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.

SELECTING CLUSTER MODEL IN Sn - BASED SOLDER ALLOY DESIGN WITH DV - X_α CALCULATION METHOD

Applying calculation method in alloy design should be an important tendency due to its characters of inexpensive cost, high efficiency and prediction. DOS calculations of AuSn, AsSn and SbSn Sn- based alloys have been investigated by employing DV - Xa method, in which different cluster models were adopted to calculate electron structure.It is proved that some regulations must be taken into ac- count in order to carry out alloy design calculation successfully,which are described in this paper in detail.

Accelerated design of high-performance Mg-Mn-based magnesium alloys based on novel bayesian optimization

Magnesium(Mg),being the lightest structural metal,holds immense potential for widespread applications in various fields.The development of high-performance and cost-effective Mg alloys is crucial to further advancing their commercial utilization.With the rapid advancement of machine learning(ML)technology in recent years,the“data-driven''approach for alloy design has provided new perspectives and opportunities for enhancing the performance of Mg alloys.This paper introduces a novel regression-based Bayesian optimization active learning model(RBOALM)for the development of high-performance Mg-Mn-based wrought alloys.RBOALM employs active learning to automatically explore optimal alloy compositions and process parameters within predefined ranges,facilitating the discovery of superior alloy combinations.This model further integrates pre-established regression models as surrogate functions in Bayesian optimization,significantly enhancing the precision of the design process.Leveraging RBOALM,several new high-performance alloys have been successfully designed and prepared.Notably,after mechanical property testing of the designed alloys,the Mg-2.1Zn-2.0Mn-0.5Sn-0.1Ca alloy demonstrates exceptional mechanical properties,including an ultimate tensile strength of 406 MPa,a yield strength of 287 MPa,and a 23%fracture elongation.Furthermore,the Mg-2.7Mn-0.5Al-0.1Ca alloy exhibits an ultimate tensile strength of 211 MPa,coupled with a remarkable 41%fracture elongation.

Relationship between the unique microstructures and behaviors of high-entropy alloys

High-entropy alloys(HEAs),which were introduced as a pioneering concept in 2004,have captured the keen interest of nu-merous researchers.Entropy,in this context,can be perceived as representing disorder and randomness.By contrast,elemental composi-tions within alloy systems occupy specific structural sites in space,a concept referred to as structure.In accordance with Shannon entropy,structure is analogous to information.Generally,the arrangement of atoms within a material,termed its structure,plays a pivotal role in dictating its properties.In addition to expanding the array of options for alloy composites,HEAs afford ample opportunities for diverse structural designs.The profound influence of distinct structural features on the exceptional behaviors of alloys is underscored by numer-ous examples.These features include remarkably high fracture strength with excellent ductility,antiballistic capability,exceptional radi-ation resistance,and corrosion resistance.In this paper,we delve into various unique material structures and properties while elucidating the intricate relationship between structure and performance.

A machine learning approach for accelerated design of magnesium alloys. Part A:Alloy data and property space

Typically, magnesium alloys have been designed using a so-called hill-climbing approach, with rather incremental advances over the past century. Iterative and incremental alloy design is slow and expensive, but more importantly it does not harness all the data that exists in the field. In this work, a new approach is proposed that utilises data science and provides a detailed understanding of the data that exists in the field of Mg-alloy design to date. In this approach, first a consolidated alloy database that incorporates 916 datapoints was developed from the literature and experimental work. To analyse the characteristics of the database, alloying and thermomechanical processing effects on mechanical properties were explored via composition-process-property matrices. An unsupervised machine learning(ML) method of clustering was also implemented, using unlabelled data, with the aim of revealing potentially useful information for an alloy representation space of low dimensionality. In addition, the alloy database was correlated to thermodynamically stable secondary phases to further understand the relationships between microstructure and mechanical properties. This work not only introduces an invaluable open-source database, but it also provides, for the first-time data, insights that enable future accelerated digital Mg-alloy design.

A machine learning approach for accelerated design of magnesium alloys.Part B: Regression and property prediction

Machine learning(ML) models provide great opportunities to accelerate novel material development, offering a virtual alternative to laborious and resource-intensive empirical methods. In this work, the second of a two-part study, an ML approach is presented that offers accelerated digital design of Mg alloys. A systematic evaluation of four ML regression algorithms was explored to rationalise the complex relationships in Mg-alloy data and to capture the composition-processing-property patterns. Cross-validation and hold-out set validation techniques were utilised for unbiased estimation of model performance. Using atomic and thermodynamic properties of the alloys, feature augmentation was examined to define the most descriptive representation spaces for the alloy data. Additionally, a graphical user interface(GUI) webtool was developed to facilitate the use of the proposed models in predicting the mechanical properties of new Mg alloys. The results demonstrate that random forest regression model and neural network are robust models for predicting the ultimate tensile strength and ductility of Mg alloys, with accuracies of ~80% and 70% respectively. The developed models in this work are a step towards high-throughput screening of novel candidates for target mechanical properties and provide ML-guided alloy design.

Rational design, synthesis and prospect of biodegradable magnesium alloy vascular stents

Biodegradable magnesium(Mg) alloys are expected to be promising materials for cardiovascular stents(CVS), which can avoid the longterm clinical problems of current CVS, such as in-stent restenosis, late stent thrombosis, etc. Mg alloy stents exhibit superior biocompatibility and tunable biodegradability, compared with conventional permanent metallic stents. However, the poor formability and non-uniform corrosion of Mg alloy stents hinder their clinical application of CVS. This review focuses on the development of Mg alloys for CVS in recent years.According to the results of bibliometric analysis, we analyzed different biodegradable Mg alloy systems. Moreover, the structural design strategies for Mg alloy stents that can reduce the stress concentration, as well as the surface modification methods to control the corrosion behavior and biological performance of Mg alloy stents are also highlighted. At last, this review systematically discussed the potential directions and challenges of biodegradable magnesium stents(BMgS) in cardiovascular fields.

Design of a low-alloy high-strength and high-toughness martensitic steel

To develop a high strength low alloy （HSLA） steel with high strength and high toughness, a series of martensitic steels were studied through alloying with various elements and thermodynamic simulation. The microstructure and mechanical properties of the designed steel were investigated by optical microscopy, scanning electron microscopy, tensile testing and Charpy impact test. The results show that cementite exists between 500℃ and 700℃, M7C3 exits below 720℃, and they are much lower than the austenitizing temperature of the designed steel. Furthermore, the Ti（C,N） precipitate exists until 1280℃, which refines the microstructure and increases the strength and toughness. The optimal alloying components are 0.19% C, 1.19% Si, 2.83% Mn, 1.24% Ni, and 0.049% Ti; the tensile strength and the V notch impact toughness of the designed steel are more than 1500 MPa and 100 J, respectively.

Efficient alloy design of Sr-modified A356 alloys driven by computational thermodynamics and machine learning

A356 alloys are widely used in industries due to their excellent comprehensive performance.Sr is usually added in A356 alloys to improve their mechanical properties.There have been various experimental reports on the optimal additional amount of Sr in A356 alloys,but their results are inevitably inconsistent.In this paper,a combination of computational thermodynamic and machine learning approaches was employed to determine the optimal Sr content in A356 alloys with the best mechanical properties.First,a self-consistent thermodynamic database of quaternary Al-Si-Mg-Sr system was established by means of the Calculation of PHAse Diagram technique supported by key experiments.Second,the fractions for solidified phase/structures of A356-xSr alloys predicted by Scheil simulation,together with the measured mechanical properties were set as the input dataset in the machine learning model to train the relation of“composition-microstructure-properties”.The optimal addition of Sr in A356 alloy was designed as 0.005 wt.%and validated by key experiments.Furthermore,such a combinatorial approach can help to understand the strengthening/toughening mechanisms of Sr-modified A356 alloys.It is also anticipated that the present approach may provide a feasible means for efficient and accurate design of various casting alloys and understanding the alloy strengthening/toughening mechanisms.

RBF-Type Artificial Neural Network Model Applied in Alloy Design of Steels

RBF model,a new type of artificial neural network model was developed to design the content of carbon in low-alloy engineering steels.The errors of the ANN model are：MSE 0.052 1,MSRE 17.85%,and VOF 1.932 9.The results obtained are satisfactory.The method is a powerful aid for designing new steels.

STUDY ON COMPOSITION DESIGN OF A SUPERALLOY

A new superalloy without Co, Ta and Hf has been developed. The high temperature creep properties of the alloy approach to that of Mar-M246 superallov and with good antioxidation and anticorrosion abilities.

Recent research and developments on wrought magnesium alloys

Wrought magnesium alloys attract special interests as lightweight structural material due to their homogeneous microstructure and enhanced mechanical properties compared to as-cast alloys.In this contribution,recent research and developments on wrought magnesium alloys are reviewed from the viewpoint of the alloy design,focusing on Mg-Al,Mg-Zn and Mg-rare earth(RE)systems.The effects of different alloying elements on the microstructure and mechanical properties are described considering their strengthening mechanisms,e.g.grain refinement,precipitation and texture hardening effect.Finally,the new alloy design and also the future research of wrought magnesium alloys to improve their mechanical properties are discussed.

Improved Fatigue and Damage Tolerant Material Design for Aerospace Industry

Various micro-mechanical and micro-structural influences on fatigue crack growth resistance of the material have been investigated over the years. It is widely recognized that resistance to fatigue crack growth can be differentiated into ‘intrinsic＇ and ‘extrinsic＇. The separation of intrinsic and extrinsic crack growth resistance has constituted a major theme of fatigue research in the last 30 years, with the concept of crack closure or crack tip shielding being used to rationalize a wide range of micro-structural and mechanical influences on fatigue crack growth behavior. An accurately quantitative understanding of intrinsic and extrinsic effects on crack growth is essential to directed alloy design for improved fatigue resistance, and/or improved structural service life. This paper presents a compliance-based crack closure measurement method and a multi-mechanism based analytical model for the separation of intrinsic and extrinsic material fatigue resistance, with application in characterizing the fatigue performance of two high strength damage tolerant airframe AI alloys.

EXPLOITATION AND APPLICATIONS OF METASTABLE AUSTENITE MATRIX WEAR ALLOYS

Fe based cast alloys with double phases structure of m etastable austenite m atrix an d eutecticcarbide M7 C3 were provided with the excellent properties of high abrasion resistance andhigher i m pact toughness . An i m portant reason of high abrasion resistance is hard ness violentincreasing on the m atrix surface because of w ear easily induced m artensite transfor m ation . The exploitation and applications of m etastable austenite m atrix wear alloys of Fe C Cr Nisyste m and Fe C Cr Mn system were described in this paper . The excellent properties of thesealloys w ill be sufficiently indicated by authors’exa m ples . To exploit a class of these alloyswith high abrasion resistance and various im pact toughness for m eeting the requirem ent of dif ferent environ ment , the proble m of the structure design of metastable austenite m atrix wearalloy w as also described in this paper .

Design biodegradable Zn alloys: Second phases and their significant influences on alloy properties

Alloying combined with plastic deformation processing is widely used to improve mechanical properties of pure Zn.As-cast Zn and its alloys are brittle.Beside plastic deformation processing,no effective method has yet been found to eliminate the brittleness and even endow room temperature super-ductility.Second phase,induced by alloying,not only largely determines the ability of plastic deformation,but also influences strength,corrosion rate and cytotoxicity.Controlling second phase is important for designing biodegradable Zn alloys.In this review,knowledge related to second phases in biodegradable Zn alloys has been analyzed and summarized,including characteristics of binary phase diagrams,volume fraction of second phase in function of atomic percentage of an alloying element,and so on.Controversies about second phases in Zn-Li,Zn-Cu and Zn-Fe systems have been settled down,which benefits future studies.The effects of alloying elements and second phases on microstructure,strength,ductility,corrosion rate and cytotoxicity have been neatly summarized.Mg,Mn,Li,Cu and Ag are recommended as the major alloying elements,owing to their prominent beneficial effects on at least one of the above properties.In future,synergistic effects of these elements should be more thoroughly investigated.For other nutritional elements,such as Fe and Ca,refining second phase is a matter of vital concern.

Thermal and microstructural characterization of a novel ductile cast iron modified by aluminum addition

In high-temperature applications,like exhaust manifolds,cast irons with a ferritic matrix are mostly used.However,the increasing demand for higher-temperature applications has led manufacturers to use additional expensive materials such as stainless steels and Ni-resist austenitic ductile cast irons.Thus,in order to meet the demand while using low-cost materials,new alloys with improved high-temperature strength and oxidation resistance must be developed.In this study,thermodynamic calculations with Thermo-Calc software were applied to study a novel ductile cast iron with a composition of 3.5wt%C,4wt%Si,1wt%Nb,0‒4wt%Al.The designed compositions were cast,and thermal analysis and microstructural characterization were performed to validate the calculations.The lowest critical temperature of austenite to pearlite eutectoid transformation,i.e.,A1,was calculated,and the solidification sequence was determined.Both calculations and experimental data revealed the importance of aluminum addition,as the A1 increased by increasing the aluminum content in the alloys,indicating the possibility of utilizing the alloys at higher temperature.The experimental data validated the transformation temperature during solidification and at the solid state and confirmed the equilibrium phases at room temperature as ferrite,graphite,and MC-type carbides.

Designing high-strength titanium alloy using pseudo-spinodal mechanism through diffusion multiple experiment and CALPHAD calculation

This study used the pseudo-spinodal mechanism to obtain the ultrafineαphase for designing highstrength titanium alloy.Diffusion multiple experiments were designed to find the composition range of TM-x Mo-y V alloy(TM:Ti-4.5 Al-2 Cr-2.5 Nb-2 Zr-1 Sn)for obtaining ultrafineαphase.CALPHAD results confirm that when the alloy composition is located near the intersection of theαandβphase free energy curves,the alloy will undergo pseudo-spinodal transformation and obtain the ultrafineαphase.The designed TM-6 Mo-3 V alloy has a yield strength of 1411 MPa and an elongation of 6.5%.The strength of the alloy depends on the thickness,fraction of theαphase and the solid solution strengthening effect of the alloying elements.The deformation mechanism of the alloy is the dislocation slip of theαandβphases and the twin deformation of theαphase.The large number ofα/βinterfaces produced by the fineαphase is the main reason for limiting the ductility of the alloy.The use of the pseudo-spinodal mechanism combined with diffusion multiple experiments and CALPHAD is an effective method for designing high-strength titanium alloys.

Design, strength prediction of Ti35NbxSnyZrzMo alloys with low elastic moduli and experimental verification on their mechanical properties

Based on the d-electron alloy design method and the average valence electron concentration theory,three kinds of metastable b-type Ti–35Nb-based alloys containing different quantities of Sn, Zr, and Mo alloying elements were designed, whose —Md = 2.45 e V and e/a = 4.24, and —Bo values = 2.869, 2.866, and 2.860,respectively. Meanwhile, a formula for predicting the solid solution strength of Ti–Nb-based alloys was developed using JMat Pro software. The experiments show that the designed alloys are all composed of single b phase after solid solution treatment, the tensile strength of the alloys coincides well with that predicted by the formula（the relative error ／4 %）, and the elastic moduli decrease with —Bo values increasing. The Ti35Nb3.7Zr1.3Mo alloy has the biggest —Bo value（2.869） and the lowest elastic modulus（54 GPa） among the three designed alloys.

cardiGAN:A generative adversarial network model for design and discovery of multi principal element alloys

Multi-principal element alloys(MPEAs),inclusive of high entropy alloys(HEAs),continue to attract significant research attention owing to their potentially desirable properties.Although MPEAs remain under extensive research,traditional(i.e.empirical)alloy production and testing are both costly and timeconsuming,partly due to the inefficiency of the early discovery process which involves experiments on a large number of alloy compositions.It is intuitive to apply machine learning in the discovery of this novel class of materials,of which only a small number of potential alloys have been probed to date.In this work,a proof-of-concept is proposed,combining generative adversarial networks(GANs)with discriminative neural networks(NNs),to accelerate the exploration of novel MPEAs.By applying the GAN model herein,it was possible to directly generate novel compositions for MPEAs,and to predict their phases.To verify the predictability of the model,alloys designed by the model are presented and a candidate produced-as validation.This suggests that the model herein offers an approach that can significantly enhance the capacity and efficiency of development of novel MPEAs.

Elemental partitioning as a route to design precipitation-hardened high entropy alloys

Precipitation-hardened high entropy alloys(HEAs)with carefully tuned compositions have shown excellent mechanical properties,demonstrating great potential for engineering applications.However,due to the lack of precise multiple phase diagrams,the composition design of multi-principal-component HEAs still inevitably relies on the extremely time-consuming trial-and-error approach.The present study,on the basis of powerful composition quantification ability of atom probe tomography(APT)technology,proposed a framework to guide the quantitative design of precipitation-hardened HEAs.In this framework,the elemental partitioning was used as a crucial route to avoid the thermodynamic challenge of designing precipitation-hardened HEAs.As a case study,the role of Ti/Al ratio in the design ofγ-γ’HEAs was predicted through the proposed framework and then validated by experimental studies.The framework predicted that when the total content of Ti and Al is fixed,a higher Ti/Al ratio makesγ-γ’HEA stronger.APT and mechanical results agreed well with these predictions and validated the feasibility of the framework.These findings provided a new route to design the precipitation-hardened alloys and a deeper insight into the design ofγ-γ’HEA.