxides during the oxidation process of Co-Ni-based cast superalloys with a high Al content for the first time and explains the formation mechanism of holes in the oxide layer.The results provide a basis for gaining insights into the effects of the interaction of alloying elements on the oxidation resistance of the alloys they form.

Design of low-alloying and high-performance solid solution-strengthened copper alloys with element substitution for sustainable development

Solid solution-strengthened copper alloys have the advantages of a simple composition and manufacturing process,high mechanical and electrical comprehensive performances,and low cost;thus,they are widely used in high-speed rail contact wires,electronic component connectors,and other devices.Overcoming the contradiction between low alloying and high performance is an important challenge in the development of solid solution-strengthened copper alloys.Taking the typical solid solution-strengthened alloy Cu-4Zn-1Sn as the research object,we proposed using the element In to replace Zn and Sn to achieve low alloying in this work.Two new alloys,Cu-1.5Zn-1Sn-0.4In and Cu-1.5Zn-0.9Sn-0.6In,were designed and prepared.The total weight percentage content of alloying elements decreased by 43%and 41%,respectively,while the product of ultimate tensile strength(UTS)and electrical conductivity(EC)of the annealed state increased by 14%and 15%.After cold rolling with a 90%reduction,the UTS of the two new alloys reached 576 and 627MPa,respectively,the EC was 44.9%IACS and 42.0%IACS,and the product of UTS and EC(UTS×EC)was 97%and 99%higher than that of the annealed state alloy.The dislocations proliferated greatly in cold-rolled alloys,and the strengthening effects of dislocations reached 332 and 356 MPa,respectively,which is the main reason for the considerable improvement in mechanical properties.

Phase-field simulation of lack-of-fusion defect and grain growth during laser powder bed fusion of Inconel 718

The anisotropy of the structure and properties caused by the strong epitaxial growth of grains during laser powder bed fusion(L-PBF)significantly affects the mechanical performance of Inconel 718 alloy components such as turbine disks.The defects(lack-of-fusion Lo F)in components processed via L-PBF are detrimental to the strength of the alloy.The purpose of this study is to investigate the effect of laser scanning parameters on the epitaxial grain growth and LoF formation in order to obtain the parameter space in which the microstructure is refined and LoF defect is suppressed.The temperature field of the molten pool and the epitaxial grain growth are simulated using a multiscale model combining the finite element method with the phase-field method.The LoF model is proposed to predict the formation of LoF defects resulting from insufficient melting during L-PBF.Defect mitigation and grain-structure control during L-PBF can be realized simultaneously in the model.The simulation shows the input laser energy density for the as-deposited structure with fine grains and without LoF defects varied from 55.0–62.5 J·mm^(-3)when the interlayer rotation angle was 0°–90°.The optimized process parameters(laser power of 280 W,scanning speed of 1160 mm·s^(-1),and rotation angle of 67°)were computationally screened.In these conditions,the average grain size was 7.0μm,and the ultimate tensile strength and yield strength at room temperature were(1111±3)MPa and(820±7)MPa,respectively,which is 8.8%and10.5%higher than those of reported.The results indicating the proposed multiscale computational approach for predicting grain growth and Lo F defects could allow simultaneous grain-structure control and defect mitigation during L-PBF.

Erratum to:Phase-field simulation of lack-of-fusion defect and grain growth during laser powder bed fusion of Inconel 718

The original online version of this article unfortunately contained a mistake.The reference[24]in the original online version is incorrect.The correct version is given below:Y.H.Cheng,Numerical Simulation and Experimental Research of Selective Laser Melting on Nickel Based Alloy Powder GH4169[Dissertation],North University of China,Taiyuan,2016.

Fabrication of pure copper rods containing continuous columnar crystals by continuous unidirectional solidification technology

Pure copper rods containing continuous columnar crystals were fabricatedusing the downward CUS (Continuous Unidirectional Solidification) equipment. When the technologicalparameters were set as the ranges of mould temperature 1100-1300℃, cooling distance (the distancefrom the exit of the cast mould to the start point of cooling) 10-20 mm, casting speed 0.2-2.5 mm/s,cooling water (20-25℃) volume 1000-1320 L/h, and when these parameters matched reasonably, the CUSprocess was performed stably, and pure copper rods containing continuous columnar crystals withbright and smooth surface were produced. The dendritic arm spacing of the crystals in copper rodsdecreased with increasing the casting speed. The results of the texture by X-ray diffractionanalysis showed that the rods has strong <100> fiber texture.

Editorial for Special Issue on Materials Genome Engineering

With the rapid advancement of computing and information technology at the turn of the 21st century,the power of data collection and processing has multiplied tremendously.Based on this a game-changing advancement,science is at the advent of the “fourth paradigm”of massive data plus artificial intelligence,in which the efficiency of scientific research is continuously improved,research time is shortened,and research cost is reduced[1].

基于线上资源和平台的在线教学实践——以电化学基础课程教学为例

为应对新型冠状病毒肺炎疫情,教育部提出“停课不停教、停课不停学”指导意见,各高校积极响应。本文分析了地方高校开展在线教学时存在的问题:教学形式单一、教师在线教学技能参差不齐、教学观念有待转变及学生线上学习能力不足,分别从学校、教师及学生层面阐述在线教学质量提升策略,并以电化学基础课程教学为例,详细介绍线上资源和平台在教学实践中的具体应用及所取得的成效,为各高校教师利用在线资源因地制宜、因时制宜开展在线教学提供参考。

A Vision of Materials Genome Engineering in China

In the new round of global scientific and technological revolution and industrial transformation,one of the biggest bottlenecks we are facing is the lack of adequate materials technology.It is urgently hoped that there can be a major revolution in the materials research and development(R&D)field,rather than gradual and slow development[1].Under this background,materials genome engineering(MGE)has been developing rapidly,and its key technologies and engineering applications have become the frontiers of materials research[2].

Recent progress in the machine learning-assisted rational design of alloys

Alloys designed with the traditional trial and error method have encountered several problems,such as long trial cycles and high costs.The rapid development of big data and artificial intelligence provides a new path for the efficient development of metallic materials,that is,machine learning-assisted design.In this paper,the basic strategy for the machine learning-assisted rational design of alloys was introduced.Research progress in the property-oriented reversal design of alloy composition,the screening design of alloy composition based on models established using element physical and chemical features or microstructure factors,and the optimal design of alloy composition and process parameters based on iterative feedback optimization was reviewed.Results showed the great advantages of machine learning,including high efficiency and low cost.Future development trends for the machine learning-assisted rational design of alloys were also discussed.Interpretable modeling,integrated modeling,high-throughput combination,multi-objective optimization,and innovative platform building were suggested as fields of great interest.

The effect of far field flow on a spherical crystal growth in the undercooled melt

The effect of convective flow on a spherical crystal growth in the undercooled melt with a moderate far field flow is studied. The asymptotic solution of the evolution of the interface of the spherical crystal growth is obtained by the matched asymptotic expansion method. The analytic result shows that the convective flow in the undercooled melt has a strong effect on the evolution of spherical crystal growth. The convective flow induced by the far field flow makes the interface of the growing spherical crystal enhance its growth velocity in the upstream direction of the far field flow and inhibit growth in the downstream direction, and the interface of the decaying spherical crystal further decay in the upstream direction and inhibit decay in the downstream direction. The maximum growth velocity of the interface of the spherical crystal influenced by the far field flow is obtained.

Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu-Ni-Co-Si-X alloy via Bayesian optimization machine learning

It is difficult to rapidly design the process parameters of copper alloys by using the traditional trial-and-error method and simultaneously improve the conflicting mechanical and electrical properties.The purpose of this work is to develop a new type of Cu-Ni-Co-Si alloy saving scarce and expensive Co element,in which the Co content is less than half of the lower limit in ASTM standard C70350 alloy,while the properties are as the same level as C70350 alloy.Here we adopted a strategy combining Bayesian optimization machine learning and experimental iteration and quickly designed the secondary deformation-aging parameters(cold rolling deformation 90%,aging temperature 450℃,and aging time 1.25 h)of the new copper alloy with only 32 experiments(27 basic sample data acquisition experiments and 5 iteration experiments),which broke through the barrier of low efficiency and high cost of trial-and-error design of deformation-aging parameters in precipitation strengthened copper alloy.The experimental hardness,tensile strength,and electrical conductivity of the new copper alloy are HV(285±4),(872±3)MPa,and(44.2±0.7)%IACS(international annealed copper standard),reaching the property level of the commercial lead frame C70350 alloy.This work provides a new idea for the rapid design of material process parameters and the simultaneous improvement of mechanical and electrical properties.

有机化学实验混合式教学研究与实践--以“乙醚的制备”为例

围绕完善优课,打造"金课"的理念,推进混合式教学在有机化学实验教学中的应用与实践,以乙醚的制备为例,详细阐述该教学模式的具体实施过程。教学实践说明混合式教学辅以实验的创新设计,不仅可以提高学生课前的自学成效,激发学生学习的探索欲,而且还能促进教师教学水平的提升,提高教学的创新性,可在实验教学中推广。

Microstructure and properties evolution of in-situ fiber-reinforced Ag-Cu-Ni-Ce alloy during deformation and heat treatment

Silver-based alloys are significant light-load electrical contact materials(ECMs).The trade-off between mechanical properties and electrical conductivity is always an important issue for the development of silver-based ECMs.In this paper,we proposed an idea for the regulation of the mechanical properties and the electrical conductivity of Ag-11.40Cu-0.66Ni-0.05Ce(wt%)alloy using in-situ composite fiber-reinforcement.The alloy was processed using rolling,heat treatment,and heavy drawing,the strength and electrical conductivity were tested at different deformation stages,and the microstructures during deformation were observed using field emission scanning electron microscope(FESEM),transmission electron microscope(TEM)and electron backscatter diffraction(EBSD).The results show that the method proposed in this paper can achieve the preparation of in-situ composite fiber-reinforced Ag-Cu-Ni-Ce alloys.After the heavy deformation drawing,the room temperature Vickers hardness of the as-cast alloy increased from HV 81.6 to HV 169.3,and the electrical conductivity improved from 74.3%IACS(IACS,i.e.,international annealed copper standard)to 78.6%IACS.As the deformation increases,the alloy strength displays two different strengthening mechanisms,and the electrical conductivity has three stages of change.This research provides a new idea for the comprehensive performance control of high-performance silver-based ECMs.

Flow field simulation of the thin metal ribbon formed by twin-belt rapid solidification

The flow field formed when an aluminum ribbon is produced by twin-belt rapid solidification is simulated with the solution algorithm （SOLA）-volume of fluid （VOF） method, and the influence of casting speed and cooling-belt velocity on the flow field is analyzed for a given ejection nozzle size. It has been found that the flow field can be filled well when the width of the ejection nozzle outlet is the same as that of the up slit of the cooling belt. When the ejection nozzle outlet is wider than the up slit of the cooling belt, the aluminum fluid can fill well if only the casting speed matches the cooling-belt velocity properly, otherwise under filling occurs in some areas in the cooling zone.

Alloy synthesis and processing by semi-supervised text mining

Alloy synthesis and processing determine the design of alloys with desired microstructure and properties.However,using data science to identify optimal synthesis-design routes from a specified set of starting materials has been limited by large-scale data acquisition.Text mining has made it possible to convert scientific text into structured data collections.Still,the complexity,diversity,and flexibility of synthesis and processing expressions,and the lack of annotated corpora with a gold standard severely hinder accurate and efficient extraction.Here we introduce a semi-supervised text mining method to extract the parameters corresponding to the sequence of actions of synthesis and processing.We automatically extract a total of 9853 superalloy synthesis and processing actions with chemical compositions from a corpus of 16,604 superalloy articles published up to 2022.These have then been used to capture an explicitly expressed synthesis factor for predictingγ′phase coarsening.The synthesis factor derived from text mining significantly improves the performance of the data-drivenγ′size prediction model.The method thus complements the use of data-driven approaches in the search for relationships between synthesis and structures.

A rapid and effective method for alloy materials design via sample data transfer machine learning

One of the challenges in material design is to rapidly develop new materials or improve the performance of materials by utilizing the data and knowledge of existing materials.Here,a rapid and effective method of alloy material design via data transfer learning is proposed to efficiently design new alloys using existing data.A new type of aluminum alloy(E2 alloy)with ultra strength and high toughness previously developed by the authors is used as an example.An optimal three-stage solution-aging treatment process(T66R)was efficiently designed transferring 1053 pieces of process-property relationship data of existing AA7xxx commercial aluminum alloys.It realizes the substantial improvement of strength and plasticity of E2 alloy simultaneously,which is of great significance for lightweight of high-end equipment.Meanwhile,the microstructure analysis clarifies the mechanism of alloy performance improvement.This study shows that transferring the existing alloy data is an effective method to design new alloys.

Alloy design for laser powder bed fusion additive manufacturing:a critical review

Metal additive manufacturing(AM)has been extensively studied in recent decades.Despite the significant progress achieved in manufacturing complex shapes and structures,challenges such as severe cracking when using existing alloys for laser powder bed fusion(L-PBF)AM have persisted.These challenges arise because commercial alloys are primarily designed for conventional casting or forging processes,overlooking the fast cooling rates,steep temperature gradients and multiple thermal cycles of L-PBF.To address this,there is an urgent need to develop novel alloys specifically tailored for L-PBF technologies.This review provides a comprehensive summary of the strategies employed in alloy design for L-PBF.It aims to guide future research on designing novel alloys dedicated to L-PBF instead of adapting existing alloys.The review begins by discussing the features of the L-PBF processes,focusing on rapid solidification and intrinsic heat treatment.Next,the printability of the four main existing alloys(Fe-,Ni-,Al-and Ti-based alloys)is critically assessed,with a comparison of their conventional weldability.It was found that the weldability criteria are not always applicable in estimating printability.Furthermore,the review presents recent advances in alloy development and associated strategies,categorizing them into crack mitigation-oriented,microstructure manipulation-oriented and machine learning-assisted approaches.Lastly,an outlook and suggestions are given to highlight the issues that need to be addressed in future work.

Discovery of aluminum alloys with ultra-strength and high-toughness via a property-oriented design strategy

Aluminum alloys with ultra-strength and high-toughness are fundamental structural materials applied in the aerospace industry.Due to the intrinsic restriction between strength and toughness,optimizing a desirable combination of these conflicting properties is always challenging in material development.In this study,171 sets of data were curated based on the characteristics of high-strength and high-toughness aluminum alloys in the literature.Then,a machine learning design system(MLDS)with a property-oriented design strategy was established to rapidly discover novel aluminum alloys with ductility and toughness indexes(with elongationδ=8%–10%and fracture toughness K_(IC)=33–35 MPa·m^(1/2))comparable to those of current state-of-the-art AA7136 aluminum alloys when the ultimate tensile strength(UTS)exceeded approximately 100 MPa,with values reaching 700–750 MPa.With the MLDS for experimental verification,three typical candidate alloys show satisfactory performance with UTS of 707–736 MPa,δof 7.8%–9.5%,and K_(IC)of 32.2–33.9 MPa·m^(1/2).The high contents of Mg and Zn alloying elements in the novel alloys form abundantη'phases,which produce a significant hardening effect,while the reasonable matching of Cr,Mn,Ti and Zr dispersoids refines the grain size.The decreased Cu content compared with that in the AA7136 alloy inhibits the formation of theσphase and S phase,so that the alloys show high toughness.

A property-oriented design strategy for high performance copper alloys via machine learning

Traditional strategies for designing new materials with targeted property including methods such as trial and error,and experiences of domain experts,are time and cost consuming.In the present study,we propose a machine learning design system involving three features of machine learning modeling,compositional design and property prediction,which can accelerate the discovery of new materials.We demonstrate better efficiency of on a rapid compositional design of high-performance copper alloys with a targeted ultimate tensile strength of 600–950 MPa and an electrical conductivity of 50.0%international annealed copper standard.There exists a good consistency between the predicted and measured values for three alloys from literatures and two newly made alloys with designed compositions.Our results provide a new recipe to realize the property-oriented compositional design for highperformance complex alloys via machine learning.

Automated pipeline for superalloy data by text mining

Data provides a foundation for machine learning,which has accelerated data-driven materials design.The scientific literature contains a large amount of high-quality,reliable data,and automatically extracting data from the literature continues to be a challenge.We propose a natural language processing pipeline to capture both chemical composition and property data that allows analysis and prediction of superalloys.Within 3 h,2531 records with both composition and property are extracted from 14,425 articles,coveringγ′solvus temperature,density,solidus,and liquidus temperatures.A data-driven model forγ′solvus temperature is built to predict unexplored Co-based superalloys with highγ′solvus temperatures within a relative error of 0.81%.We test the predictions via synthesis and characterization of three alloys.A web-based toolkit as an online open-source platform is provided and expected to serve as the basis for a general method to search for targeted materials using data extracted from the literature.