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.

有机硅改性酚醛气凝胶的制备及隔热疏水性能

酚醛(PR)气凝胶具有低密度和低导热等突出优点,广泛用作隔热、保温材料。然而,传统的PR气凝胶制备工艺复杂,且与生俱来地存在易吸湿的弊端。文中以线型酚醛树脂为原料、辛基三乙氧基硅烷(OTES)为疏水剂,分别通过化学接枝法和物理共混法制备了硅改性酚醛树脂,进一步借助溶胶-凝胶和常压干燥工艺制备了硅改性酚醛气凝胶。采用扫描电子显微镜和能谱分析、N2吸附-脱附实验、同步热分析、热常数和疏水角测量对气凝胶进行表征,并通过水浸泡实验评估其吸水性能。所制备的试样密度在0.205~0.260 g/cm~3、热导率在0.040~0.044 W/(m·K)。研究表明,引入OTES在不同程度上改善了PR气凝胶的热稳定性、疏水性和隔热性能,且化学接枝法的综合表现优于物理共混法。物理共混改性使气凝胶的高温残重率提高约5%,疏水角提高约121.0°,在水中的饱和吸湿率为59.6%(改性前高达349.5%);而通过化学接枝路线添加少量的OTES即可大幅度提升气凝胶性能,残重率增加约10%、疏水角达153.4°、饱和吸湿率仅19.6%。

酚醛树脂气凝胶的溶胶-凝胶法制备及合成机理

以商业酚醛树脂为原料,介绍了一种工艺简单、制备周期短、成本低的低温溶胶-凝胶聚合和常压干燥工艺制备酚醛树脂气凝胶的方法。使用3种酚醛树脂(低黏度钡酚醛、普通热塑性酚醛和硼酚醛)在醇溶剂中制备了3种系列的凝胶。采用扫描电子显微镜、比表面积及孔隙率分析仪和红外光谱分析了所制备气凝胶的骨架结构和孔结构及合成机理。所制备的多孔纳米酚醛树脂气凝胶密度低至0.17~0.23 g/cm^(3)、压缩强度为0.68~0.80 MPa,热导率为0.039~0.046 W/(m·K)。研究表明,酚醛树脂与醇溶剂之间有较强的选择匹配性,轻质多孔的钡酚醛树脂气凝胶只能在乙醇中合成,热塑性酚醛树脂在正丙醇溶剂中得到的气凝胶结构更均匀,而硼酚醛树脂由于固化温度过高,难以通过该方法获得气凝胶材料。这种低成本、简捷的气凝胶制备工艺对实现酚醛树脂气凝胶的大尺寸制备和产业化发展具有重要意义。

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.

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.

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.

Gyroid Triply Periodic Minimal Surface Lattice Structure Enables Improved Superelasticity of CuAlMn Shape Memory Alloy

Improving the shape memory effect and superelasticity of Cu-based shape memory alloys(SMAs)has always been a research hotspot in many countries.This work systematically investigates the effects of Gyroid triply periodic minimal surface(TPMS)lattice structures with different unit sizes and volume fractions on the manufacturing viability,compressive mechanical response,superelasticity and heating recovery properties of CuAlMn SMAs.The results show that the increased specific surface area of the lattice structure leads to increased powder adhesion,making the manufacturability proportional to the unit size and volume fraction.The compressive response of the CuAlMn SMAs Gyroid TPMS lattice structure is negatively correlated with the unit size and positively correlated with the volume fraction.The superelastic recovery of all CuAlMn SMAs with Gyroid TPMS lattice structures is within 5%when the cyclic cumulative strain is set to be 10%.The lattice structure shows the maximum superelasticity when the unit size is 3.00 mm and the volume fraction is 12%,and after heating recovery,the total recovery strain increases as the volume fraction increases.This study introduces a new strategy to enhance the superelastic properties and expand the applications of CuAlMn SMAs in soft robotics,medical equipment,aerospace and other fields.

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.

A general and transferable deep learning framework for predicting phase formation in materials

Machine learning has been widely exploited in developing new materials.However,challenges still exist:small dataset is common for most tasks;new datasets,special descriptors and specific models need to be built from scratch when facing a new task;knowledge cannot be readily transferred between independent models.In this paper we propose a general and transferable deep learning(GTDL)framework for predicting phase formation in materials.The proposed GTDL framework maps raw data to pseudoimages with some special 2-D structure,e.g.,periodic table,automatically extracts features and gains knowledge through convolutional neural network,and then transfers knowledge by sharing features extractors between models.Application of the GTDL framework in case studies on glass-forming ability and high-entropy alloys show that the GTDL framework for glass-forming ability outperformed previous models and can correctly predicted the newly reported amorphous alloy systems;for high-entropy alloys the GTDL framework can discriminate five types phases(BCC,FCC,HCP,amorphous,mixture)with accuracy and recall above 94%in fivefold cross-validation.In addition,periodic table knowledge embedded in data representations and knowledge shared between models is beneficial for tasks with small dataset.This method can be easily applied to new materials development with small dataset by reusing well-trained models for related materials.

Co-guiding the dendrite-free plating of lithium on lithiophilic ZnO and fluoride modified 3D porous copper for stable Li metal anode

Lithium metal battery is considered to be the most promising energy storage technologies due to its ultra-high theoretical capacity and extremely low standard potential.However,the infinite volume change during uneven deposition/dissolution process and the growth of lithium dendrite resulting in severe capacity decay and high safety hazards,which hinders the application in next generation secondary batteries.In this paper,the three dimensional(3D)porous copper is prepared through an electrochemical etching CueZn alloy,and the pore walls are modified with lithiophilic layer of ZnO and fluorine.The as-prepared 3D Cu/ZnO/F can inhibit the growth of Li dendrite and mitigate the huge volume change of Li metal anode during cycling process,resulting in stable solid electrolyte interface(SEI)layer and electrode structure.The Li|3D Cu/ZnO/F cell can be stably cycled over 300 cycles with 98% of coulomb efficiency at 0.5 mA cm^(-2),1 mAh cm^(-2).The synergistic effects of both ZnO and fluorine on inducing the uniform deposition of lithium by providing bonding sites can inhibit the generation of lithium dendrites and thus improve the electrochemical performance of lithium metal batteries.

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.