<figure><img src="image_26.png" /><figcaption>Fig. 24. Bendability of 6111 with differing Fe content as a function of the reduction in thickness measured by tensile tests. Data extracted from [298].</figcaption></figure>

The failure strain is also particularly important in stretching operations. An example from Das et al. [297] of the effects of Fe-
containing intermetallics on the FLC is shown in Fig. 23b. Here the FLC was determined for 6022 sheets with both a low Fe con-
tent and an elevated Fe content. The FLC for the material with elevated Fe lies below that of the low Fe material. The origin of this
reduction in formability is also the Fe-containing intermetallics and their effect on fracture.

However, when considering contaminant effects and intermetallic particle formation it is important to realise that not all inter-
metallic particles are the same, and that they are not always detrimental. Sillekens et al. observed no significant changes in the FLC for
5017 when they increased the Fe content to 1.4% [299]. Different effects are observed in different alloy systems and for different
impurities, depending on the ability of a base alloy to spread plastic deformation during straining. In addition, the critical stress and
strain for the initiation of local failure at a particle also depends on its shape. This can be manipulated by adding specific alloying
elements. An example is the addition of Mn in the case that the alloy has Fe/Si-containing intermetallics. Mn can change the particles'
shape from plate-like to less damaging equiaxed morphologies. Therefore, alloy- and impurity-specific doping strategies can be
developed which mitigate potentially damaging effects of the increasing fraction and shapes of intermetallics as a result of recycling.
However, as seen below, the effects of intermetallics are not always detrimental.

While the emphasis so far has been the role of intermetallic particles in fracture and the resulting implications for formability, as
scrap use increases attention should also be paid to the accumulation of elements that may segregate to grain boundaries and
potentially cause embrittlement and intergranular failure. Some elements (such as Ga) are known to generate such effects in Al, but as
alloy compositions become more influenced by contaminants we will need knowledge about other embrittling species, and must be
able to either control their accumulation or develop mitigation strategies (such as co-segregants).

3.7.2. Grain size, crystallographic texture and R-values

During stretching or drawing operations, the sheet experiences both in-plane and through-thickness strains. The resistance to thinning during forming is the limiting factor in some operations, and this is often characterized by the R-value (the ratio of the in-plane to through-thickness strains). An R-value of 1.0 corresponds to isotropic deformation. Larger values for R correspond to greater resistance to thinning and thus usually translate to better formability of the material. For Al-alloys, the R-value is typically 0.6–0.8. Large values for R are obtained when the texture contains a large fraction of {1 1 1} planes parallel to the plane of the sheet, but such textures are difficult to obtain in Al-alloys and this is the reason for the low R-values (compared to steels). Strain within the plane of the sheet is also anisotropic, and the anisotropy is characterized by ΔR, calculated from measurements taken at 0°, 45° and 90° to the rolling direction. It is the ΔR that gives rise to the well-known earning phenomena during the deep drawing of beverage cans. A small ΔR is usually preferable [300–306].

The grain size and shape, and the crystallographic texture, all depend on the thermo-mechanical processing of the sheet; this also
involves, in particular, the elevated temperature processes that include recovery and recrystallization. These processes therefore affect
the R- and ΔR-values [307–312].

The accumulation of contaminant elements as a result of scrap usage can affect all stages of thermo-mechanical processing. Solute
in solution will affect the accumulation of stored energy that drives recovery and recrystallization; intermetallic particles will affect the
distribution of stored energy and therefore have an influence on the initiation of recrystallization, for instance through particle
stimulated nucleation; tramp elements in solution can strongly affect recovery, hence modifying the competition between recovery and
recrystallization; intermetallic particles will affect the migration of grain boundaries through the Zener force and therefore directly
influence the final grain size; and solute in solution can also affect the grain boundary migration through effects on boundary mobility. The final grain size and texture of the sheet will be a result of all these coupled effects – all of which can be affected by contaminant elements [313–321].

Intermetallic particles, such as the Fe-containing particles that can accumulate as a result of recycling, have already been shown to have important effects on limiting grain growth. Sarkar et al. [322] reported that the grain size of their 6111 alloy decreased from 110 µm in an alloy containing 0.06Fe to 40 µm in an alloy containing 0.68Fe. Reductions in grain size can have beneficial effects, such as improving the surface appearance of the sheet after forming by limiting the “orange peel” effect, and they can also affect the Lüders banding. A refined grain size can also give some additional strengthening (although this is not generally a major strengthening mechanism in Al-alloys) and help spread plasticity by limiting slip band length.

Intermetallic particles can, in principle, also affect texture development through potential roles in recrystallization initiation, although this has not yet been studied in detail in alloys contaminated by scrap. In the 6022 studied by Das et al. [297] with two different Fe content figures (Fig. 23b), the alloy containing more Fe actually had a lower ΔR than the lower-content Fe alloy.

Solute in solution can also affect the grain size and texture of the material through effects on both grain boundary motion (solute drag effects) and modifications to the competition between recovery and recrystallization. These have not been studied in scrap-contaminated aluminum alloys, but potential effects probably depend on the elements that accumulate during recycling.

### 3.7.3. Strain-hardening capacity and strain-rate sensitivity

A high strain-hardening rate and positive strain-rate sensitivity are desired in all forming operations to spread the plasticity at large strains and to prevent, as long as possible, strain localization. Whereas the formation of intermetallic particles was especially important in discussions of local fracture during forming, solute in solution is particularly significant for strain-hardening capacity and strain-rate sensitivity.

Although most solutes improve the strain-hardening rate of Al, some solutes, such as Mg, have an especially strong beneficial effect. This is one of the strategies behind the Mg-rich 5xxx sheet alloys used in applications requiring high formability. Modifications to the solute in solution due to scrap-related contaminants must be monitored carefully, both in terms of adding solute to solution, but also in terms of the formation of intermetallics that may remove valuable solute from solution. A recent study examined the effect of Cu contaminants on 5xxx alloys [323] for exactly this reason – to understand the effects on strengthening and strain hardening from the view of tolerance to recycling. We should expect much more work of this type in the coming years.

While solutes in the matrix are generally beneficial for strain hardening, in alloys containing large solute additions (such as 5xxx series and the age hardenable alloys in T4 state) the dislocation-solute interactions can lead to negative strain-rate sensitivities and the associated instabilities in plastic flow. These can manifest as serrated yielding (PLC effect), stretcher markings or even Lüders bands. All these features are detrimental to formability and can also cause poor surface finish. Some elements are worse than others, and hence an understanding of how the accumulation of scrap-related impurities affect these instabilities is required.

The formability of aluminum sheet material is complex. It depends on the ability of the material to spread plasticity during forming and resist plastic localization. This in turn depends on many features of the microstructure and its plastic response, almost all of which are affected by changes in composition due to contaminants accumulated by recycling. Since flat-rolled product represents such a significant fraction of all the Al used, it is inevitable that scrap use in sheet alloys will continue to increase in the future. It will be necessary to manage the effect of compositional variations in the phenomena discussed above, and also to develop a more in-depth understanding of the roles of all contaminant elements and how they interreact with each other and with deliberate additions. Such an understanding will hopefully result in new, more contamination-tolerant sheet alloys in the future.

## 3.8. Joining of alloys containing scrap-related impurities

The ability to join aluminum alloys with high integrity is a key technology in manufacturing finished products across all industrial sectors. Joints often limit crash worthiness and service durability and are favored sites for fatigue and corrosion initiation. Aluminum is readily weldable by most gas-shielded fusion technologies, including by a range of laser and arc welding processes [324–327]. Of the arc welding processes, Gas-shielded Metal Arc Welding (GMAW) and its more recent lower heat input variant, Cold Metal Transfer (CMT), are generally preferred by the automotive sector to reduce distortion [325]. Al can also be resistance-spot-welded (RSW); however, point joining of Al-alloys in automotive manufacturing currently favors self-piercing rivets (SPR) because RSW with Al-alloys has a high energy requirement and low tolerance to variation in the electrode contact conditions, and produces joints with poorer mechanical performance [326,327]. Aluminum is also readily weldable by solid-state friction stir welding (FSW) which works well with all alloy classes and new variants of FSW, such as stationary shoulder and bobbin welding, offer opportunities for higher surface quality and wider applicability [328–331]. In addition, mechanical joining by SPR and hemming are widely applied to body-in-white assembly in the automotive sector, often together with adhesive bonding [326]. To date, the impact of more recycled Al scrap entering the supply chain on these joining technologies has been the subject of limited systematic research. Specific issues can, however, be anticipated in each class of joining process.

### 3.8.1. Fusion welding

The main issues associated with fusion welding of recycled Al-alloys are the impact on their high propensity to form gas pores [332,333] and, for certain compositions, the susceptibility to solidification and liquation cracking [334–336]. Due to the significant change in solubility of hydrogen in aluminum between its liquid and solid states, aluminum fusion welds are prone to the formation of H-gas pores [332,333], which can be very detrimental to joint performance. Reuse of scrap with a high surface area could potentially
lead to an increase in hydrogen concentration due to surface contamination from hydrocarbons, moisture, and hydrated surface oxides, as well as the introduction of oxide films into liquid melts. Both of these factors are detrimental to porosity because H-pore formation is very sensitive to the hydrogen concentration and nucleation on oxide bi-films [337]. Fortunately, melt processing technologies are available which can adequately de-gas and filter recycled feedstock if applied correctly.

A further concern associated with fusion welding is that higher Fe and Si levels, and other contaminants present in the scrap supply chain, can affect solidification cracking. Solidification cracking is common in Al-alloys with a wide freezing range (e.g. 7xxx alloys containing Cu) and is caused by a pressure drop developing across the inter-dendritic channels in the mushy zone during the final stages of freezing [336,338]. As such, it is highly sensitive to the freezing range of the last 5–10% fraction of (typically eutectic) liquid, and the ability for liquid to feed through the inter-dendritic channels. Fe and Si on their own do not generally have a great effect on the critical final freezing range of most Al-alloys, but they do promote the formation of complex intermetallic compound (IMC) phases (e.g. α-Al<sub>8</sub>Fe<sub>2</sub>Si, β-Al<sub>5</sub>FeSi, and α-Al<sub>15</sub>(Fe,Mn)<sub>3</sub>Si<sub>2</sub>) that can form large, high aspect-ratio IMC particles. These can block feeding channels and can also promote nucleation of H-pores [336,339]. For example, it is known that increased Fe levels can generate higher levels of shrinkage porosity in castings and are expected to aid the nucleation of inter-dendritic gas with negative effects on mechanical performance [336,339]. High solidification rates achievable by laser welding may be beneficial in this regard, as Fe has a low diffusivity in liquid Al and some solute trapping can occur at high welding speeds [340] (see Fig. 25).

Liquation cracking can also occur near the fusion boundary in Al-alloys with low incipient melting points [334,335], but this is not expected to be greatly affected by slightly higher levels of Fe or Si. Other contaminants in Al scrap streams are also of concern to fusion welding. For example, low-melting-point metals like Bi, Pb, Sn and Cd are added to free-machining Al-alloys (e.g. 2011). These elements form soft, low-melting point, phases to promote chip breaking and cutting tool lubrication [341]. In fusion welding, contaminants can also become concentrated at the weld centerline, exaggerating their effect, particularly when welding with a teardrop-shaped melt pool. Care should therefore be taken to exclude materials from the scrap system that contain such elements. Hot cracking issues can also be alleviated by selecting welding conditions and grain refining additions that generate a fine-equiaxed weld zone grain structure, and by avoiding excessive constraint and out-of-plane stresses in recrystallized sheet/plate products [335,336,342].

### 3.8.2. Friction stir welding technologies

Friction stir welding (FSW) methods have increasingly been adopted to join Al-alloy plate, sheets and extrusions in ship and train building, as well as in specialized automotive and in some aerospace products, particularly for space launch vehicles [328]. FSW is ideally placed to weld more highly contaminated low-grade recycled alloys, as it suffers from none of the liquid-phase-related problems that beset fusion welding. FSW is also very tolerant of the presence of high volume fractions of coarse second-phase particles, and actually refines such particles in the weld zone microstructure by imparting severe deformation to the weld members [328,330]. Indeed, friction stir processing (FSP) has been developed from FSW to specifically refine cast microstructures such as, for example, the primary and eutectic Si particles present in Al-Si castings, bringing significant improvements in mechanical properties [343].

### 3.8.3. Mechanical joining and adhesive bonding

Self-piercing rivets (SPR) and hemming are common joining technologies used in lightweight Al-alloy car body structures [326,344]. Both processes can be adversely affected by a reduction in the formability of the join members (see Fig. 26). In hemming, Al-alloy closure panels are stretch-bent through a tight radius by rolling or die pressing, giving rise to high surface strains in plane strain where there is a minimum in the sheet forming limit [305,327]. It is already challenging for Al suppliers to meet the cold hemming requirements of vehicle manufactures [322,327] and any further reduction in ductility could have a significant impact on the ability to form hemmed joints. SPR is also sensitive to poor bottom sheet formability, which can cause cracking in the joint buttons [326]. Forming limits in such scenarios are strongly influenced by the presence of clusters of coarse intermetallic particles that

<figure><img src="image_27.png" /><figcaption>Fig. 25. Cracking following bands of Fe-rich intermetallic particles during bending. Reproduced with permission from [322].</figcaption></figure>
<figure><img src="image_28.png" /><figcaption>**Fig. 26.** Joining of aluminum alloy sheets by using self-piercing rivets: (a) conventional; (b) optimized. Reproduced with permission from [347].</figcaption></figure>

prematurely nucleate voids, particularly under triaxle stress states [305,322,345,346]. For example, premature shear cracking has been noted in Al automotive sheets (e.g. 6111) with a high Fe content when bending transverse to bands of Fe-rich stringers [322,345]. The requirement to form mechanical joints is thus probably one of the key factors limiting Fe content in recycled Al for automotive closure sheets.

The high-strength steel rivets currently used in SPR are a known issue with recycling, in that they are difficult to remove from Al scrap and are an increasing source of Fe contamination. Efforts have been made to use high-strength Al-alloy rivets to increase recyclability, but to date this has had limited success due to lower joint strengths and restrictions on the hardness of the materials that can be successfully joined [326,344]. Clinching, while circumventing the requirement for steel rivets, leads to inferior joint performance [327].

Adhesive bonding is frequently applied in conjunction with SPR to assemble aluminum car bodies [326,327]. The chemical conversion treatments used to engineer oxide films on Al-sheets, prior to applying adhesives, are sensitive to the presence of surface second-phase particles, which may not always be removed by the etching step that precedes conversion coating formation. The presence of such residual particles can locally alter the film properties and, due to the cathodic nature of the particles compared to the alloy matrix, may influence the long-term durability of the surface oxide film when exposed to wet or humid environments. However, due to the relatively small particle surface area, it is unlikely that their presence significantly affects surface adhesion.

# 4. Methods and tools for the investigation and design of aluminum alloys which contain scrap

## 4.1. Thermodynamics simulation tools for the study of scrap-related impurities and intermetallic phases

The underlying phase diagrams provide tools for understanding and designing scrap-based, impurity-tolerant alloys. These diagrams can be calculated using computational thermodynamics methods which capture and interpolate/extrapolate experimental data. The CALPHAD (CALculation of Phase Diagrams) approach (e.g. [348,349]) has systematized the procedure. Here the excess Gibb's free energy is described using a polynomial expansion which extrapolates the response of pure elements and binary and ternary interactions [350]. Ordered phases are described in the compound energy formalism using multiple sublattices, each with a distinct composition [350,351]. Commercial software packages such as Thermo-Calc [352], Pandat [353] and FactSage [354] provide access to databases which contain free energy-composition descriptions for phases. These can be used to construct estimates for phase fractions as a function of temperature, pressure and composition under stable or metastable equilibrium conditions, also considering tramp-related alloy variations.

Recent CALPHAD solutions and databases suitable for the thermodynamic investigation of Al-alloys are as follows. Thermo-Calc offers the Al-based alloy database TCAL7, which includes 39 elements and contains 267 assessed binary systems, 99 assessed ternary systems, 12 assessed quaternary systems and >600 solution and intermetallic phases [355]. The PanAluminum database from Pandat (PanAl2020) includes 34 elements and 851 phases [356]. The FTlite database from FactSage includes >54 elements for Al-alloy calculations. The FTlite 7.3 database includes 1703 pure compounds with 2104 stoichiometric phases and 265 solution phases [357].

In principle these databases consider the essential, and typical scrap-related contaminants, but they can still neglect some of the associated phases. For instance, in the context of possible incorporation of impurities in recycling, the impact of special minor alloying elements such as rare earth elements in aluminum alloys may be an issue, but small-extent impurities are not always detected by scrap sorting techniques or are overlooked by some databases. The element neodymium is the basis in NdFeB magnets, which provide a significantly higher magnetic field per unit volume compared to ferrite based magnets and are therefore attractive for clean energy applications such as the electric motors found in electric/hybrid vehicles [358]. The FTlite database in FactSage includes neodymium [357]; the databases TCAL7 in Thermo-Calc and PanAl2020 in Pandat do not. The larger number of elements included in the FTlite database are thus an advantage in thermodynamic calculations which include rare earth elements.

Besides the commercial databases, the open database mc_al_v2.029.tdb issued by MatCalc is also available. It is designed for thermo-kinetic simulations for Al-alloys with additions of Cu, Fe, Mg, Mn, Si, Zn and Zr [359]. In a comparative study, DSC experiments were performed to measure the solidus temperature of Al-Zn-Mg-Cu alloys and the experimental results were compared with thermodynamic calculation results obtained by the software packages FactSage, Pandat and MatCalc. The results of the experimental tests matched the results of the thermodynamic calculations, which predicted the trend accurately within a scatter of 49 °C. On the one
hand it is noted that such scatter can occur in the experimental setup due to thermal lag and due to the standard deviation of the DSC measurement results. On the other, the extrapolation of optimized systems in limited thermodynamic datasets and the thermodynamic data itself, which can include measurement errors, may contribute to the observed scatter. Overall it was concluded that CALPHAD can be a useful tool for optimizing specific alloys by predicting the solidus temperature of specific alloy compositions [360].

Pertinent studies depicting the use of the CALPHAD approach to recyclable aluminum alloys have been published in the context of Al-Si cast alloy design [352,361]. These alloys have a higher tolerance for compositional variations compared to wrought alloys, and have long served as sink systems for recycling aluminum scrap [36]. One of the key impurities stemming from such recycled feedstock and needing control is Fe. While Fe is desirable to a small extent because it helps to reduce die soldering and hot tearing, at higher concentrations it reduces ductility. Recycling-friendly wrought aluminum alloys made (e.g.) from automotive scrap are subject to similar challenges, due to increased Fe content and the formation of brittle intermetallic phases. Also requiring consideration is the potential of other tramp elements which generate unwanted brittle phases and alter castability (for strip or direct chill casting) or modify the sequence of precipitation. Here, for example, a quasi-ternary Al-Mg-Si 6xxx series alloy produced from recycled scrap aluminum would need to be viewed as a multicomponent alloy containing non-negligible amounts of Fe, Mn, Cr, Ti, Zn and Cu [3]. The empirical challenge presented by experimentally evaluated alloys which contain up to eight alloying elements, each with compositions in the range 0–10 wt%, is overwhelming. Here up to 10⁸ independent alloys would have to be prepared and evaluated in order to assess properties [362].

The CALPHAD approach allows predictions to be made by drawing on a database formulated from a much smaller number of assessments, based on interpolation. The excess free energies are interpolated from the assessment of the pure materials, binary and (sometimes) ternary interactions. Even this strategy, however, leads to combinatorial complexity as the number of elements increases. To fully describe a system with more than three components, we would want to assess all of the binary and ternary systems within it.

An alloy containing 10 elements needs the complete assessment, based on the binomial coefficient, of $\binom{10}{2} = 45$ binary and $\binom{10}{3} = 120$ ternary systems, while a system containing 15 elements would require 105 binary alloys and 455 ternary system descriptions [363]. While it is often considered sufficient to consider interactions up to ternary, ignoring any higher order terms, in cases where quaternary phases are present they need to be accounted for. The CALPHAD thermodynamic databases are quite extensive as regards classic aluminum alloy compositions, but even there they do not include descriptions for all binary and (particularly) ternary interactions. The lack of experimental studies of ternary systems is often an impediment. For example, the TCAL5 database available from Thermo-Calc was developed in the framework of 35 elements, with the assessment of 258 binaries but only 87 ternary systems [352]. For reference, a complete description of a system containing 35 elements would require the assessment of 595 binaries and 6545 ternaries.

In many cases such a complete description is unnecessary because the assessment can be optimized to focus on specific alloys of interest. Interpolation within the range of alloys close to these are then found to be sufficiently accurate. However, complications arise when using such databases to explore alloying effects where extrapolation is required. Issues of incomplete coverage of binary and ternary systems then become more problematic, as does the possibility of missing phases in the assessment. Recent efforts motivated by multi-principal component alloy design have sought to assess the predictive capability of CALPHAD calculations of phase equilibria based on the fraction of assessed binary (FAB) and ternary (FAT) systems in a given database [363,364]. In a recent study, several 4-component alloys were assessed based on different FAT and FAB values, and compared with the predictions made when the fully assessed database was used [364]. While it was shown that under limited conditions extrapolating from the binary assessments can provide good predictions, this was generally not true across the entire phase diagram. In cases where binary or ternary compounds exist, their omission strongly affects the phase equilibria predictions nearby [364].

In the context of non-traditional alloy chemistries, such as those encountered in aluminum alloys containing a high fraction of scrap-related contaminants, the items outlined above need to be assessed in further detail, particularly for the ternary and higher-order alloy systems and novel binary, ternary and higher-order compounds. The time and effort of conducting experiments have been a traditional bottleneck in this process, but computational techniques help fill such gaps [365–367]. Ab initio electronic structure simulation packages and advanced statistical mechanics tools allow proper sampling of properties at finite temperatures and in disordered phases. Ab initio data in the form of predicted heats of formation for ordered phases have long been used to supplement data in CALPHAD thermodynamic assessments [367]. With the advent of high-throughput tools, the ability to screen a wide range of compositions and crystal structures to identify stable phases and their heat of formation has become essential. For example, in the area of Al-Li-Cu alloys there has been much controversy regarding the composition and structure of the T1 precipitate phase. Using ab initio calculations, Kim et al. [368] were able to compute the heat of formation for five candidate structures proposed in the literature based on experimental observation. From the 0 K heat of formation for the candidate phases and other known stable phases in the alloy system, it was possible to test which phase would be expected to be most stable and to share a tie line with Al. In this case none of the proposed structures was found to be stable, and therefore cluster expansion calculations were used to expand the structures studied. Two were discovered to be stable, and one of them was close to sharing a tie line with Al.

Here one limitation of 0 K heat of formation calculations appears: the possibility of phase stabilization due to the effects of entropy. An example where lack of vibrational entropy effects can cause incorrect predictions is the stability of the equilibrium θ phase relative to that of the metastable θ' phase in Al-Cu binary alloys [369]. The development of effective Hamiltonians to extrapolate 0 K ab initio calculations to include entropic stabilization is progressing, mostly using harmonic or quasi-harmonic approximations [370]. For high temperature predictions, the inclusion of anharmonicity (temperature-dependent phonon softening and broadening beyond the
thermal expansion softening captured in the quasi-harmonic approximation) is becoming more important. While predicting fully anharmonic free energies from density functional theory (DFT) calculations has been computationally taxing up to now, the advent of new techniques is reducing this computational bottleneck significantly. Using machine-learned surrogate interatomic potentials, for example, can make fully anharmonic calculations of free energy by thermodynamic integration from DFT data feasible in multi-component alloys [371]. Anharmonicity can also be important in ab initio predictions of phase stability in another way. In order for ab initio calculations to predict a heat of formation, the phase must be mechanically stable at 0 K [366,370]. There are many examples of phases for which this is not true, but which are stable for T > 0 K. Any high throughput approach at 0 K will fail to predict these as a possible phase, leading to incorrect assessments if the results are used as input to CALPHAD databases. Recently a new approach was proposed for handling such cases [372], but it is important not to neglect the potential impact of such phases at finite temperature.

The discussion highlighted examples of ordered phases whose properties were predicted by ab-initio calculations. Fully ordered phases, such as Al₂Cu, where each site is unambiguously occupied by Al or Cu, are well suited to ab initio calculations given the limited number of atoms that can be modelled with current computational limitations. Disordered (or partially disordered) phases with site defects present the challenge of adequately representing the disorder when only a small number of atoms (a few unit cells at most) can be simulated. In this context, there have been significant advances in the development and deployment of special quasi-random structures (SQSs) for the simulation of compositional disorder [373,374]. While such SQSs cannot account for short-range order, and are thus limited to regular solution-like models, their coupling with analytical cluster expansions can be used to correct for this in systems where it is important [365]. This may be the case for many multicomponent aluminum alloys where clustering or ordering are observed to precede precipitation [55].

While the specific technical details described above are instrumental in high-fidelity thermodynamic predictions from ab initio calculations, one must also reflect on the combinatorial challenge in trying to construct a CALPHAD (or CALPHAD-like) representation of the phase equilibria in a multicomponent system. It is here that high-throughput workflows specifically designed to work within a CALPHAD framework show significant promise [365]. A number of systematic and open-source tools for such high throughput calculations have been developed, including the Materials Project, the Open Quantum Materials Database (OQMD) [375], Automatic Flow for Materials Discovery (AFLOW) [376] and the Novel Materials Discovery Laboratory (NOMAD) [377]. The T1 phase predictions described above [368] were performed, for example, within the OQMD database. Such databases, which also collect prior calculations, also allow data mining or the use of search routines to help identify potential alloys of interest [378].

A final consideration is the fact that the above discussion has implicitly focused on classical thermodynamics, where each phase is assumed to be infinite in extent and free of defects. Yet defects play an important role in defining the properties of alloys, and the interaction between chemistry and defects can lead to substantial deviations from the behavior anticipated from an equilibrium phase diagram. The first thermodynamic property that is essential for predictions of microstructure evolution in aluminum alloys is interfacial energy, particularly between the matrix and precipitate phases. How chemistry effects surface energy can have a significant impact on the progression of decomposition of a solid solution. Ab initio calculations in this case are possible in some cases, but require many more computational resources because relatively large supercells are needed to allow one phase to be embedded into the other. The possible equilibrium arrangement of solute around grain boundaries, free surfaces and at dislocations is a topic of increasing importance, requiring an assessment of chemical equilibrium. Recent work has illustrated how chemical equilibrium between solute, a solid solution and a grain boundary can generate solute ordering induced by segregation and the structure of the boundary. Computation of “defect phase diagrams” is necessarily complicated by the fact that supercell sizes need to be large to incorporate the defect; for example, in the case of a dislocation one must properly account for the long-range elastic strain field if one wishes to predict equilibrium. This may preclude “brute force” ab initio calculations, but techniques such as quantum mechanics/molecular mechanics (QM/MM) or machine-learned interatomic potentials with accuracies for formation energies which approach those from direct ab initio calculations show promise for the further exploration of this topic.

## 4.2. Theory and simulation for the study of scrap-related impurity effects on precipitation kinetics and microstructure evolution

The impact of impurity elements on the thermo-mechanical response during production and on the properties of the finished product is many-sided, and the consequences of even minor amounts of impurity elements can be substantial. For instance, if a certain element accelerates or decelerates the formation of a particular precipitate, or if it modifies the sequence of precipitation or changes the crystal structure (e.g., Cu- additions [379]), this will affect many other (subsequent) processes, such as (i) the further formation of (strengthening) precipitates due to the availability of precipitate-forming elements (Cu, Mg, Si, Zn etc.); (ii) the annihilation or generation of structural vacancies at the precipitate/matrix interfaces during heat treatment and thus the diffusion-controlled kinetics of subsequent precipitation reactions (quench-sensitivity); (iii) the amount of solute present in the matrix, which will affect electrical and thermal conductivity; and (iv) the mobility of grain boundaries and dislocations, which are influenced by the (precipitation-related) Zener drag or the solute drag / dynamic strain aging effect caused by segregation of dissolved atoms into interfaces/dislocations, which will impact the degree and continuity of the stress-strain response of the material (Portevin-Le Chatelier effect), grain growth and recrystallization kinetics during heat treatment [55]. Impurity atoms in solid solution can also (v) modify recrystallization textures (as has been shown for (e.g.) Fe additions [236]); or (vi) change the strain hardening behavior of alloys (e.g. Cu [380] or Mg [381]), stimulate precipitate/dispersoid nucleation, (e.g. Cd [382], Sn [383], Au [384] or Ag and Si [385]), or generally influence heat treatments, such as homogenization (as shown for Fe and Sc additions [386,387]).

### 4.2.1. General considerations about microstructure and precipitation modeling

When designing a computational framework for the modeling and simulation of scrap effects in Al-alloys, one is confronted with
the fundamental question of which models and mechanisms should be considered. Simulations can only deliver useful predictions if the relevant mechanisms are properly reflected in the constitutive formulations. For instance, quench sensitivity and natural aging kinetics can only be properly described if the evolution of excess vacancies is taken into account in the simulation together with the precipitation kinetics of dispersoids and the trapping of vacancies at solute atoms or within atomic clusters [388]. Otherwise the impact of alloy composition and/or thermo-mechanical treatment on the processing and properties of the alloy will not be reflected in the necessary depth, meaning that their effects cannot be correctly predicted; and the model will remain incomplete and of limited value.

A major aspect of such simulations of precipitation and microstructure evolution is that the individual metallurgical mechanisms never operate independently, but are coupled; i.e., they interact and are often competitive [55]. The more complex the simulation, the more the individual models need to be incorporated into the framework and properly connected to each other. Fig. 27 shows a rough sketch of fundamental models for precipitation kinetics and microstructure evolution, with their interactions and relevance for Al-alloy processing. For instance, precipitation kinetics will determine the amount and type of solutes that are dissolved in the matrix. This will directly influence the mobility of dislocations and grain boundaries and, thus, impact recrystallization kinetics and stress-strain response during plastic deformation. The latter processes will, in turn, influence the precipitation process by providing the heterogeneous nucleation sites (dislocations, subgrain boundaries and cells, grain boundaries) and deformation-induced vacancies. In addition, the number of dissolved impurities impacts the availability of vacancies through the mechanism of solute trapping, which is a major factor in all kinds of diffusion-controlled processes, such as precipitation, recovery, recrystallization or creep. Table 12 displays an overview of the metallurgical mechanisms that are triggered by scrap-related impurities and their possible impact.

### 4.2.2. Precipitation kinetics modeling

The formation of precipitates is a process that is governed by nucleation of particles within a supersaturated matrix [389,390], followed by the subsequent growth of the precipitates [390,391] and, eventually, overaging/coarsening [392,393]. Although sometimes treated in a separate manner, all three processes can overlap [394]. The following sections briefly review precipitation modeling and outline the advantages and potential drawbacks of the respective approaches.

#### Constitutive models

A mean field model for describing phase transformation kinetics is the Kolmogorov-Johnson-Mehl-Avrami (KJMA) approach [395-397], which can be written [398].

$$V(t) = 1 - e^{-V_c(t)} \quad (4)$$

where $V(t)$ is the transformed volume and $V_c(t)$ is called the extended volume. The latter quantity is formulated in terms of suitable nucleation and growth expressions, and the well-known sigmoidal curves are obtained for the particular phase transformation

<figure><img src="image_29.png" /><figcaption>Fig. 27. Interaction of models for microstructure evolution and precipitation kinetics.</figcaption></figure>
<table><caption>**Table 12**
Overview of the possible impact of scrap elements on processing and properties.</caption><thead><tr><th></th><th>Mechanism</th><th>Consequence</th><th>Effect on</th></tr></thead><tbody><tr><td rowspan="5">Trace elements<br>(impurities)</td><td>Segregation to grain<br>boundaries</td><td>Modify grain boundary energy<br>Modify heterogeneous nucleation energy<br>Modify grain boundary mobility (solute<br>drag)</td><td>Grain growth<br>Precipitate nucleation<br>Grain growth<br>Recrystallization</td></tr><tr><td>Segregation to dislocations</td><td>Dislocation pinning / cross-core diffusion<br>effect</td><td>Plastic response / Portevin-Le Chatelier effect</td></tr><tr><td>Trapping of vacancies</td><td>Reduce/increase effective diffusion<br>coefficient</td><td>Precipitation / clustering kinetics</td></tr><tr><td>Heterogeneous nucleation</td><td>Change lattice concentration of vacancies</td><td>Extension of precipitation free zones</td></tr><tr><td>Precipitate-forming<br>elements</td><td>Precipitate/dispersoid<br>formation</td><td>Increased nucleation rates<br>Capture other alloying elements</td><td>Precipitate/dispersoid density<br>Precipitate formation / change of precipitation<br>sequence</td></tr><tr><td></td><td>Enter into precipitates</td><td>Change its crystal structure</td><td>Change thermodynamic stability</td></tr></tbody></table>

considered [396]. The extended volume is often written as $V_c(t) = kt^n$, where k is a rate constant and n is the Avrami exponent. The KJMA approach has been used at various times to describe precipitation in Al-alloys. Sekhar et al. [399] used the KJMA model to describe the underaging kinetics in 6063 and combined it with the classical LSW [392,393] model for coarsening to take into account overaging. The information on volume fraction and radius was then used to describe the Vickers hardness and yield strength of the alloy. Sha [400] also used the KJMA approach to model the Vickers hardness evolution in several 6xxx alloys. In the same way, Pogatscher et al. [388] fit Avrami equations to their experimental hardness data from artificial aging of a 6061. Earlier modeling activities date to the 1990 s, where Shercliff and Ashby [401,402] developed a precipitation kinetics model to predict the yield strength in 6082. This model was later adopted by Rometsch and Schaffer [403] in an Al-7Si-Mg alloy. Eivani and Taheri [404] applied the KJMA model to simulate the age-hardening kinetics in an Al-Mg-Si-Cu alloy, and Guo et al. [405] investigated the effect of Zn-additions on precipitation kinetics. Yazdanmer et al. [406] applied the KJMA model to precipitation-hardening for non-isothermal aging of Al-Mg-Si alloys. Esmaeili et al. [407,408] developed their KJMA model for the 6111 alloy. Starink [409] introduced a model for diffusion-controlled precipitation that was later utilized (e.g.) in the analysis of cooling DSC curves in Al-Cu [410], Al-Mg-Si [411], Al-Mg-Mn-Cu [412] and Al-Zn-Mg-Cu [413] alloys. Another interesting example of DSC curve modeling is the approach by Hersent et al. [414], who used the regular solution model to represent the thermodynamics in the Al-Mg-Cu system. Further constitutive models for precipitation kinetics are (e.g.) [415–419].

Although computationally efficient, applying the original KJMA approach to the entire precipitation reaction is not straightforward in more complex heat treatments, because a single transformation variable, i.e., the volume fraction of the transformed phase, $V(t)$, cannot usually describe processes that occur after reaching the peak hardness [84]. This applies to (e.g.) Ostwald ripening/overaging and to the dissolution of metastable phases during prolonged heat treatment, or the dissolution of precipitates following a reheating treatment, for instance during homogenization. Possible solutions to this are offered by multi-phase KJMA models, as shown in [420,421] for precipitation in steel.

To predict mechanical properties, however, at least a minimum of information on (morphological) parameters is necessary, such as the mean radius of the precipitates, their number density and also often their shape. Models that incorporate evolution equations for radius and number density of precipitates are now commonly used.

### Nucleation, growth and coarsening models

Models characterized by evolution equations for the radius and number density of the precipitates, involving the elementary processes of nucleation, growth and coarsening, are generally termed Classical Nucleation and Growth Theories (CNGT). A first category of the corresponding models involves the “mean radius approach” [422], where the evolution of a size distribution of precipitates is reduced to the evolution of its mean radius and precipitate number density. Models of this type exhibit good computational efficiency. The classical representative of this type of approach is the Langer-Schwartz (LS) model [423], which has been taken as starting point in numerous works.

In a practical sense, the most essential ingredient of the LS-type models is the nucleation model, because the nucleation process determines the number of precipitates that will form and, by extension, dictates the maximum size of the precipitates as determined by soft impingement (overlap of diffusion fields) and the evolution of the precipitate volume fraction. A lower number density of particles means, on the one hand, that each precipitate can grow to a larger size because its individual growth volume is larger. On the other, the overall reaction will take more time to reach the maximum phase fraction because the diffusion distances become wider. The precipitation kinetics can differ by many orders of magnitude depending on the nucleation rate, whereas the overall reaction is much less sensitive to variations in the growth rate of precipitates at constant particle number density.

Nucleation is commonly expressed in terms of the nucleation rate, $J$, which is the number of nuclei of the new phase that will form per unit volume and unit time. It is commonly written as [389,424].

$$J = N_0 \beta^* Z e^{\frac{G^*}{k_B T}} e^{-\frac{t}{\tau}} \quad (5)$$

where $N_0$ is the number of potential nucleation sites, $\beta^*$ is the atomic attachment rate, $Z$ is the Zeldovich factor, $G^*$ is the critical nucleation energy, $k_B$ is the Boltzmann constant, $t$ is the incubation time and $T$ and $\tau$ have their usual meanings. From Classical
Nucleation Theory (CNT), the nucleation barrier assuming homogeneous nucleation is given by.

$$G^* = \frac{16\pi \gamma^3}{3 d^2} \qquad (6)$$

where $\gamma$ is the interfacial energy (J/m²) and $d$ is the effective driving force (J/m³). The latter quantity can be estimated from computational approaches, such as, CALPHAD [425]. Multi-component expressions for the quantities appearing in the nucleation rate are summarized, for example, in [390].

Interfacial energies can also be calculated from thermodynamic databases using, for example, the Generalized Broken Bond (GBB) model [426]. However, for estimates of the interfacial energies and, thus, reasonable estimates of nucleation rates, it is necessary to go beyond the planar sharp interface approximation of the GBB model and to treat the interface energy, $\gamma$, as a function of the nucleus size (interface energy size effect) [427] and temperature (diffuse interface effect) [428]. In multi-component precipitation systems with solution phases, i.e. precipitates with a certain compositional range of stability, the chemical composition of the nucleus also plays a decisive role; see e.g. [429,430].

Once the precipitate nucleus has reached supercritical size, its further evolution is usually described by the diffusion-controlled transport of atoms towards and away from the particle. The evolution of (mean) radius, $r$, as function of time can be written in a general form as.

$$\frac{dr}{dt} = \alpha_g \frac{D}{r}, \qquad (7)$$

where $D$ is an effective diffusion coefficient and $\alpha_g$ is a growth rate coefficient. Various expressions for $\alpha_g$ are reported in literature, e.g. in Zener [391], where the local equilibrium (LE) hypothesis is used to obtain an approximate solution of the moving boundary problem at the precipitate matrix interface [350,431,432]. Integrating eq. (7) delivers the well-known parabolic growth law for diffusion-controlled growth.

Alternatively, growth rate equations can be derived from the thermodynamic extremum principle [433–435], which was first demonstrated by Svoboda et al. [436] and which has recently also attracted the attention of other researchers [437,438]. Evolution equations for grain boundary precipitation have been developed; see e.g. [439,440] and the works cited therein.

Most coarsening / overaging models used in CNGT with mean precipitation quantities are based on the mean-field solution of the classical LSW treatment [392,393]. The general form of the coarsening rate equation for the mean radius evolution is.

$$\frac{dr}{dt} = \alpha_c \frac{D}{r^2} \qquad (8)$$

where $\alpha_c$ is a coarsening rate coefficient. The literature on this topic is exhaustive and various extensions of the original LSW model are discussed, e.g., in [441–443]. With reference to Al-alloys, coarsening of Al₃Sc particles was investigated, for instance, by Novotny and Ardell [444].

The models introduced above are valid for stationary and/or known size distribution of particles. For instance, the LSW model predicts a self-similar left-skewed distribution, also known as LSW-distribution, which remains stationary throughout coarsening. The distribution that is assumed to be present in the LS-model [423] is a-priori undefined; however, it is approximately fixed by the assumption that it coincides with the LSW-distribution in the long-term limit. Alternatively, the LS-model can also be derived assuming a log-normal distribution [445].

In the models discussed below, these assumptions are relaxed because the precipitate size distribution is discretized and the evolution of the distribution is governed by the evolution equations for the individual size classes or the fluxes prescribed between the size classes.

## Class precipitation models and software

The second group of CNGT precipitation kinetics approaches [422] comprises “class” models, where the actual particle size distribution is discretized in several size classes. The time evolution of each class is calculated either by evaluating the fluxes between neighbouring classes (Euler-type integration scheme) or by the size evolution of each individual class (Lagrange-type integration scheme). Both approaches deliver the evolution of the size distribution of precipitates over time as well as the evolution of mean precipitate quantities, radius, number density and volume fraction. A major advantage of these class models is that no separate model for overaging/coarsening is required. This is because the possible dissolution of precipitates is an inherent part of class models for spherical precipitates, provided that the mathematical formalism incorporates the Gibbs-Thomson effect [446].

The classical simulation approach for class precipitation kinetics modeling was introduced by Kampmann and Wagner [447] and it is known as the Numerical Kampmann Wagner (NKW) model. It is an extension of the original LS approach, where the evolution of the mean precipitation parameters, i.e. number density and mean radius, are replaced by the evolution of discretized precipitate size classes.

The available NKW models can be roughly categorized into (i) general-purpose precipitation kinetics and microstructure evolution software and (ii) special-purpose software/approaches, developed for particular application to certain groups of Al-alloys. Reviews on precipitation kinetics models and software are available from (e.g.) Hutchinson [84], Sigli et al. [448] and Robson et al. [449]. A summary of NKW models and software is shown in Table 13.

## Precipitate shape effects

An explicit feature of Al-alloys is that many of their important (strengthening) precipitates can be highly non-spherical. The reasons
<caption>**Table 13**</caption>

Overview of numerical Kampmann Wagner models and corresponding software for the simulation precipitation kinetics and microstructure evolution, including information about specific alloying / tramp elements.

<table><thead><tr><th>Name</th><th>Model</th><th>Publ.</th><th>Comment</th><th>Al-Applications</th></tr></thead><tbody><tr><td>Cinkilic et al.</td><td>[450]</td><td>2020</td><td>PK,YS; TDC: PanEngine¹</td><td>[450]</td></tr><tr><td>ClaNG</td><td>[451,452]</td><td>2002</td><td>PK,YS; Al-Mn-Mg-Fe-Si, 8xxx alloys</td><td>[314,315,453-458]</td></tr><tr><td>Du et al.</td><td>[459-461]</td><td>2012</td><td>PK; TDC: Gibbs energy lookup Table 2</td><td>[462]</td></tr><tr><td>Khan & Starink</td><td>[463,464]</td><td>2008</td><td>PK,YS; Al-Cu-Mg</td><td>[465]</td></tr><tr><td>Larouche et al.</td><td>[466-468]</td><td>2015</td><td>PK,YS; Mixed-mode growth; Al-Cu</td><td>[469,470]</td></tr><tr><td>MatCalc*</td><td>[436]</td><td>2004</td><td>PK,MS,YS; TDC: CALPHAD databases + MatCalc engine³</td><td>[471-477]</td></tr><tr><td>multipreci</td><td>[478]</td><td>2005</td><td>M(C,N) precipitates in steel</td><td>—</td></tr><tr><td>NAMO</td><td>[479-483]</td><td>2000</td><td>PK,MS,YS; Al-Mg-Si alloys</td><td>[484-488]</td></tr><tr><td>Nicolas and Deschamps</td><td>[489]</td><td>2003</td><td>No nucleation, reversion treatments; Al-Mg-Zn</td><td></td></tr><tr><td>PanPrecipitation*</td><td>[490,491]</td><td>2009</td><td>PK; TDC: PanEngine/PanDat¹</td><td>—</td></tr><tr><td>PrecipiCalc</td><td>[492]</td><td>2004</td><td>PK,YS; Ni-base alloys</td><td>—</td></tr><tr><td>Preciso</td><td>[422,493,494]</td><td>1998</td><td>PK; various Al-alloys</td><td>[495,496]</td></tr><tr><td>Robson et al.</td><td>[497-499]</td><td>2001</td><td>PK; TDC: pre-calculated chemical potentials⁴</td><td>[500-502]</td></tr><tr><td>Samaras</td><td>[503]</td><td>2006</td><td>PK,YS; population balance approach</td><td></td></tr><tr><td>Sarafoglu et al.</td><td>[188]</td><td>2019</td><td>Dual-grain model + NKW</td><td></td></tr><tr><td>TC-Prisma*</td><td>[504-506]</td><td>2014</td><td>PK; alloys with precipitates</td><td>—</td></tr></tbody></table>

*General purpose software; PK: Precipitation kinetics; MS: Microstructure evolution; YS: Yield strength; TDC: Thermodynamic coupling.

¹ PanEngine: CompuTherm LLC. (Madison, WI, USA); ²from ThermoCalc AB, Sweden; ³MatCalc: https://matcalc.at; ⁴From MT-DATA, National Physical Laboratory, UK.

for this may be [448] (i) anisotropy of interfacial energy or mobility; (ii) anisotropy of elastic constants; and/or (iii) strain incompatibility of precipitate and matrix. Precipitate plates with aspect ratios of 10–100 are common in Al-Cu-based alloys, e.g. Θ' (Al₂Cu) or T₁ (Al₂CuLi). The modeling of precipitation processes discussed above is well developed for spherical particles, and coarsening processes can only be modelled analytically for spherical particles. In some cases, such as 7xxx alloys, the precipitates have an aspect ratio generally only up to ~ 3. In these cases the spherical particle assumption still seems to work well, and there are many examples where state-of-the-art CNGT precipitation modeling was applied with success to 7xxx alloys, e.g. [449,489,507].

However, in alloys such as 6xxx, where precipitates usually adopt rod shapes, and 2xxx, where particles often have plate shapes, the spherical approximation in modeling is no longer suitable. In the simplest configuration of dilute solutions, non-spherical precipitate shapes mainly modify the diffusion fields around the particles, and thus lead to changes in the growth kinetics of these particles. A pragmatic solution which accounts for the growth kinetics of elongated or flat cylinders to represent needles and plates has been derived by Kozeschnik et al. [508]; the geometrical assumptions for the shape factor $h = H/D$ are shown in Fig. 28.

The central prediction of this model [508] is an accelerated growth rate for disc-shaped precipitates and a deceleration of diffusional growth for needle-shaped particles. In more recent work Holmedal et al. [461] arrived at partially contradictory results, which these authors attribute to different model assumptions. In their analysis they observe rather similar growth kinetics for needle-shaped precipitates compared to spheres of equivalent volume, but a deceleration in coarsening rates. The implementation of the Holmedal et al. model in the NKW framework is described in [462]. There are several other general treatments of non-spherical precipitate growth kinetics [466,494,509–513], some of them focusing specifically on Al-alloys [461,514,515].

<figure><img src="image_30.png" /><figcaption>**Fig. 28.** Definition of a simple shape factor to represent needles and plates based on a cylindrical shape. Reproduced with permission from [508].</figcaption></figure>
A number of new factors must be incorporated into an even minimum ingredients model for precipitation if the precipitates are highly non-spherical. The first is that in the case of rod- or plate-shaped precipitates, the evolution of particles is no longer well described by a single growth rate at each point in time. For a precipitate plate the lengthening rate will have one value, and the thickening rate will have a different, much lower value. The time dependence of these growth rates also differs from that of spherical particles: diffusion-controlled lengthening is usually linear in time (i.e. there is a constant lengthening rate), and thickening is interface-controlled. Consequently the whole time dependence of the precipitation growth phase is modified, and this in turn affects the time evolution of the solute content in solution. The second consequence of highly anisotropic precipitate shapes is that soft-impingement of the ends of precipitates can occur far earlier than soft impingement at other interfaces. A schematic example is shown in Fig. 29 for plate-shaped θ' particles forming on {100} planes in the Al matrix [516]. Both the particle itself and the surrounding solute diffusion field are shown. As seen in Fig. 29c, soft impingement occurs at the ends of the particles long before it is seen at the broad faces.

The consequence of the geometry effects shown in Fig. 29 are that coarsening processes begin at the ends of the plates long before they do so at the broad faces. This means that there is an extreme overlap in the growth and coarsening processes, specific to Al-alloys, that must be taken into account in CNGT models.

The third factor that must be addressed is the coarsening behavior of non-spherical particles. Analytical expressions of this have not yet been developed. Some authors have described the coarsening of such structures using LSW and spheres of equivalent volume, but the results are not fully satisfactory [516]. General solutions for precipitate coarsening based on simple shape factors are also rare (see for example Kim and Voorhees [517]), and asymptotic analysis shows that the temporal exponents for the coarsening law for prolate spheroidal particles are identical to those for a system of spherical particles. However, the amplitudes of the power laws depend on the particle aspect ratio. The coarsening rate constant for the equivalent mean radius of a particle decreases as the aspect ratio increases [518].

Two approaches have been suggested to model highly non-spherical particles. The low computational cost approaches described above, e.g. those of Holmedal et al. [461] or da Costa Teixeira et al. [516], consider the lengthening rate of plates using a Zener-Hillert diffusion-controlled approximation and combine it with LSW mean-field coarsening kinetics. The medium computational cost approach by Bardel considers rod-shaped precipitates in 6xxx in a coupled multi-class and phase field model approach [494]. This type of model is discussed in more detail below.

## Phase-field modeling of precipitation

<figure><img src="image_31.png" /><figcaption>**Fig. 29.** Schematic representation of the diffusional growth and coarsening of a distribution of plate-shaped precipitates on {100} planes of an Al matrix viewed in a (100) direction. (a) Rapid nucleation and early growth of the θ' precipitates; (b) diffusional precipitate growth; (c) soft impingement of adjacent plates at different times (dotted rectangles). The white regions represent the untransformed supersaturated matrix. (d) Coarsening during which a decrease in the number density is observed. Reproduced with permission from [516].</figcaption></figure>
Common approaches to modeling of precipitation kinetics utilize either spherical or shape-conserving approximations in a mean-field environment. If the precipitation analysis should extend to the impact of precipitate morphology on growth and coarsening rates, the phase-field technique [519–521] offers a versatile tool for expanding into the analysis of orientation-dependent kinetics and nucleation [522].

The nucleation process can be incorporated into the phase-field technique [424] either by using the phase-field simulation to calculate spatial variations in chemistry and then applying the classical nucleation rate equation to place seeds into the simulation domain, as in [523–525] for nucleation in Ni-base superalloys; or by incorporating a stochastic term in the phase-field equations and modeling the nucleation process in this manner, as in [526,527] for nucleation involving elastic stress. An interesting work on predicting the equilibrium shape of Θ' (Al₂Cu) particles based on the combination of ab-initio simulations with Density Functional Theory is that by Kim et al. [528], where the theory predicts a plate-like morphology for the strengthening precipitate in Al-Cu systems utilizing the first-principles-calculated interface energy anisotropy and the phase-field method, in agreement with experience.

In the context of scrap-related elemental contamination, impurities can influence the interface energy, which can affect nucleation, shape anisotropy and the size of the precipitation-free zones [529,530]. In such cases the phase-field method is a method of choice to account for these effects; see e.g. [522].

## Atomistic models

CNGT models generally utilize Classical Nucleation Theory to model the nucleation process, and they are limited to the capabilities of this theory and its extensions. In some cases, however, alternative approaches can provide additional insights into the nucleation process [449]. Examples are atomistic approaches, such as (kinetic) Monte Carlo with Cluster Expansion of Density Functional Theory (DFT) energies [531–535], or cluster dynamics [536].

Although this type of simulation approach delivers results of significant physical depth, present limitations in computational resources limit atomistic techniques as a method of choice mainly to fundamental studies of metallurgical mechanisms. The calculation of segregation energies for solutes at dislocations or grain boundaries is an example. Although typical engineering applications commonly involve simulation systems too large and complex to be handled by these methods, atomistic models are a highly valuable means of gaining insights into fundamental mechanisms and can provide physical quantities that may be used as input parameters for larger-scale simulation approaches, as in [537].

## Applications of precipitation kinetics modeling

Many applications of constitutive and CNGT-type models have been reported in literature. An example is the analysis of quench-sensitivity of Al-alloys [538], which documents the degradation of mechanical properties as a result of decreasing cooling rates after solution annealing. The main reason for this is the precipitation of coarse particles that bind certain elements and make them unavailable for later strengthening heat treatments. For a computational treatment of quench sensitivity, it is necessary to simulate the heterogeneous precipitation of particles during the quench process. In earlier studies based on cooling Differential Scanning Calorimetry DSC experiments, the kinetic model, developed originally by Zahra and Starink [539] for Al-Si alloys and developed further by Rometsch et al. [419], was utilized to investigate, for instance, the quench sensitivity of Al-Si alloys [540]. In more recent work, a KJMA-based model from Starink [409] is applied to study quench sensitivity in Al-Mg-Si [411] and Al-Zn-Mg-Cu [413].

A similar application for precipitation modeling is the identification of precipitation sequences and deconvolution of DSC curves [541]. Examples for corresponding studies are, for instance, the analysis of precipitation reactions performed by Lang et al. in Al-Mg-Si [473] and in Al-Zn-Mg [472] alloys in the framework of an NKW approach. These authors utilized the software package MatCalc to simulate the precipitation sequence during DSC heating experiments and to derive the corresponding enthalpy change accompanying the phase changes from the simulations. The experimental and computed DSC signals are compared and the peaks of the DSC signal are correlated to the formation or dissolution of the corresponding precipitate types. The measured and calculated heat flow for one of the alloys in Lang et al. [473] is shown in Fig. 30, and the corresponding phase fraction evolution in Fig. 31. Formation peaks of precipitates produce an exothermic reaction in the DSC; dissolution of precipitates show an endothermic signal. By comparing calculation and experiment, each peak can be assigned to the formation/dissolution of a specific precipitate type and the integral DSC signal can be deconvoluted into individual reactions.

A particularly interesting application of precipitation modeling with relation to scrap-related effects is the analysis of dispersoid evolution during 8xxx alloy heat treatments. Simulations performed with CLaNG [451] have been presented which were performed in the Al-Mn-Mg-Fe-Si system, including a number of dispersoid phases such as Mg₂Si, Al₃(Fe,Mn), Al₆(Mn,Fe), and others. The interactions between these precipitate phases alongside the homogenization and solution annealing treatments is of particular importance for the number of atoms remaining in solid solution after the treatments.

A recent work by Engler and Miller-Jupp [453] analyzes the evolution of dispersoids in the course of various heat treatment schedules; see the example in Fig. 32. The plots show the evolution of the volume fractions of dispersoids and the number of elements in solid solution for the heat treatment indicated by the temperature profile. The ClaNG simulations demonstrate a complex interaction of precipitate types, with formation of new dispersoids and dissolution of existing ones as a function of temperature. The experimentally measured solute contents and the simulations are in good agreement, thus demonstrating the predictive capabilities of the simulations and their potential in alloy development.

In the context of scrap elements, it is interesting to note that one key publication on the ClaNG model, [452], already bears the expression "... for recycled commercial aluminum alloys" in its title. The thermodynamic data in this work are reported to stem from a database which contains 15 elements. With most of the impurity elements of recycled Al-alloys in their database, the authors claim that the model can be applied to practically all commercial Al-alloys.

Another Al-alloy system is treated in NaMo ("nanostructure model"), which is described in refs. [479–483]. This model is focused on 6xxx alloys and their typical processing route, which is depicted in Fig. 33. The basic thermo-mechanical treatment involves the
<figure><img src="image_32.png" /><figcaption>Fig. 30. Measured and calculated heat flow during heating DSC experiment in alloy A. Reproduced with permission from [473].</figcaption></figure>

<figure><img src="image_33.png" /><figcaption>Fig. 31. Calculated phase fractions of precipitates during heating DSC experiment in alloy A. Reproduced with permission from [473].</figcaption></figure>

solution heat treatment, followed by a quench and holding at room temperature before cold rolling. After a period of room temperature aging, the model accounts for an artificial aging treatment, where the supersaturated solid solution decomposes into the precipitation sequence of ss-nuclei-β''-β' and the stable β-phase. The final part of the model involves a prediction of the stress-strain response of the material as a function of processing steps and schedule. In their study, Myhr et al. [482] compute the evolution of microstructure

<figure><img src="image_34.png" /><figcaption>Fig. 32. ClaNG simulations showing the evolution of (a) volume of second-phase particles and (b) solutes $c_{ss}$ during homogenization practice “HT” (symbols: experimental data). Reproduced with permission from [453].</figcaption></figure>
<figure><img src="image_35.png" /><figcaption>Fig. 33. Schematic outline of the NaMo software. SHT: solution heat treatment; NA: natural aging; AA: artificial aging; CD: cold deformation; TH: thermal; WH: work hardening; TD: thermodynamic; SR: static recovery; YS: yield strength; ss: solid solution. Reproduced with permission from [482].</figcaption></figure>

and precipitates over the entire production route and finally compare the predicted and measured yield strength of various 6xxx alloys with good accuracy. The corresponding diagram is shown in Fig. 34.

### 4.2.3. Vacancy trapping and interaction with scrap-related tramp elements

Precipitation processes in Al-alloys are diffusion-controlled, and vacancies are the carriers of this mechanism. For most metals their concentration approaches $10^{-4}$ close to the melting point. This is important in Al processing, because homogenization and solution treatments are often carried out in this temperature range. When the alloy is quenched to lower temperature, a considerable portion of the thermal vacancies can be quenched-in and remain in the crystal. Diffusion is thus accelerated and the kinetics of diffusion-controlled reactions can occur orders of magnitude faster when compared to a crystal which contains only thermally equilibrated vacancies.

Vacancies are created at sources such as free surfaces, grain boundaries, dislocation jogs or incoherent precipitate/matrix interfaces (e.g. those of tramp-element-related IMPs). The same microstructural features will act as sinks for vacancies. The impact of excess vacancies on diffusion in aluminum alloys has been investigated by many groups [542–549]. Robson [550] recently showed that the concentration of excess vacancies due to deformation can even exceed the maximum thermal vacancy concentration close to the melting point.

Vacancies can have attractive or repulsive interactions with many solute atoms within an Al matrix [551,547]. This is relevant for trace elemental concentrations, which are introduced via recycling. Therefore such effects will prospectively become more relevant in future.

If a vacancy travels through the crystal lattice and encounters a corresponding trap with attractive interaction, it will remain in the vicinity of this trap for a characteristic time determined by the value of the interaction energy. As a consequence, the average travel distances during diffusion will decrease and the *effective* diffusion coefficients become smaller. This effect is well documented as the retarding effect of Sn- or In- micro alloying on natural aging kinetics in 2xxx alloys [552] and 6xxx alloys [545,553–555] or as a major effect determining hydrogen diffusion in steel in the presence of microstructural traps [556]. Fischer et al. [557] investigated the impact of element trapping on diffusion and derived the effective diffusion coefficient in the presence of traps. Using this approach, Lang et al. [558,559] investigated the impact of vacancy trapping for various types of element in Al-0.5 Mg-1Si with and without addition of 0.05Sn (wt%). With interaction energies calculated from Density Functional Theory ($\Delta E_{Mg} = 2510$; $\Delta E_{Si} = 3180$; $\Delta E_{Mg} = 27100$ J/mol), these authors obtain a significant reduction in vacancy diffusion, particularly if the effect of Sn is taken into account. This demonstrates quite a strong interaction with vacancies. Some of their results are shown in Fig. 35 and Fig. 36. The study indicates that trapping of vacancies is particularly efficient for elements with high binding energy, such as Sn, and that trapping increases strongly with decreasing temperature. Consequently certain impurities, which may also be impurities introduced via recycling, make it possible to trap vacancies at room temperature to suppress fast natural aging and release vacancies again at higher temperature to accelerate artificial aging kinetics, in line with experimental evidence [555].

The preceding paragraphs discussed the impact of vacancy trapping on an *effective* diffusion coefficient, which ultimately determines the kinetics of diffusion-controlled reactions. The main impact of vacancy trapping is thereby exhibited on the remaining concentration of lattice vacancies that are available to enable the diffusion processes. The examples shown involve situations where the effective traps are provided by individual atoms with attractive vacancy-solute interactions. In some alloys, for example in the Al-Mg-Si system, the atomic clusters that are formed in the precipitation process can act as strong vacancy traps by themselves, thus leading to significantly altered precipitation kinetics. Pogatscher et al. [388] have denoted this phenomenon as the “vacancy prison”
<figure><img src="image_36.png" /><figcaption>Fig. 34. Strength prediction with NaMo for various 6xxx alloys. Reproduced with permission from [482].</figcaption></figure>

mechanism, proposing that vacancies can be efficiently trapped in solute clusters during natural aging, but can escape the clusters again during heating to artificial aging temperature and thus improve the age-hardening response of the alloy.

Zurob and Seyedrezai [560] provide a theoretical investigation of the concurrent trapping of vacancies and growth of solute clusters, obtaining a model that predicts a severe deviation from the classical parabolic growth law for diffusion-controlled reactions. Although a direct observation of growth kinetics appears to be difficult, these authors provide indirect evidence for a log-based growth kinetics utilizing electrical resistivity measurements. Fig. 37 shows a sketch of a vacancy trapped in a solute cluster.

In the Zurob and Seyedrezai model [560], the cluster growth rate, i.e., the evolution of the number of atoms in the cluster, $n_{cl}$, is assumed to be proportional to the escape rate of vacancies from the cluster as.

$$ \frac{dn_{cl}}{dt} \propto e^{\frac{m_{cl} \Delta E_{cl}}{k_B T}}, \qquad (9) $$

where $\alpha$ is a constant and $\Delta E_{cl}$ is the binding energy between the vacancy and a solute atom in the cluster. Separating variables and integration leads to the result that the number of atoms in the clusters evolves in proportion to the logarithm of time as.

$$ n_{cl}(t) = \frac{k_B T}{\alpha \Delta E_{cl}} \ln \left( \frac{K \alpha \Delta E_{cl}}{k_B T} \right) + \frac{k_B T}{\alpha \Delta E_{cl}} \ln(t), \qquad (10) $$

where K is the proportionality constant from eq. (9).

The logarithmic growth rate also seems to agree well with experimental evidence regarding the growth kinetics of clusters obtained via atom probe tomography. The clusters commonly show a statis in their growth behavior when observed over long periods during natural aging [561]. Direct experimental confirmation of this growth characteristic is sparse. Some evidence for logarithmic growth characteristics in clusters is available from positron annihilation experiments, e.g. Lay et al. [543]. In their review on clustering in age-hardenable aluminum alloys, Dumitraschkewitz et al. [562] also conclude that Mg-Si-rich clusters stagnate in size evolution even at small sizes, again supporting the prediction of logarithmic growth behavior.

### 4.2.4. Microstructure evolution modeling

In the present context, the term “microstructure modeling” refers to mechanisms that alter the grain, subgrain/cell and dislocation structure of the metallic matrix as well as its corresponding stress-strain response (strain hardening). The particular mechanisms that are important for green Al production are the segregation of impurities into, and their interactions with, dislocations and grain/

<figure><img src="image_37.png" /><figcaption>Fig. 35. Vacancy trapping in Al-0.5 Mg-1Si-0.05Sn and corresponding deceleration of diffusion kinetics. Reproduced with permission from [558].</figcaption></figure>
<figure><img src="image_38.png" /><figcaption>**Fig. 36.** Vacancy trapping and relative deceleration of diffusion kinetics in pure Al, Al-0.5 Mg-1Si and Al-0.5 Mg-1Si-0.05Sn. Numbers refer to the fraction of diffusivity with solute trapping in comparison with pure Al. Reproduced with permission from [558].</figcaption></figure>

<figure><img src="image_39.png" /><figcaption>**Fig. 37.** Illustration of a solute cluster with gray and black circles representing solvent and solute atoms, respectively. The arrow illustrates a vacancy jump, which will free the vacancy from some solute atoms but place it next to other solute atoms within the cluster. Reproduced with permission from [560].</figcaption></figure>

subgrain boundaries. Some areas where scrap elements might potentially impact microstructure evolution are briefly outlined below.

It is well known that impurities have a certain tendency to diffuse to the stress field of dislocations. On the one hand, these atoms then influence the mobility of the dislocations by forming Cottrell atmospheres, an effect that is familiar in steel with respect to carbon atoms. Numerous publications deal with the phenomenon and modeling of this effect [563–568]. In Al-alloys it has been shown that the effect of cross-core diffusion can have a significant impact, and it is well documented that Mg is a crucial element in this context. Soare and Curtin [569,570] developed a rate theory for dynamic strain aging, a mechanism that is made responsible for the Portevin–Le Chatelier effect in aluminum alloys containing Mg [571,572]. The Soare and Curtin model was recently utilized by Kreyca et al. [573,574] to simulate stress–strain curves in Al-Mg alloys and to predict regions of negative strain rate sensitivity. Kabirian et al. [575] use a constitutive approach to model the same phenomenon.

Dissolved impurities also tend to segregate to grain boundaries, and thus impact the mobility of planar defects by solute drag. It has been demonstrated that even small amounts of solute can have severe effects, for instance on grain growth and recrystallization. A classic study in this context is the analysis by Huang and Humphreys [319,320], which investigates the impact of Mg, Mn and Si on grain boundary mobility. The information on mobility was recently deployed by Buken and Kozeschnik [474] in a simulation of recrystallization in Al-Mg alloys, where the significant impact of Mg on the kinetics of recrystallization is observed both experimentally and in simulations. Fig. 38 shows grain boundary mobility from this analysis as function of temperature and Mg content. A particularly strong effect can emerge when the Mg content increases substantially, for instance where there are high levels of contamination from scrap.

### 4.2.5. Future challenges for green aluminum modeling and simulation

The models reviewed above can be used in simulations of scrap-related alloying effects. However, several pieces of the green aluminum puzzle are missing, and some represent mechanisms which have a severe impact.

One topic is the question of the exact nature of interaction between solute atoms and quenched-in excess vacancies. What is the nature of the vacancy trapping in 6xxx alloys that makes the kinetics of natural aging insensitive to cooling rates after solution annealing? Why is the kinetics of natural aging similar, independently of how the quenching process is performed? Why do water quenching, cooling with ventilated air and still air cooling result in natural aging kinetics occurring within one order of magnitude of
time [576–578], while simulations of excess vacancies (see model of Fischer et al. [579]) deliver excess lattice vacancy concentration differences of several orders of magnitude? A detailed analysis and overview of the role of excess vacancies in natural and artificial aging which addresses many of these questions, particularly for the Al-Mg-Si system, was recently provided by Yang and Banhart [544].

What is the role of atomic pairs and couples in this context? Madanat et al. [577] speculate that vacancies can be very efficiently trapped at Si-Si pairs and that the corresponding trapping enthalpies should be significantly higher than those of individual Si atoms. The modeling of this effect is possible in principle using the models for couple and pair formation recently developed by Svoboda et al. [580,581], combined with the vacancy trapping approach reported here. Corresponding analyses are still lacking in literature, and conclusive models describing the mechanism of vacancy trapping during quenching of 6xxx alloys are needed. In 6xxx alloys with a high level of dispersoids the effect also seems much less pronounced, as demonstrated for instance by Strobel et al. [582]. This has immediate relevance for green aluminum alloys, where elevated levels of Fe can be expected. Here the quenching rate may assume renewed importance.

What, exactly, happens during natural aging? How can the thermodynamics of cluster formation be described properly, keeping in mind that thermodynamics is a theory involving systems large enough to allow statistical averaging? Clusters consist of a very limited number of atoms. Can atomistic modeling deliver correct answers, and can the associated approaches be extended to sufficiently large systems? Reports on successful applications exist, but these do not properly describe the observed compositional variations of 6xxx alloy clusters during natural aging. These clusters can be Si-rich, Mg-rich, or anything in between [561,583].

Can the application of Classical Nucleation Theory be justified in precipitation modeling in systems at high supersaturation, which are typical for quenched Al-alloys? The driving forces are often so high that the sizes of critical nuclei fall below the dimensions of a single atom, and clusters form homogeneously in the undisturbed crystal with no conceivable nucleation barrier. Can compositional gradients around near-critical nuclei be neglected [584]?

What is the exact nature of the transformation of clusters and precursor phases into precipitates with distinct crystal structures, e.g. $Mg_5Si_6 (\beta')$ particles in 6xxx alloys, $\eta'$ in 7xxx alloys [585] or $\Theta'$ in Al-Cu [542]? What about particle-into-particle transformation versus independent classical nucleation of the same phases (without precursor phases) in the bulk volume of the Al matrix, for instance during direct aging treatments?

What is the role of heterogeneous nucleation sites? In continuous cooling DSC experiments with medium to slow cooling rates, the stable $Mg_2Si$ seems to form very close to its thermodynamic solvus. Classical Nucleation Theory predicts that a necessary undercooling of >100 K will take place before this phase nucleates, in clear contrast to experimental evidence. The energy of heterogeneous nucleation sites, e.g. grain boundary corners and edges, must also be taken into account.

Is precipitate growth controlled by diffusion, while precipitate dissolution is controlled by the interface or reaction rate? There is some evidence that these mechanisms may be important in the evolution of clusters and other particles [467].

There is also a need to further investigate the impact of solutes on the mobility of linear and planar defects. Solute drag may potentially play an important role in alloys containing (uncontrolled) amounts of impurities that show strong segregation tendencies towards grain boundaries and dislocations. Variations in grain boundary mobility can require the adaptation of entire alloy processing regimes to achieve stable production of green aluminum products.

### 4.3. Combinatorial metallurgical methods to study the effects of tramp elements

The processing parameter scope of classical aluminum alloys has many dimensions. These comprise chemical composition in the

<figure><img src="image_40.png" /><figcaption>**Fig. 38.** Mobility of grain boundaries in Al-Mg alloys as function of temperature and Mg content. The HP-Al line was calculated for an Mg content of $X_{Mg} = 10^{-8}$. Reproduced with permission from [474].</figcaption></figure>
multi-component alloys (typically 4 to 5 different species that have a first-order influence on alloy properties), and processing parameters, whose number can be very large if the entire processing route of the alloy is considered and typically consist of temperatures (of rolling, heat treatment); heating and quenching rates; heat treatment duration; amount of plastic deformation, etc. All of these parameters affect final properties via differences in microstructure at different scales: grain size, morphology and texture; intermetallics and dispersoid distribution; and nanosized precipitate nature, shape, size, density, distribution and dislocation density. When dealing with recycled alloys the diversity of the parameters to be investigated is potentially even greater, with new chemical species added to the classical ones, guiding us into regions of the alloy design space which are essentially unexplored. Innovative processes such as additive manufacturing or severe plastic deformation (see section 3.6) are also frequently required to mitigate the negative effects of adding impurity elements, and are also mostly uncharted in the alloy design sense.

Finding optimal solutions within such a large design space is an immense task. The traditional route would combine an understanding of the physical and mechanical processes that govern microstructure development and the resulting properties with predictive integrated modeling and a few experimental validations. However, in corners of the design space where little knowledge and data are available, such as the area of recycled Al-alloys, the predictive capability of existing knowledge and models is likely to be insufficient. There is a strong need for extensive experimental data to improve matters and identify optimal solutions.

Obtaining extensive datasets on microstructure and properties requires us to develop specific high-throughput methodologies. These can be grouped under the generic name "combinatorial methods" [586,587], although in metallurgy such methods are seldom truly combinatorial (in the sense of randomly combining many parameters). The objective is to quasi-continuously vary a few alloy design parameters (whether compositional or processing-related), and be able to measure, also quasi-continuously, the relevant microstructure parameters and properties of interest [588]. Possible strategies for these two steps are detailed below.

### 4.3.1. Methods for high-throughput material fabrication

It is possible to systematically vary alloy design parameters either continuously, by establishing materials with gradients of composition or processing materials [83,589], or discretely, by quickly fabricating a large number of samples in a controlled manner [590]. When dealing with gradient materials, the magnitude of the gradients is important. Too-small gradient sizes in variation may impact the transformations to be studied. For instance, a very steep composition gradient (e.g., a few µm) may influence diffusion-controlled phase transformations and (even more) the transferability of results on microstructure evolution (e.g. recrystallization and grain growth) to larger scales. Thus the range of parameter variation within a gradient sample should be much greater than the characteristic size scale of the microstructure to be studied. The microstructure of the graded material should also be representative of the microstructure in bulk alloys, especially if mechanical properties are to be measured. This imposes particular requirements on the fabrication process of samples. For example, combinatorial studies of functional properties (thermoelectric efficiency, shape-memory effect, etc.) have often focused on fabricating compositionally graded materials using physical vapor deposition [591,592], which enables ternary planar gradients to be achieved in a straightforward manner. This method, however, is ill-suited for the study of metallurgical processes because the fabricated materials have a very limited thickness. The dimension of the gradient should also be of the appropriate scale with respect to the characterization methods applied to evaluate the microstructure or the properties as a function of the local parameter value. This depends on the phenomenon to be characterized and the technique used, but as a rule of thumb the resolving capability of high-throughput characterization is on the order of 100 µm, demanding gradients of several millimeters in order to resolve a sufficient number of parameter values. This requirement means limitations for fabrication methods that imply solid-state diffusion to create the gradient, especially where the influence of slow-diffusing elements (such as Fe for the study of recycling) is to be investigated.

Compositionally graded aluminum alloys have been fabricated mostly by making diffusion couples between two alloys of the end compositions in the gradient [593–595]. Successfully achieving the required continuous gradient requires an additional criterion: a temperature must exist where the alloy is in a single (solid) phase for all compositions within the gradient, so that the interdiffusion heat treatment can be realized without composition partitioning. When joining aluminum alloys in the solid state, a difficulty is the presence of the oxide layer, which prevents good metallurgical bonding. However, interdiffusion can still be achieved by simple hot pressing [593]. Due to time limitations, large gradients cannot be obtained in this way, and extending the gradient is hard due to the brittleness of the joining interface. An alternative is to use a solid-state joining technique which removes the oxide layer at the junction. Dissimilar friction stir welding is a widely available technique, but has the disadvantage of a very complex 3-D distribution of the two joined materials. This is complicated to transform into a smooth gradient by diffusion. Linear friction welding has been successfully applied to generate planar oxide-free dissimilar joints, which could then be extended by interdiffusion and plastic deformation (rolling) [594,595], as illustrated in Fig. 39. The advantage of this strategy is that the graded material is made from wrought alloys, which are representative of the bulk end alloys.

Powder metallurgy has also been used to fabricate compositionally graded material. By mixing different powders, several end alloy compositions can be targeted and then placed in a crucible before sintering, e.g. using spark plasma sintering [596]. Compositionally graded materials can be fabricated in this way, although the methodology has yet to be validated in the context of aluminum alloys. Because sintering breaks the oxide layers, the compacts thus obtained can be further processed by interdiffusion heat treatments and plastic deformation in order to reach the appropriate scale of the gradient.

Combinatorial thin-film synthesis techniques, for instance magnetron sputtering, may be suitable methods for efficiently screening and exploring a large variety of chemical compositions with respect to changes in microstructure. Compositionally graded films sputtered on one substrate can consist of 5 elements [597], which may include elements familiar in aluminum scrap such as Fe and Si. The screening of such compositionally graded samples may inspire a focus on interesting specific chemical compositions, which can be further investigated with the corresponding bulk material.
However, due to the high solidification rate of energetic particles, the conditions of thin film growth may be unbalanced [598]. Magnetron sputtering is considered a far-from-equilibrium technique [599], and films produced by it might not be directly comparable with bulk samples produced by conventional casting techniques. During the magnetron sputtering process it may be possible to reduce the solidification rate by heating the substrate, as shown during plasma-spraying [600]. To obtain thermodynamically stable phases and to mitigate the effects of defects, annealing treatments are often applied to co-sputtered thin films [597]. Compositionally graded material might also be developed by casting techniques using one chemical composition. It has been shown that the Fe content in 3104 ingots can vary between the surface and the center of the ingot produced by DC casting [45].

An alternative to using samples with gradients of composition or processing parameters is to fabricate a large number of samples, each with different parameters (e.g. of composition). This may be achieved by direct metal deposition, although the obtained materials may not show microstructures representative of bulk wrought alloys. A large set of bulk samples were shown to be made with the DC casting method [210]. 950 industrial ingots within the compositional limits of 6008 alloy were produced and processed to study the effects of the main impurity elements Fe and Si on mechanical properties such as yield strength and tensile strength [210]. While an increase in the Fe content without increasing the Si content can reduce mechanical properties, a high Si content combined with low Fe content can increase them. It may be necessary to compensate a high Fe content with an increase in the Si content to balance the Fe-Si reaction [210]. Bulk samples can also be made using traditional casting and processing methods, miniaturized and automatized in order to facilitate duplication in large numbers. This Rapid Alloy Prototyping strategy has been used to design steels [590,601,602]. Although the number of parameters that can be reached is more limited when compared to gradient materials, this strategy has the advantage of making it possible to evaluate bulk properties such as fracture stress or even fracture toughness.

### 4.3.2. Methods for high-throughput microstructure and property characterization

Resolving microstructure features and properties with a high throughput capability is the next challenge. Both direct observation and indirect methods can be used for microstructure evaluation. High throughput necessarily means automated data analysis. In addressing large particles or grain size, optical or scanning electron microscopy may be used [603], where data acquisition and analysis can be automated if the contrast is sufficiently uniform. The situation is more complicated with nanometer-scale micro-structure features, such as nanoscale precipitates. Spatially-resolved observation of graded alloys in a transmission electron microscope has been achieved for Ni based alloys [589], but with a compositional gradient scale which is necessarily of the same order of magnitude as the characteristic distance of precipitation. The alternative is to use indirect measuring techniques such as X-ray diffraction, small-angle X-ray scattering (see section 4.6), differential scanning calorimetry (DSC), or spatially resolved conductivity measurements. Some methods need development or adaptation (such as robotic sample handling for DSC) to approach high throughput. With DSC it is difficult to obtain more than a few hundred measurements in a reasonable amount of time (weeks!). Using X-ray methods, on the other hand, it is possible to obtain $10^3-10^4$ results easily in a few hours if using synchrotron sources and appropriately prepared graded samples [83,604]. X-ray methods are also adapted to in-situ measurement, making it possible to study the kinetics of microstructure evolution in graded material by simultaneously performing space- and time-resolved experiments. Fig. 40 illustrates this for an Al-Cu-Li alloy with variable Mg.

In terms of mechanical properties, the feature that can be spatially most easily resolved with high throughput is hardness, with a resolving power of ~100 µm for microhardness and ~1 µm for nano-indentation. It is reasonably possible to obtain datapoints on the order of $10^3-10^4$ in a few hours [605]. However, hardness is only a rough indication of mechanical properties and it is usually desirable to characterize them more precisely. When dealing with graded samples, it is possible to spatially resolve the yield stress by applying a tensile test parallel to the gradient of the composition / processing parameter, while measuring the strain distribution by digital image correlation. A requirement for achieving this is that the gradient of yield stress should be sufficiently modest for the measurement in the hardest zones of the gradient not to be affected by the plasticity in the softest zones (change in cross-section and possibly fracture). Measurement of fracture properties is much more challenging, as these properties have a strong size dependence and measurement is difficult to achieve on graded samples. But inspiration can be taken from other alloy families, where, for instance, the forming ability of

<figure><img src="image_41.png" /><figcaption>**Fig. 39.** Diffusion couple made by linear friction welding from two Al-Cu-Li alloys of variable Mg content. (Top left) Micrograph after welding. (Bottom left) Micrograph after homogenization. (Right) Composition profile after the different processing steps. Figure used with permission from [594].</figcaption></figure>
metallic glasses has been mapped in compositional space using innovative arrays of holes made in a two-dimensional composition gradient [592]. Other properties related to alloy durability can be also mapped; this is the case for electrochemical properties using Kelvin Force microscopy [606]. Local electrochemical properties can also be obtained by using a scanning droplet cell (SDC). It has been shown that SDC microscopy can be successfully combined with an automatic robotic system, which can provide an efficient automated characterization of co-sputtered thin film surfaces [607]. Multi-SDC microscopy can be applied to conduct electrochemical measurements on several (e.g. 8) local surface spots at the same time [608].

### 4.4. Chemically highly-resolved characterization of tramp elements

Understanding the metallurgical influence of impurity elements on the solid solution and precipitation state of recycled Al-alloys requires that these elements be observed with high chemical and spatial detection sensitivity. Standard techniques used for this purpose are the various chemical sensing methods used in Scanning and Auger Electron Microscopy; Glow Discharge Optical and Mass Emission Spectroscopy; Secondary Ion Mass Spectrometry; X-ray Photoelectron Spectroscopy; and Rutherford Backscattering. When aiming at the characterization of tramp elements at near-atomic scale, spatial and chemical resolution typically involve a combination of more advanced techniques, often culminating in the various imaging, diffraction and spectroscopic methods available in the (scanning) transmission-electron microscope (TEM) and atom-probe tomography (APT). Each of these two techniques has its strengths, limitations, and idiosyncrasies, but they offer a combination of the highest possible spatial resolution and compositional accuracy, specifically when performed in combination on a single specimen [609,610]. The operation and challenges associated with each technique are explored in the following sections. APT is addressed first, including opportunities to link it with atomistic simulations. Second comes (S)TEM. Finally, we discuss examples and challenges associated with both techniques as regards the chemically highly-resolved characterization of impurity elements in Al-alloys.

<figure><img src="image_42.png" /><figcaption>**Fig. 40.** Parameters of the nanoscale cluster and precipitate microstructure obtained by simultaneous time- and space-resolved SAXS experiments performed during natural aging of an Al-Cu-Li-Mg compositionally graded alloy of varying Mg content. Figure used with permission from [595].</figcaption></figure>
4.4.1. Atom probe tomography

APT relies on the time-controlled removal (evaporation) and projection of individual atoms, in the form of low-charge state ions, from a needle-shaped specimen (a nanotip) onto a position-sensitive detector, as depicted in Fig. 41a. The ions are created by the process of field evaporation; that is, under the influence of an intense electric field generated by subjecting a sharp needle-shaped nanotip to high voltage of a few kilovolts (for tungsten, for example, the evaporation field is 40 V/nm). The ion trajectories are defined mostly by the distribution of the electrostatic field surrounding a sharply pointed nanotip [611], but also by the shape of the nanotip and the geometry of the APT itself [612]. The measurement of the time-of-flight (ToF) of each ion proceeds via nanosecond high-voltage pulses or ultraviolet picosecond laser pulses (sometimes femtosecond laser pulses) superimposed on the static electrostatic field. The pulses provide time-control of the departure of each ion. Thus it is possible to field evaporated atoms essentially one

<figure><img src="image_43.png" /><figcaption>**Fig. 41.** Schematic overview of atom probe and transmission-electron microscopy in combination. (a) Needle-shaped atom probe specimen containing a grain boundary and a range of second-phase precipitates both intragranular (in yellow and pink) and intergranular (dark blue and magenta). The specimen is located in front of the counter-electrode enhancing the intensity of the electrostatic field. Ions emitted by the specimen fly towards the position-sensitive detector and their time-of-flight is recorded, along with the impact position. (b) Because phases require different electric field to be field evaporated, different curvatures develop, generating either compression or dilation of the ion trajectory that affects the imaged size of the particles and causes changes in the impact density on the detector maps. (c) A cumulative histogram of the impact positions shows a pattern that can reveal symmetries of the crystal and differences in the density which typically indicate different phases. (d) Cartoon view of a section of a mass spectrum highlighting some of the possible isobaric and charge-state-related overlaps, or issues associated with background levels or mass resolution. (e) Projected image of the corresponding microstructure within the atom probe specimen obtained by the transmitted electrons. (f) The through-thickness and corresponding top view of the atomic arrangement inside the specimen and examples of a Z-contrast image and possible X-ray energy dispersive spectroscopy map obtained from the corresponding top view. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
atom at a time and atomic layer by atomic layer. During post-processing of this metadata, the times-of-flight are converted into a mass-to-charge-state ratio for each ion, and the impact position on the detector is reverse-projected by assuming a certain projection law onto a virtual model of the emitting nanotip specimen [613,614].

A number of challenges are generally encountered during APT analyses, and these can be more critical in the case of Al-alloys. First, as indicated in Fig. 41b, the surface of the emitter can develop local radii of curvatures. In addition, the critical electric field necessary to trigger field evaporation depends on the composition, and probably also the structure, of each of the phases [615–617]. Subtle roll-up-type, short-range displacements of atoms on a specimen's surface prior to their departure cause the spatial precision of the measurement to degrade [618–620]. Because a second phase precipitate intersects a specimen's emitting surface, a different radius of curvature also develops locally according to the magnitude of the electric field (and local work function) required to initiate field evaporation, i.e. the so-called evaporation field. The magnification of the ion's projection depends directly on the local radius of curvature of an emitter; therefore a change in the local radius of curvature leads to either compression or dilation of an ion's trajectory if the evaporation field is respectively greater or smaller than that of the matrix [621]. This effect is referred to as the local magnification, which causes an artificial expansion or compression of the imaged dimensions of a precipitate [622,623]. This can also be observed as a variation in the impact density on the detector maps, as suggested by a series of detector hit maps obtained during an analysis near a grain boundary in a 7xxx-series alloy, Fig. 41c. Changes in the local point density are also typically observed at grain boundaries and more generally at crystalline imperfections [624,625]. They are associated with the shaping of a specimen's surface or end form, which permits retrieval of information pertaining to the local crystalline structure and orientation [626]. This latter aspect has been utilized efficiently in the case of Al-alloys [627–629]. Fig. 41d displays a cartoon of a portion of a mass spectrum, showing a series of peaks, often observed in the analysis of Al-alloys, which highlight some of the possible isobaric and charge-state-related overlaps. These effects are discussed in greater detail below.

APT has been used extensively for the analysis of precipitation in Al-alloys [531,630–638]. However, in these materials local magnification effects are critical. Examples for high-field precipitates, which are imaged larger than their real size, are Cu-rich GP zones, θ' [639] or T₁ precipitates in the 2XXX-series [640], β'-precipitates in the 6xxx-series [641]; Al₃Zr and Al₃Sc precipitates. Low-field precipitates, which are imaged smaller, are for example the η'-precipitates in the 7xxx-series or Al-Ag or Al-Li δ' precipitates [642].

Trajectory aberrations lead to a crossing of the ions' trajectories, resulting in an intermixing of atoms from within precipitates and the matrix which affects the accuracy of the composition measurements [643]. The precision of the spatial and compositional measurements in APT is hence extremely complex because it is determined by the details of the field-evaporation process and the local environment of each atom at a specimen's surface. Recent reports state that even segregated, first-nearest-neighbour (NN) atoms at a specimen's surface before departure are not reconstructed as NN atoms after analysis [644], raising questions about a number of literature analyses of the early stages of clustering in Al-alloys [562,645–647]. These aspects are discussed extensively by De Geuser et al. [641].

### 4.4.2. Analysing tramp-related solute atoms and precipitates in atom probe tomography data from aluminum alloys

Analyses of solute atoms in aluminum-based alloys start with atomic scale fluctuations in solid solution, which lead to the formation of short-range order (SRO) or clustering of atoms to form ultimately stable precipitates or nuclei (in the language of classical nucleation theory (CNT)) [648]. An unstable nucleus is referred to as an embryo in CNT. One approach to this problem is to apply partial radial distribution functions (p-RDFs) [649–651]; a sub-routine for this analysis is readily found in most data analysis programs [652]. This permits one to distinguish between clusters of solute atoms (embryos) and stable precipitates (nuclei); a specimen may contain both embryos and nuclei simultaneously. Stable precipitates may also exhibit a certain degree of SRO, which increases as the mean radius, $<R(t)>$, of precipitates grows with increasing aging time. Partial RDFs also permit us to distinguish between repulsive versus attractive interactions between different species [653]. The identification of precipitates (nuclei) can sometimes be accomplished using cluster-finding algorithms [654] and, when larger, via iso-concentration surfaces and proximity histograms [655,656]. No specific approaches need to be developed for SoDA, i.e. to detect effects associated with scrap-related tramp elements, but for elements with low concentrations care must generally be taken with parameter selection [657,658].

### 4.4.3. Combination of atom probe tomography with atomistic simulations

In the context of scrap-related contaminants, the scale of volumes imaged by APT and the fine scale resolution make the technique particularly useful in combination with atomistic simulations: the simulations help to interpret experimental atom-probe tomographic data, while APT helps inform the simulations. Here multiple techniques have been deployed, including Monte Carlo [647,659,660], molecular dynamics [661,662] and density-functional theory [663,664], particularly in the area of Al-alloys [665–669]. Many efforts have involved vacancy-mediated lattice kinetic Monte Carlo (LKMC) simulations. LKMC allows the modeling of diffusive processes in alloys mediated by a vacancy mechanism. Here a mono-vacancy changes places with the nearest NN atoms [392], and the necessary energy interaction parameters can be obtained from density-functional theory. Specifically, LKMC is capable of studying nucleation, growth, and coarsening of precipitates in great detail [660,670–672], while allowing comparison with coarsening models [673–675]. In the area of Al-alloys, precipitation in binary and ternary alloys, Al-Sc, Al-Zr, Al-Sc-Zr and Al-Sc-Mg have been studied extensively [676–680], especially the core / double-shell nanoprecipitates produced by solid-state precipitation [681,682]. Correlations from LKMC and cluster dynamics were drawn by for such systems by Clouet et al. [531,536,683–685], revealing many details of the phase separation mechanisms. Up to now such studies utilizing APT in conjunction with atomistic simulations have been performed for intentional, controlled alloying additions and for mixtures of small numbers of species. They have not yet been directly applied to the SoDA concept.
### 4.4.4. Scanning transmission electron microscopy

The following (scanning) transmission-electron microscopy (STEM) processes are well documented in the literature [686]: (i) an electron-beam is generated and accelerated, typically to 80–300 keV; and (ii) a thin specimen, usually of between 10 and 150 nm for metallic samples, is placed in the beam. This beam can be a large parallel beam flooding the sample as in TEM (see Fig. 41f), or a finely focused probe that can be scanned over the specimen (STEM) or kept stationary (see Fig. 42). The interaction between the incident beam of electrons and the specimen generates many different signals, the most useful containing the transmitted electrons. These can be collected to form images and diffraction patterns and yield spectroscopic data. X-rays are also generated, and can provide chemical information. To characterize metallic alloys at high spatial resolution, HAADF (high angle annular dark field) STEM and energy dispersive X-ray spectroscopy (EDXS) in STEM mode (STEM-EDXS) have been the techniques most commonly used in the last decade, because they can provide atomic resolution of the projected two-dimensional structure and chemistry. Many further (S)TEM techniques are available, and are becoming increasingly relevant to the characterization of alloying elements; these are also discussed briefly below. A schematic illustration of these techniques is displayed in Fig. 42 and Fig. 41f.

HAADF-STEM collects electrons scattered at high angles and thus provides contrast approximately proportional to the atomic number Zⁿ, where n ~ 1.7, depending on many factors such as specimen thickness, local atomic environment, probe size, etc. [687]. Although HAADF-STEM was first developed in the 1970s [687], it is only since the mid to late 2000s that it has become the technique of choice for characterizing the structure of materials at the atomic scale. This revolution has been enabled by major improvements in electronics which permit the accurate scanning of atomically-sized electron probes. It has been accelerated by the advent of aberration correctors [688], which have further improved spatial resolution to sub-angstrom levels. HAADF-STEM is particularly advantageous for embedded particles or interfaces. This contrasts with high-resolution TEM, where the image contrast is generally much more difficult to interpret (compare for instance, the images of Ag-rich interfaces in [689,690]).

The Z contrast in HAADF-STEM images is a form of chemical characterization and is especially useful when imaging chemical elements with very disparate atomic numbers. Examples relevant to aluminum alloys and other light alloys include the characterization of atomic clusters [85,691], the chemical segregation of elements at precipitate interfaces [383,384,690,692,693] and lattice defects [694]. In some cases, atomic clusters or interfaces containing only a handful of atoms [85,383,691] can be detected. Even for materials containing elements with widely spaced Z numbers, HAADF-STEM does not always provide unambiguous characterization of the chemistry and atomic structure. For example, features buried in the sample rather than near its top surface will not be imaged

<figure><img src="image_44.png" /><figcaption>**Fig. 42.** Schematic overview of scanning transmission electron microscopy (STEM). (a) A focused electron beam incident on a thin specimen is scanned laterally. Transmitted electrons which have interacted with the specimen are detected at different angles and locations, where they form images, diffraction patterns and provide spectroscopic information. Electrons scattered at high angles are detected by a high angle annular dark field (HAADF) STEM detector, which will result in images dominated by atomic number (Z) contrast. Electrons scattered at lower angles pass through the hole of the HAADF-STEM detector and may be used to form other signals which can provide additional structural or chemical information: STEM images in bright field (BF), annular bright field (ABF) or low-angle annular dark field (LAADF) modes; convergent beam electron diffraction (CBED) patterns; and electron energy loss spectra (EELS). X-rays are also generated and can be analysed through energy dispersive X-ray spectroscopy (EDXS). (b) A schematic EDX spectrum shows the characteristic X-ray peaks associated with specific chemical elements. If some of the peaks are selected, elemental maps can be generated in conjunction with images in HAADF-STEM (or other STEM image modes). At high resolution, the projected atomic structure of the specimen may be visible, with brighter regions indicative of greater Z density in projection. The case shown here comprises an Al-Cu-Ag alloy with Cu-rich and Ag-rich precipitates. Ag atoms segregate to the Cu-rich precipitate-matrix interfaces, as revealed by HAADF-STEM imaging and EDXS maps for Cu and Ag [690].</figcaption></figure>
clearly due to broadening of the electron probe as it travels through the specimen [695]. Although this may lead to significant errors in
interpreting the images, simple strategies are available for avoiding these pitfalls. They involve recording electron diffraction patterns
and simulating images, and are similar to the strategies required (e.g.) to ascertain the validity of the interpretation of most (S)TEM
images.

It is important to mention that STEM imaging modes other than HAADF are easily accessible in modern instruments and can provide extremely useful and complementary information. For instance, bright-field (BF) and annular bright field (ABF) STEM can image light elements (e.g. Li) [696,697], while low or medium angle annular dark field (LAADF, MAADF) STEM can image strain fields and lattice defects [85]. The following fact should also be stressed, as it is often poorly appreciated: in ADF-STEM modes the electron diffraction pattern at each pixel can be recorded simultaneously (four-dimensional (4D)-STEM) [698] or as an average (position averaged convergent beam electron diffraction, PACBED) [699]. These reasonably new techniques will become powerful tools for multiscale characterization of structure and chemistry [698], including that of light alloys.

Obtaining direct chemical information in the TEM requires spectroscopic data. This can be acquired using EDXS or electron energy loss spectroscopy (EELS). EDXS is preferred for most applications involving metal alloys because it can be used on the typically thicker specimens (>20 nm) of alloy samples; it also discriminates well between different chemical elements with Z > 5. EDXS detects characteristic X-rays emitted from the sample when electrons from the atomic inner shells of the material excited by the electron beam return to their original states. When used in the STEM, atomically-resolved EDXS maps can be obtained which provide information which is complementary to HAADF-STEM results. Examples relevant to aluminum and other light alloys are currently limited to precipitate structure [700] or segregants in significant quantities along the electron beam direction (i.e. several nanometres), such as at planar defects [701]. This is because only a small fraction of the X-rays emitted is collected, even in current state-of-the art X-ray detectors, where it is ~ 8% at best [702]. It should also be emphasized that as in HAADF-STEM images, the EDX signals may not come from an atomic column which was hit by the electron probe on the sample's top surface. Therefore, without detailed simulations great caution must be exercised when interpreting atomically resolved EDXS maps [703]. Another important limitation of EDXS is its inability to detect light elements such as Li or H, which are important alloying or trace elements in Al-alloys. If a TEM is employed, however, EELS can be used to detect Li, and this has been applied to Mg-alloys [704]. In fact, EELS can be extremely useful depending on the elements of interest, because in contrast to EDXS most of the EELS signal is detected and can be attributed to single atomic columns. To date the application of atomic-resolution EELS to light alloys has been limited to probing internal and interfacial precipitate chemistry [705,706]. However, the success of EELS in probing atomic clusters or even single dopant atoms in non-metallic materials [707,708] offers the prospect that this technique might be employed to characterize the chemistry of trace elements near the atomic scale in metallic alloys.

As seen above, one of the main limitations of (S)TEM is the difficulty of accessing the third dimension of a sample. Three-
dimensional structural and chemical information can be obtained by deploying the sophisticated techniques of electron tomogra-
phy [709,710], atom counting in its different forms [384,711,712] and quantitative convergent beam electron diffraction (QCBED)
[713]. Although they currently require significant experimental and computational effort to achieve trustworthy results, these
techniques are extremely powerful. For example, the size and location along the incident electron beam direction of nanovoids in
aluminum can be determined with sub-nanoscale accuracy [714].

Another substantial limitation of (S)TEM at the nanoscale and sub-nanoscale is the poor statistics it provides due to the relatively small volumes it can examine. However, this limitation may cease to apply in the near future thanks to the application of different electron diffraction methods together with new-generation electron detectors and powerful computational tools [698]. For instance, scanning precession electron diffraction (SPED) has recently been used in a statistical analysis of nanoscale precipitates in 6xxx alloys [715].

Other potential problems associated with (S)TEM are sample preparation artefacts and electron beam damage. Luckily, aluminum alloy specimens for (S)TEM can generally be prepared relatively easily by electropolishing or ion beam thinning. Beam damage can be mitigated by applying lower accelerating voltages (as low as 30 kV in the latest off-the-shelf (S)TEM instruments) and specimen stage cooling, or lower electron doses thanks to the more sensitive detectors currently available. In fact, many technological and technique developments from the last decade have not yet been exploited for the characterization of trace elements in light alloys at high spatial and chemical resolution.

4.4.5. Examples and challenges associated with the chemically highly resolved characterization of impurity elements

Both APT and (S)TEM have been extensively applied to characterize Al-alloys. APT, in particular, has been used to study the distribution of minor alloying additions [716,717]. However, only a few studies have focused on the role of actual scrap-related impurities [718–721], e.g. Mn, Fe or Si integrated within precipitates or in intermetallics, as commonly seen in alloys in the SoDA field. This is in part due to challenges inherent in analysing impurity elements by APT and TEM. Let us consider two key aspects.

First, impurity elements typically exhibit low solubility in the FCC-Al matrix, and hence tend to be more highly concentrated within secondary phases or segregated at crystalline defects. These include grain boundaries, where significant segregation and consequently precipitation often occurs. Segregation is often the precursor to precipitation. It is driven by a decrease in the Gibbs free energy of a grain boundary (GB) according to the Gibbs adsorption isotherm. Yet from a characterization standpoint, GBs pose significant challenges. They are typically considered as two-dimensional structures, although they can assume complex shapes in three-dimensions. High-resolution characterization of these GBs typically requires dedicated approaches to prepare specimens suitable for both TEM or APT. Site-specific GB preparation is hindered in Al and its alloys due to the use of gallium in the liquid–metal-ion source of the focused-ion beam used to perform targeted specimen preparation for APT [722]. Ga is known to be a strong segregant in Al [720], in particular at GBs, and can cause severe embrittlement [723]. This can be overcome by using other types of ions, e.g. xenon from a plasma (PFIB)
source [724]. Recently Lilensten et al. demonstrated that these issues can be overcome by using a cryogenic stage to form an APT specimen [725], and similar results can probably be obtained for TEM-lamellae based on observations in other alloy systems [726]. These advances in the preparation of specimens from bulk model alloys [727], including those intended for high-resolution imaging [728], and of industrial alloys [729], are important.

Second, there is an issue with the sensitivity limit of each technique. Sensitivity in STEM-EDXS is at best 0.05% [702], depending on the elements being considered and the microstructural feature being investigated [703]. For dilute elements within the matrix the effect of the through projection further reduces the signal from the element of interest, because most of the X-rays are generated by the matrix atoms. This is also true for the imaging, as discussed above: light elements tend to go undetected. When considering impurity elements, if segregated at second-phase precipitates or at crystalline defects their concentration may not be sufficient enough to be detected. In APT, the issues are different. Fig. 41 showcases some of them. There are isobaric overlaps, and molecular ions may form at the surface of the specimen with the residual hydrogen in an APT vacuum chamber which overlap with (for example) Si. This makes identifying the presence of some of the key impurity elements, such as Cr, Mn or Fe, extremely challenging. The volume analysed by APT is also relatively small; and numerous property-controlling microstructural features are on a larger scale or scarce, making them difficult to analyse by APT in particular, despite the possibility of target specimen preparation.

Despite these limitations, and the possible problems associated with APT, and to a lesser extent TEM, one must bear in mind that the combination of sub-nanometre resolution and high elemental sensitivity still makes APT an amazing and truly unique materials characterization technique, especially for analysing Al-alloys. Similarly, TEM remains unrivalled in the local, structural analysis of materials, even though substantial challenges require great care in the analysis and interpretation of the data. Efforts are ongoing to overcome many of these limitations, including the search for better ways to process the data originating from these techniques. An interesting perspective on the TEM side at least was recently provided by some of the experts in this field [730].

## 4.5. Advanced in-situ diffraction methods to study the effects of tramp elements on precipitation kinetics and thermodynamics

### 4.5.1. Introduction to small-angle X-ray scattering for the investigation of aluminum alloys

Monitoring the precipitation kinetics in age-hardenable aluminum alloys is of great importance in developing advanced alloy design strategies which optimize the heat treatments and thermo-mechanical processes of alloy variants with high scrap-related impurity content. This is because these alloys are strengthened by a fine intragranular distribution of precipitates which are usually metastable [720,731,732].

Because impurity elements do not normally change the nature of the main alloying elements, their effect on the age-hardenability of aluminum alloys should not drastically alter the precipitation “sequence” and the nature of the main stable and metastable phases that are expected to form within the grains. However, their effect on the kinetics of precipitation of those phases, and on their distribution (size and density), can be very significant. The kinetics of precipitation can be monitored by indirect means, e.g. via hardness, resistivity, dilatometry, calorimetry, etc. [413,544,554,636,733–735]. The relationship between the measurements and the actual precipitation reaction is, however, far from trivial and can lead to ambiguous interpretations. In addition, no information can be gained on the size and density of precipitates.

Transmission electron microscopy and atom probe tomography enable direct measurement of precipitation (see the preceding section), but not in-situ measurement in general (see the additional discussion and references in section 4.4) [385,636,645,736–739]. Scattering experiments are a good alternative, as they allow direct and in-situ monitoring of precipitation. X-ray diffraction (XRD) has often been deployed to study precipitates, but this is usually limited to coarse particles (grain-boundary precipitates, dispersoids, etc.) because strengthening precipitates are frequently coherent with the host matrix and generally too small to give an exploitable signal.

The technique of choice is thus small-angle X-ray scattering (SAXS). A more detailed description of SAXS principles can be found in [740,741]. SAXS is a diffraction experiment for which only the signal very close to the transmitted beam is collected. Because it focuses on small scattering angles SAXS, is sensitive to composition fluctuations at scales larger than the interatomic distances, so that the samples can be described by a continuous function $\rho(\vec{r})$ representing the local electron density. Small-angle scattering can thus be seen as a measure of the homogeneity of the sample, i.e. a homogeneous sample does not give rise to a SAXS signal. Interpreting SAXS data requires the construction of a model for electron density fluctuations. A widely used interpretation model for precipitation in alloys is the “2-phase” model which assumes precipitates of homogeneous composition in a matrix of likewise homogeneous composition, separated by a sharp interface [742,743]. Errors of the method are usually associated with the model used for signal interpretation, where the decisive parameters are the shape of the particles, the size distribution function, etc. It is thus important to associate SAXS analysis with complementary techniques such as TEM or APT to confirm the model’s assumptions.

### 4.5.2. SAXS monitoring of the precipitation kinetics in aluminum alloys

Because of the high contrast in atomic numbers between Al-Cu, Al-Zn, Al-Li, Al-Zr, Al-Ag, Al-Fe and Al-Mn, SAXS is well adapted for studying precipitation in most of the widely used aluminum alloy series, such as the 2xxx series or the 7xxx series. This is particularly true for typical impurity elements (such as Cu, Fe or Mn). The 6xxx series alloys, on the other hand, which are common in the automotive industry, require the use of small-angle neutron scattering (SANS) rather than SAXS, due to the lack of sufficient electron contrast between Al, Mg and Si [494,744,745]. SANS requires longer counting times, which is less favorable for in-situ studies, although some successful in-situ SANS experiments have been performed [746].

Guinier-Preston zones (GP zones) are at the origin of the small-angle X-ray scattering technique, as we know from the seminal work of André Guinier [747]. However, only a comparatively low number of studies have addressed this because of the difficulties of coping
with the anisotropic signal due to the large aspect ratio of the particles. Some recent works [748] used both small and wide angle scattering to monitor the archetypal precipitation sequence of metastable phase GP zones, θ", θ' and θ, which can be found in most textbooks. The body of literature concerning spherical precipitation in aluminum alloys studied in situ by SAXS is much more substantial, mostly because the isotropies of the precipitates and of the signal allow easier interpretation. The Al-Li system, with the formation of ordered coherent L1₂ δ' precipitates, has been the subject of many studies assessing the classical theories of nucleation, growth and coarsening [13–15]. This alloy system has also triggered studies of complex non-isothermal or multi-step aging processes and has provided evidence for the clear complementarity of in-situ SAXS studies of precipitation kinetics with thermo-kinetics pre-cipitation modeling [16]. This is shown in Fig. 43, where the regression of a pre-existing population of precipitates during an isothermal high temperature treatment is excellently reproduced by a Kampmann-Wagner-type class model for precipitation [17,18]. Other spherical L1₂ precipitates in aluminum such as Al₃(Sc,Zr) have also been characterized by SAXS.

Among the aluminum alloys of industrial interest, the 7xxx Al-Zn-Mg-(Cu) series has received much attention in terms of SAXS analysis. This series features high contrast; the early GP zones can be considered as spheres; and the metastable η' MgZn₂ phases, while they are faceted and their aspect ratio increases with time, can usually be approximated as spheroidal. These convenient interpretation conditions have motivated several SAXS studies, addressing effects of composition [749–751], pre-deformation [752], cooling rate [753,754], multi-step aging treatment [755] and severe plastic deformation [756].

The 7xxx alloy series is also suited for anomalous small-angle scattering (ASAXS), or resonant SAXS experiments, where energies close to the X-ray absorption edge of a specific element are used to modify the apparent electron density contrast Δρ and gain information on the chemistry of the precipitates formed. In-situ ASAXS experiments can probe the evolution of the chemistry of the precipitates as a function of aging time [755].

Some researchers have also used SAXS to map the precipitation state in heterogeneous samples of 7xxx alloys, such as in welds involving either MIG welding [757], electron beam welding [758,759], or friction-stir welding [760], but also after surface treatments [761]. This microstructure mapping of heterogeneous samples, together with combinatorial strategies, can generate high-throughput characterization methods, as discussed in section 4.3.

Other important Al-alloy families have been studied, but present interpretation challenges. These are mostly due to the anisotropic shape of precipitates and simultaneous addition of multiple contributions, in combination. This is true of the wide 2xxx series, which includes both Al-Cu-Mg-based alloys and Al-Cu-Li alloys. These alloys are strengthened by precipitates of high-aspect-ratio, with orientation relationships with the aluminum matrix. While these might be easily dealt with theoretically via either a perfectly aligned single crystal, or a perfect “powder” in the powder diffraction sense (i.e. grains which are small compared with the beam size, and random orientation), in real samples the 2D scattering patterns are much more complex and difficult to interpret.

In samples prepared from rolled sheets with a strong brass texture, this texture can be used and full modeling of the scattering signal of the platelet precipitates can proceed, depending on their habit planes [763]. The result can be a complete description of the precipitation kinetics, in particular including a separate assessment of the length and thickness of the strengthening objects to provide a better understanding of strengthening by non-spherical precipitates. Fig. 44 illustrates this, showing the precipitation kinetics of T₁ Al₂CuLi, which forms platelets in the {111} planes of the Al matrix, and in a 2198 Al-Li-Cu alloy during aging at 155 °C and 190 °C

<figure><img src="image_45.png" /><figcaption>**Fig. 43.** Comparison between SAXS experiments and precipitation modeling in two isothermal reversion experiments at 180 °C on Al-2 wt%Li-5 wt %Mg. The starting microstructures were created by isothermal treatments of 8 h at 150 °C (left) and 24 h at 120 °C (right). Reproduced with permission from [507].</figcaption></figure>
<figure><img src="image_46.png" /><figcaption>Fig. 44. T₁ precipitate morphology measurements during aging at 155 °C and 190 °C of a 2198 Al-Li-Cu alloy: (a) diameter; (b) thickness. The morphology can be used to better model the strengthening of non-spherical precipitates, as shown in (c). Reproduced with permission from [762].</figcaption></figure>

[762]. Precipitate length and thickness are measured simultaneously and can be used to model the strengthening mechanism, as shown by the evolution of yield strength plotted together with the strengthening model.

This is, however, not always practical, and is impossible in heterogeneously textured samples. There, it is necessary to make assumptions and approximations, which generate a quantification that is potentially less complete, but is still unique in the kinetics information it can give [605,764–766].

## 4.6. Machine-learning approaches for the development of sustainable aluminum alloys

### 4.6.1. Brief introduction to machine learning and the architecture of an artificial neural network

Machine learning refers to the ability of computer programs to learn something about the features and effects of an input system and its projection to an output or response. Computer programs in machine learning are usually staggered workflow arrays of mathematically rather plain algorithms that apply certain rules, often in the form of matrix-shaped filters, to individual nodes referred to as artificial neurons, which in turn are assembled in layers to mathematically mimic mutual connectivity.

The interesting feature of machine learning is that it learns about input-output relations without having explicitly been programmed with the underlying balance and conservation laws from physics and chemistry that would otherwise describe such systems in forward simulations (for instance in the form of differential equations, which can be solved with adequate initial values and boundary conditions). The training of machine-learning programs using reference data – i.e. (usually large) data sets that carry experience of the problem at hand, and specifically some output features that can be empirically related to the input data – generates improvements in predictive or recognition performance.

Machine learning is a sub-discipline of artificial intelligence, which describes any set of techniques that enable computers to imitate human cognitive behavior. When we refer to machine learning nowadays, by implication we mostly mean so-called deep learning
techniques. These involve artificial neural networks which are capable of extracting information (meaningful output/predictions) from complex, large and often high-dimensional input data sets containing apparent or hidden patterns.

Artificial neural networks are algorithms made up of at least two mathematical layers: an input and an output layer. Usually they have multiple other layers sandwiched between them, which are referred to as hidden layers. The more complex the problem to be solved with the help of the artificial neural network, the more the layers required. A large number of specialized artificial (mathematically mimicked) neurons lie on each layer of the network. Information processing in the neural network always follows the same sequence: information in the form of patterns or signals hits the neurons in the input layer, where it is processed and disseminated further to other neurons in the next layer. Usually, neurons are fed the output of multiple other neurons in the previous layer. Each neuron is assigned a weight so that neurons can have different levels of importance. The weight value of a neuron is calculated by adding up all the respective input or output values from all the neurons in the preceding layer that are connected to it. This additive value is than modified by a neuron transfer function. This function usually has a sigmoidal shape so that low input values remain below a certain threshold value. This means that the artificial neuron would not produce a notable output value for the next layer when it has not been sufficiently exited while moderate input values get amplified. This means that the additive effects of the input values, the neuron weights and the activation or transfer function, together with their respective excitation threshold values, calculate and weight the output value that is passed on to the neuron in the next step. Depending on the evaluation and weighting of the information, further neurons are linked and activated to a greater or lesser extent. This combination and weighting are used to create an algorithm (which is actually a layered set of fitting procedures) that generates a result for each input. Using penalty functions that quantify mismatch between known sets of inputs and outputs, used for training, the variables of the neurons such as the weights and threshold values are adjusted so that the network delivers ever more accurate and better results with more detailed training, provided that the training data are of sufficient quality and richness.

Besides the field of classical forward simulation (e.g. Calphad based thermodynamic predictions, phase-field simulations or crystal plasticity homogenization), machine learning has emerged as a data-driven scientific discovery method and useful statistical analysis tool in materials science that is capable of making predictions essentially on the basis of trained pattern recognition, i.e. without referring to established thermodynamic or mechanical structure–chemistry–property relations. This is particularly interesting in the highly complex field of metallurgy (many chemical elements, lattice defects, processing conditions, environmental conditions, etc.) which is often challenging to comprehend using simple laws.

A typical workflow in a machine learning application involves (i) collecting clean and reliable data with both input and output features that are detailed and rich enough to be a suitable training basis for a software code which usually contains no underlying physics model linking output to input; (ii) performing feature identification to extract relevant attributes that represent the data numerically; (iii) applying a set of staggered analyses and feature detection and prediction algorithms, usually formulated as artificial convolutional neural networks, that map the features with the property of interest; and (iv) fine-tuning the model parameters by adjusting the neuron weight factors through adequate penalty or loss functions to achieve better performance using previously unseen data.

### 4.6.2. Use of machine learning in aluminum scrap sorting

One of the biggest challenges in scrap sorting is not the availability of scrap, or collection logistics, but rather scrap quality, i.e. the degree of mutual alloy mixing. This applies particularly to the recycling of post-industrial and post-consumer scrap. Mixing renders this scrap less valuable and less useful for turning into alloy-specific high-value products, where compositional cleanliness is important.

Collection and recycling pathways for aluminum have existed for a long time. Scrap is substituted for primary raw material at levels of 30%–>90% in some aluminum products (see details in the preceding sections), but repeated recycling, and mixing with less pure scrap, can lead to the gradual accumulation or sudden poisoning of alloys by tramp elements such as Cu, Fe, Mg, Mn, Si, Zn und Cr and produce lower-quality scrap grades.

As described in section 3.1, separation technologies already exist that can, in principle, differentiate between different scrap types. One common method for identifying specific metals and even related alloys of the same material family is laser-induced plasma spectroscopy (LIBS) [59]. Here a high-intensity laser beam is focused on the surface of a small portion of the surface of the scrap alloy of interest. Through the energy input from the laser, part of the sample material is ablated and converted into a plasma. As the plasma cools, element-specific radiation is emitted which provides information on the composition of the sample. The radiation is fed into a spectrometer via an optical fiber and recorded as an atomic emission spectrum. Each peak in the spectrum can be assigned to an element based on its associated wavelength. Qualitative information on the analysed sample is obtained from the position of the peaks. Quantitative information can be determined from the peak height or peak area. To do this, a matrix-adapted calibration procedure must first be conducted using suitable reference materials. Using LIBS probing, the characteristic spectroscopic fingerprint of the scrap material, representative of its chemical composition, can in principle be determined.

By evaluating this spectrum it is possible to identify the exact alloy of a metal and thus not only differentiate between different metals such as aluminum, steel or copper, but between aluminum of the 5xxx alloy group and aluminum of the 6xxx alloy group. It is even possible to differentiate within alloy classes: the technique will discern between a 6010 aluminum alloy and a 6016 alloy. Improved results are obtained when the surfaces of the scrap chips are cleaned prior to actual LIBS probing, by removing surface impurities such as oil residues and oxides using a pre-ablation laser treatment [767].

The smaller the chemical differences among related alloys, the more challenging it is to differentiate them in terms of the underlying multi-peak spectra. These spectra have a certain scatter, as do the sometimes contaminated surfaces of the scrap pieces being probed. This is a particularly important challenge for the huge quantities of scrap from the >50 million vehicles that reach the ends of their lives every year. This mixed automotive scrap represents an essential market asset, but it also contains a number of aluminum
<figure><img src="image_47.png" /><figcaption>**Fig. 45.** Principal component analysis and classification of 35 Al-alloy specimens according to their nominal chemical compositions, obtained from the machine-learning-based analysis and classification of the LIBS spectra. The numbers #1–#4 refer to the four samples predicted by the artificial neural network. Figure used with permission from [769].</figcaption></figure>

alloys which are closely related chemically. If these alloys are not separated, they lose substantial value.

This is a task where machine learning has proven especially valuable [768]. For example, Campanella et al. [769] successfully developed a classification scheme for the identification of different types of wrought aluminum alloy scrap chips by training an artificial neural network, which also contained elements from classical fuzzy logic, with LIBS spectra taken from known, well-defined aluminum alloys. The approach enabled robust and reproducible findings, with an alloy-specific recognition rate above ff%: a good result considering the unavoidable signal variations stemming from the modest reproducibility of the LIBS signals. Interestingly, this study showed that although the principle component analysis (see Fig. 45) would suggest that separating 3xxx alloys containing Mn from 5xxx alloys containing Mg and Mg-Si blended 6xxx alloys should not be critical, the highest identification rate was obtained for 7xxx alloys, probably due to the multiple peaks produced from LIBS (see Fig. 46). Good results were also achieved in the classification of industrial and post-consumer aluminum scrap, when applied to well averaged and calibrated spectral data sets by combining classical partial least squares regression methods with artificial neural networks trained by multi-billion iterations [767].

### 4.6.3. Use of machine learning for the design of scrap-tolerant aluminum alloys

Another field relevant to the theme of this paper is the use of artificial neural networks for (scrap-tolerant) alloy design. Similar to scrap sorting, designing aluminum alloys with tramp element content inherited from secondary synthesis is a multi-parameter challenge that cannot be readily understood in simple chemistry-structure-property terms. However, the challenge might be addressable with machine learning methods. In every respect materials containing large amounts of scrap compete with (potentially cleaner) alloys made from primary synthesis. As primary production based on renewable energy sources gains momentum globally, even the “green branding” advantage associated with alloys made from scrap may in some cases not be a sufficiently strong argument for alloys which feature slightly worse performance. Hence, aluminum alloys with high tramp element content must meet a wide set of quality requirements if they are to improve their mechanical, electro-chemical, surface, optical, electrical, haptic, machining and manufacturing behavior.

Using conventional thermodynamics and kinetics simulations alone to develop crossover or less pure aluminum alloys under these constraints may be too slow and/or insufficient. This section therefore presents recent progress in the use of machine learning for alloy design that might become useful for sustainability and a circular metallurgical economy. Chaudry et al. [770] discussed various machine-learning methods and their respective suitability for the compositional design and age-hardening heat treatment of better-performing aluminum alloys based on the age-hardening concept. They targeted the Al-Cu-Mg alloy class, with variations in Zn and Zr content. The composition and heating variants were correlated with literature data on hardness for training. The result was that the so-called gradient-boosting decision tree algorithm was in particular capable of identifying unexplored alloys with increased hardness. Gradient-boosted decision trees use an amplification method, also referred to as boosting, to combine individual decision trees. The term boosting refers here to combining learning algorithms in the form of a series to achieve a strong learning method out of many sequentially connected weak learning methods. For the gradient-boosted decision tree method the weak learning methods are, for instance, classical Boolean decision trees.

Vahid et al. [771] applied a Bayesian optimization method directly to an important pending aluminum recycling problem, namely the development of 7xxx aluminum alloys using high-strength recycled aerospace-grade material. Their motivation was the expected availability of high-strength 7075 aluminum alloy scrap from airplanes reaching the ends of their lives. The authors re-melted the material and mixed it with different amounts of new primary aluminum. They successfully used Bayesian optimization as a method for most efficiently identifying suitable alloy compositions and processing workflows. Bayesian optimization methods are efficient when solving global optimization problems. They work by building a probabilistic model of the objective function, called a surrogate function, which is first searched efficiently with an acquisition function prior to subjecting candidate samples for evaluation by the real objective function. With their approach, the authors were able to identify new variants of the 7075 alloy with modified composition
<figure><img src="image_48.png" /><figcaption>**Fig. 46.** Machine-learning-based classification findings for mixed aluminum scrap chips, probed by considering the chips' LIBS spectra. Classification of these measurements into aluminum alloy groups proceeded by using a modified artificial neural network. Green: correct; red: incorrect. Figure used with permission from [769]. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

and heat treatment, yielding a material with maximum yield strength and ultimate tensile strength values of 729 MPa and 761 MPa, respectively.

In their machine-learning study of Al-Zn-Mg alloys, Cao et al. [772] included the search for both improved mechanical properties and enhanced corrosion resistance, with special attention to the role of heat treatment. They used a generalized regression neural network and a support vector machine to model both the mechanical and the corrosion properties, and the more conventional multiple linear regression method to predict mechanical properties. The support vector machine algorithm is an efficient multi-dimensional regression method that enables fast classification of high-dimensional data point arrays. The authors also evaluated the effect of the quality of the training data. One result of the study was that both, a single-stage aging heat treatment and retrogression re-aging can produce a material with higher strength than conventional double-stage aging, but at reduced corrosion resistance. The support vector machine method was also successfully used by Ampazis et al. [773] to predict the change in tensile strength brought about by surface corrosion of aerospace aluminum grade 2024. Training data were generated by tensile testing, targeted corrosion and imaging of the corroded surfaces, mapping several types of surface pitting features.

Several related studies have explored similar ways to use machine learning for the efficient prediction of (mainly) the mechanical, functional and corrosion properties of several types of aluminum alloy [774–778].

### 4.6.4. Further application areas of machine learning and artificial intelligence in the field of aluminum recycling

In the context of sustainable aluminum design, processing and manufacture, further areas where artificial intelligence and big-data-driven analysis methods may be of substantial future benefit are: tramp-element-sensitive through-process modeling; surface-quality control and improvement; corrosion analysis; and microstructural-damage phenomena [773,774,776–783].

The first area, through-process modeling based on analysing and using big data, is a general trend in metallurgical processing due to modern, fully automated and highly synchronised processing lines [285,784–786] which already feature large numbers of sensors. A very promising future approach would be to correlate all of the sensor data available with the corresponding variations in the chemical composition of the material that enters via scrap. From these correlations it should be possible to uncover trends in the impurity elements and properties of the material, particularly in the context of high-end sheet products.

Similar arguments apply to the second area, surface-quality control [787]. Customer requirements mean that most advanced aluminum products are very surface-sensitive. Examples here are outer automotive sheet materials, the alloys used for notebook computers and mobile phones, and sheets used for decorative purposes in architecture. In all of these cases the surface appearance of the material is of the highest priority. Scrap-related impurities and variations among batches can therefore be a substantial problem for manufacturers. For this reason it is a sensible option to take the sensor data from the corresponding processing lines (particularly from those processing steps that play an important role in final surface appearance, such as finish rolling, heat treatment, surface cleaning and etching) and correlate these data with fully automated surface-pattern recognition techniques that provide information about the state of the surface at the end of a manufacturing line. The connection of these two types of data through suitable machine learning techniques can help to identify impurity-sensitive correlations.

A very similar rationale applies to corrosion properties, which can react very sensitively to the chemical state of the material; and also to pattern-based precipitation-related damage analysis, including cracking during rolling and forming or the more microscopic cracking phenomena when (for instance) the final sheet material is bent [772,773,776,783,788]. In all of these cases a classical forward simulation technique is often a cumbersome or even hopeless undertaking, as the many influencing factors in real manufacturing lines are simply too influential for all to be properly considered on solid scientific grounds. Instead, the vast availability of sensor data should be exploited. Although as a rule these data may have little immediate relevance for classical structure-property relations, they can be suitable for classical convolutional neural network analysis: provided that sufficiently large data sets are
available, the associated techniques can identify trends without being fed with classical forward models.

## 5. Physical metallurgy of scrap-related tramp elements in aluminum alloys and effects on mechanical properties

Recycling aluminum alloys from end-of-life components often generates undesired composition features due to the accumulation of impurities beyond the tolerance limit and the mixing of different alloys [789,790]. In certain cases this harms product properties. To circumvent impurity-related issues it is usually helpful to reduce the impurity level in the alloys. However, it is challenging and/or energy-consuming to remove specific impurity elements (e.g. Fe or Si) during the remelting of aluminum scrap [43,791,792]. For this reason the conventional way to suppress impurity concentration in recycled aluminum is dilution by adding a large fraction of the associated alloys, and/or metals of high purity. This obviously undermines the added value of recycled aluminum alloys, and a more efficient strategy of using scrap is urgently needed.

To guide the design of recycled aluminum alloys and the optimum use of scrap, it is crucially important to fully understand the metallurgical effects of tramp elements. In this section we do not address the problems associated with primary casting and processing (please refer to [790,793,794] for these issues), but review studies of the effects of different impurities on microstructure evolution and key properties. We also summarise the consequences if the elemental concentration exceeds that of normal aluminum alloys.

The properties of aluminum alloys are determined by the matrix defect structure and a plethora of elemental segregation/pre-cipitation components of different sizes, e.g. intermetallic compounds, grain boundary precipitates (GBPs), stable precipitates, intermediate meta-stable precipitates and solute clusters (see Fig. 47). Impurities' effects on the recovery, recrystallization, grain growth and texture evolution during annealing can also be determined through precipitation processes. Therefore we present impurity-induced modification of the microstructure at different scales to build a full physical metallurgical picture of recycling aluminum alloy scrap. The fact that compositional deviation does not necessarily have a detrimental effect is highlighted.

The aim of this section is also to inspire the development of novel aluminum alloys and processing technologies that counteract the harmful effects of impurity elements in recycled aluminum alloys.

### 5.1. Influence of scrap-related tramp elements on precipitates and forming properties

As shown in Fig. 48, the compositional deviation in recycled age-hardenable aluminum alloys compared to standard aluminum alloys has two main causes: (i) mixing of two or more types of alloy series; and (ii) impurities from scrap-related tramp elements. In this section we first summarize the common precipitates in conventional aluminum alloys, and then discuss the effects of the above factors on the precipitates formed in recycled aluminum alloys. (For some of the basics and main microstructure variables which affect aluminum sheet forming in conjunction with scrap-related impurities, see section 3.7.).

#### 5.1.1. Possible precipitate types in age-hardenable aluminum alloys

The age-hardenable wrought alloy systems are based on the Al-Mg-Si-Cu 6xxx, Al-Cu-Mg 2xxx and Al-Zn-Mg-Cu 7xxx series. Minor alloying elements, e.g. Sc, Zr, Cr, Ag, are intentionally added to modify the associated microstructures [794–797], furthering the development of commercial aluminum alloys. High-performance aluminum alloy products can then be manufactured through well-controlled thermomechanical processing and heat treatment. Here precipitates play a key role in strength due to a precipitation-hardening effect. The solutes dissolved in the matrix undergo a complex precipitation process involving transformation from metastable phases to thermodynamically stable variants. To acquire the desired precipitate size and morphology and thus the maximum hardening effect, alloys' composition and thermal-mechanical history need to be strictly controlled. The rational design of alloy compositions requires an understanding of how alloying elements aggregate and grow into strengthening phases. However, it is usually very difficult to accurately determine the structure and composition of reinforcing particles because they are very small and are embedded in the aluminum matrix [795,798,799]. Over the past decade, advanced techniques such as aberration-corrected transmission electron microscopy (TEM) and three-directional atom probe tomography (APT) have made it possible to identify the structural and compositional determinations of numerous precipitates responsible for the critical performance of precipitation-hardened aluminum alloys at different stages in the processing chain [542,795,798–801]. In the following sections we summarise the current understanding of precipitation sequences in current alloy systems, which can help us to understand the effects of impurity elements on precipitates in recycled aluminum alloys. While various aluminum alloy series can be used to manufacture cast products, the common cast alloys are based on Al-Si-(Cu) and can be sorted relatively easily at the recycling stage. The non-age-hardenable wrought Al-alloys are 1xxx (Al-Fe-Si), 3xxx (Al-Mn) and 5xxx (Al-Mg). Table 14 summarizes the primary strengthening precipitates of main age-hardenable Al-alloys identified in the literature.

The precipitation sequence in ternary Al-Mg-Si alloys can be described as follows [795,798,802]: SSSS (super-saturated solid solution) → atomic clusters/GP zones → β" → β' → β, Si, while U1, U2 and B' also occur, accompanying β' in alloys when the Mg/Si ratio is low. The atomic clusters are also known as Mg-Si-vacancy complexes [824]; their configuration and ordering are still poorly understood. GP zones generally form at the initial aging stage and are fully coherent with the α-Al matrix. The needle-like β" phase usually
appears in the peak-aged state and is regarded as the primary strengthening phase in the Al-Mg-Si alloy system [798]. When a small amount of Cu (0.1–1 wt%) is added to form the quaternary alloy, the precipitation sequence is modified as follows [808,809,811]: SSSS → Clusters/GP zones → β", L, C → β', Q' → Q, where L and C are precursors to Q'. Alloys with higher Cu content usually have larger hardness, with the main strengthening phases L and C [808,809]. The C phase possesses a rectangular cross-section with a broad coherent interface along the (001) α plane. The Q' and C phases share the same triangle sub-unit [811], implying their inherent structural similarity. In general, the precipitation process in the Al-Mg-Si-(Cu) alloy is strongly dependent on the Mg/Si ratio and the Cu content [795,808,811].

The precipitation sequence in Al-Cu-(Mg) alloys takes the following two routes [542,800,812,814,816,818,825,826]: SSSS → GP I → GP II → θ'' → θ' (high Cu/Mg ratio) and SSSS → GP → S (gradual thickening by an even number of Cu-Mg layers) (low Cu/Mg ratio). GP I/II are planar structures of Cu layer lying on the {002} Al plane [812]. The tetragonal θ' phase (Al₂Cu) possesses a plate-like morphology [816] and is considered to be the most effective strengthening precipitate in the binary alloy system (e.g. the 2219 alloy). To improve the age-hardening potential in Al-alloys, a certain amount of pre-strain can be applied prior to artificial aging [812,814]. This temper is also designated as T8. The introduced dislocations have a significant influence on the phase selection and precipitation pathways in important commercial alloys. A recent study reports that the precipitation at dislocations in Al-Cu alloys evolves according to at least three paths [812,814]: (i) the direct precipitation of thick θ'; (ii) the pre-θ'-1 first forms and then transforms to θ' with a thickness of 2 c₀ (please see the c parameter of the θ' phase in Table 14); and (iii) the pre-θ'-2 first forms and then transforms to θ' with a thickness of 1.5 c₀. Note that the pre-θ' and θ' phases have the same interfacial structure, which is a coherent and continuous Cu layer. In the Al-Cu-Mg alloy with a low Cu/Mg ratio (e.g. the 2024 alloy), the major strengthening S phase (Al₂CuMg) is lath-shaped and grows along (001) Al with a habit plane of {102}Al [818]. The Guinier-Preston-Bagaryatsky (GPB) zones composed of 1D rod-like sub-nanometre units (Mg₄Cu₄Al) only occur upon aging at elevated temperatures [821]. The orthorhombic Ω-Al₂Cu phase prefers to form on {111}Al planes in the Al-Cu-Mg alloys with additions of silver [693,795]. This phase generates excellent thermal stability in the Al-alloys. When Li is further added in the multi-component Al-Cu-Li-Mg-Ag alloys (e.g. 2195 and 2050), the major hardening precipitate changes into thin plate-like T1 phases on the {111} planes of the matrix [796,820].

The generic precipitation sequence of an Al-Zn-Mg-(Cu) alloy under artificial aging is well-known as [822,823,827]: SSSS → GP zone → η' → η (MgZn₂). The precise transition mechanisms involved in this process have been elucidated by advanced TEM and density-functional theory (DFT) calculations. Atomic-resolution TEM observations reveal that η₂ (one common variant of the η phase) initially consists of rhombohedral (R) units and orthorhombic (O) units [799]. The η₂ phase grows by the stacking of R units and R⁻¹ (rotated R) units, with the artificial aging progressing toward a peak-aged state [799,828]. The experimentally observed η' → ηₚ → η sequence is also rationalized by the solute substitution process calculated by DFT [823]. Although the theoretical study supports the premise that Cu/Mg and Zn respectively prefer to dissolve into the bulk interior of η and segregate to the broad interface, the elemental segregation of Cu at the Al matrix proximate to η has been observed in a 7050 alloy [829].

<figure><img src="image_49.png" /><figcaption>**Fig. 47.** Schematic of the multiscale elemental segregation in aluminum alloys.</figcaption></figure>
<figure><img src="image_50.png" /><figcaption>**Fig. 48.** Element radar chart for age-hardenable recycled wrought aluminum alloys, where deviation from the standard compositions of the current aluminum alloy occurs due to the mixing of different grades of aluminum alloy and the impurities introduced from the contaminated scrap [43,790,791].</figcaption></figure>

### 5.1.2. Impurity effects on precipitation reactions

The major impurity elements vary according to scrap source [789–791]. The most common are Fe, Si, Cu, Zn, Sn and Mg. Most of these can cause detrimental effects in alloys and – except for Mg and Zn – are often difficult to remove [43]. Therefore, one of the core issues in the physical metallurgy of recycling aluminum alloys is to understand and control the metallurgical effect of impurities on processing and properties. In this section we briefly review the current understanding of the role of impurity effects on precipitation during final aging. Several papers on this subject have been published. They address both the effects of impurities on IMCs [789,794,830] and methods for removing such tramp elements [43,791,792].

Several minor impurity elements have subtle effects on nano-sized particles, particularly in Al-Mg-Si-(Cu) alloys [379,831]. Cu and Zn, which are often present in mixed aluminum alloys, also appear as impurities in cables and engine block parts containing Al scrap [791,794]. They normally improve the precipitation-hardening response, albeit alongside a loss in corrosion resistance due to grain boundary precipitation [379,809]. A small amount of Sn (0.02–0.08 wt%) could suppress natural clustering [555], which is responsible for the lessened press forming ability of automobile body panels made from 6xxx alloys, without reducing hardening from artificial aging. The microalloying of Sn also considerably enhances precipitation hardening at elevated artificial aging temperatures by inducing the formation of composite β" precipitates on modified β' (Mg₉Si₅-xSnx) [832]. Sn has a strong binding tendency with vacancies and thus fundamentally affects initial precipitation [555]. However, it also acts as a surface-active element and can promote corrosion.

Ag has an effect similar to that of Cu in terms of both age-hardening behavior and precipitation process [833]. Solute Ag can influence the whole precipitation stage and profoundly alter the phase transformation. Cd is considered a biologically harmful tramp element, but as an impurity in aluminum alloys (~0.25 wt%) it can appreciably improve the age-hardening response of Al-Mg-Si-(Cu) alloys [834]. Cd-rich particles form in such materials and facilitate the formation of needle-like Mg-Si-Cu precipitates containing Q'/C-like sub-units [834]. Transition metals (TM) such as Cr and Fe often reduce the precipitation-hardening response in 6xxx alloys [835]. The precipitate types remain almost unchanged, but are coarser and unevenly distributed at the dispersoids/Al interfaces. AlSi(Fe, Cr) and the different types of AlSiFe dispersoids and IMCs also consume the main alloying element Si [835]. Al-Mg-Si alloys have an extremely low tolerance limit for Ca, because 60 ppm Ca can cause measurable loss in age-hardening response [836]. Ca absorbs Si to form micro-meter-sized CaAl₂Si₂ particles, leading to a deficiency in the Si content, which is critical for forming Mg-Si coherent precipitates. Solute Ca does not accumulate in the Mg-Si precipitates [836], possibly due to its repulsion of solutes Mg and Si.

A variety of micro-alloying effects on precipitation in Al-Cu-(Mg) alloys have been investigated in the literature [383,837-842]. Many micro-alloying elements tend to accumulate in recycled Al-Cu-(Mg) alloys as impurities. The 0.3 wt% Si added in the binary Al-Cu alloy generally has a positive effect on the age-hardening response [837]; this is ascribed to the greater number density of the θ"/θ' phases. Si is believed to promote the heterogeneous nucleation of precipitates based on the observation of Si partitioning and
<caption>**Table 14**</caption>

Common precipitate types in current age-hardenable aluminium alloy series: 6xxx\Al-Mg (0.5–1 wt%)-Si(0.5–1 wt%) -Cu(0–1 wt%), 2xxx\Al-Cu (4–6 wt%)-Mg (0.0–2 wt%), 7xxx\Al-Zn(4–8 wt%)-Mg (1–3 wt%).

<table><thead><tr><th>Alloy</th><th>Phase</th><th>Shape</th><th>Space group</th><th>Lattice parameters (nm)</th><th>Composition</th><th>Ref.</th></tr></thead><tbody><tr><td rowspan="10">6xxx</td><td>Pre-β<sup>+</sup></td><td>Needle</td><td>Monoclinic C2/m</td><td>a = 1.478, b = 0.405, c = 0.674, β = 105.8°</td><td>(Al + Mg)<sub>5</sub>Si<sub>6</sub></td><td>[802]</td></tr><tr><td>β<sup>+</sup></td><td>Needle</td><td>Monoclinic C2/m</td><td>a = 1.516, b = 0.405, c = 0.674, β = 105.3°</td><td>Mg<sub>5</sub>Si<sub>6</sub></td><td>[798]</td></tr><tr><td>β'</td><td>Rod</td><td>Hexagonal P6<sub>3</sub>/m</td><td>a = b = 0.715, c = 1.215, γ = 120°</td><td>Mg<sub>9</sub>Si<sub>5</sub></td><td>[803]</td></tr><tr><td>U1 (typeA)</td><td>Lath</td><td>Trigonal P 3 m1</td><td>a = b = 0.405, c = 0.674, γ = 120°</td><td>MgAl<sub>2</sub>Si<sub>2</sub></td><td>[804]</td></tr><tr><td>U2(typeB)</td><td>Lath</td><td>Orthorhombic Pnma</td><td>a = 0.675, b = 0.405, c = 0.794</td><td>Mg<sub>2</sub>Al<sub>2</sub>Si<sub>2</sub></td><td>[805]</td></tr><tr><td>B' (type C)</td><td>Lath</td><td>Hexagonal P 6</td><td>a = b = 1.04, c = 0.405, γ = 120°</td><td>Mg<sub>9</sub>Al<sub>3</sub>Si<sub>7</sub></td><td>[802,806]</td></tr><tr><td>β</td><td>Plate</td><td>Cubic Fm 3 m</td><td>a = 0.635</td><td>Mg<sub>2</sub>Si</td><td>[802,806,807]</td></tr><tr><td>C</td><td>Lath</td><td>Monoclinic P21/m</td><td>a = 1.032, b = 0.81, c = 0.405, γ = 101°</td><td>Mg<sub>8</sub>Si<sub>6</sub>Cu<sub>2</sub>Al<sub>2</sub></td><td>[808,809]</td></tr><tr><td>Q'</td><td>Lath</td><td>Hexagonal P 6</td><td>a = b = 1.032, c = 0.405, γ = 120°</td><td>Al<sub>3</sub>Cu<sub>2</sub>Mg<sub>9</sub>Si<sub>7</sub></td><td>[810,811]</td></tr><tr><td rowspan="7">2xxx</td><td>GP I</td><td>plate</td><td>—</td><td>Single Cu layer</td><td>Cu layer</td><td>[797,812]</td></tr><tr><td>θ<sup>+</sup> (GP II)</td><td>plate</td><td>—</td><td>Cu layer separated by 0.81</td><td>Al<sub>3</sub>Cu</td><td>[813]</td></tr><tr><td>pre-θ'-1</td><td>plate</td><td>Tetragonal</td><td>a = b = 0.405, c = 0.608</td><td>Al<sub>2</sub>Cu</td><td>[800,812]</td></tr><tr><td>pre-θ'-2(η<sup>+</sup>)</td><td>plate</td><td>Orthorhombic Cmmm</td><td>a = 0.412, b = 1.216, c = 0.880</td><td>AlCu</td><td>[542,814]</td></tr><tr><td>θ'</td><td>plate</td><td>Tetragonal I 4 m2</td><td>a = b = 0.58, c = 0.405</td><td>Al<sub>2</sub>Cu</td><td>[815,816]</td></tr><tr><td>θ</td><td>plate</td><td>Tetragonal I4/mcm</td><td>a = b = 0.599, c = 0.481</td><td>Al<sub>2</sub>Cu</td><td>[795,817]</td></tr><tr><td>S</td><td>plate</td><td>Orthorhombic Cmcm</td><td>a = 0.401, b = 0.926, c = 0.712</td><td>Al<sub>2</sub>CuMg</td><td>[818]</td></tr><tr><td rowspan="5">7xxx</td><td>Ω</td><td>plate</td><td>Orthorhombic</td><td>a = 0.496, b = 0.856, c = 0.848</td><td>Al<sub>2</sub>Cu</td><td>[693,819]</td></tr><tr><td>T1</td><td>plate</td><td>Hexagonal D6h</td><td>a = 0.496, c = 1.390.</td><td>Al<sub>2</sub>LiCu</td><td>[801,820]</td></tr><tr><td>GPB</td><td>needle</td><td>—</td><td>1D periodic (c = 0.405)</td><td>AlMg<sub>4</sub>Cu<sub>4</sub></td><td>[821]</td></tr><tr><td>η<sup>+</sup></td><td>plate</td><td>Hexagonal P6<sub>3</sub>/mmc</td><td>a = 0.504, c = 1.303</td><td>Mg<sub>2</sub>Zn<sub>5-x</sub>Al<sub>2+x</sub> (x = 2–4)</td><td>[822,823]</td></tr><tr><td>η<sub>2</sub>(η<sub>p</sub>)</td><td>plate</td><td>Hexagonal</td><td>a = 0.504, c = 0.828</td><td>MgZn<sub>2</sub>Al</td><td>[799,823]</td></tr><tr><td></td><td>η</td><td>plate</td><td>Hexagonal P6<sub>3</sub>/mmc</td><td>a = 0.521, c = 0.849</td><td>MgZn<sub>2</sub></td><td>[799]</td></tr></tbody></table>

interfacial segregation in these phases [837]. Si still plays an advantageous role when it is co-doped with a small amount of Mg [842]. However, the mechanism changes: a C phase containing Si, Mg and Cu forms first, and then catalyses the heterogeneous growth of θ''/θ' [842]. When a small amount of Fe is added together with Si, Si with a content of below 1.0 wt% slightly reduces the mechanical properties, while higher Si content can significantly decrease them due to the occurrence of excessive Si particles [840]. Sn is another possible impurity, and was found to considerably promote and refine the θ' precipitates [383,838], leading to enhanced precipitation hardening in Al-Cu alloys. Sn solutes present at the rim interface may reduce the misfit strain and promote the nucleation of the θ' phase [383]. Conversely, Sn has an adverse influence on the precipitation-hardening effect when introduced with Mg (0.35 wt%) [838]. The formation of stable Mg<sub>2</sub>Sn phases consumes solute Mg and reduces its concentration in the alloy. Au has an effect similar to that of Sn on the precipitate morphology and hardening response [841]. Nevertheless, Au refines the precipitates by replacing Cu in the middle of θ' and modifying its growth mechanism [841]. These studies demonstrate that impurity effects on precipitation hardening vary with content. Na is an undesirable impurity and has a negative effect on the performance of Al-Cu alloys [839]. It dramatically impairs the hardening potential in artificially aged Al-Cu alloys, as it can segregate at and weaken the interface between the Al<sub>2</sub>Cu phase and the matrix. Na also tends to segregate to grain boundaries and embrittle Al-Cu alloys [839].

High-strength Al-Zn-Mg-(Cu) alloys are mainly used to produce high-performance, load-bearing components, such as those in airplanes. They normally have a very low tolerance limit for impurities, such as Fe, Si and some transition metals that form notoriously insoluble intermetallics [789,794,843]. Therefore it is very challenging to recycle 7xxx alloys from scrap [844]. It was found that rare earth metal elements (REM) do not influence the morphology of the η-type hardening precipitates [843]. Adding minor elements such as Mn can refine grains and thus reduce hot tearing during the casting of 7xxx alloys with high solute content [845]. However, the age-hardening response can then become significantly suppressed and larger precipitation-free zones (PFZs) can be formed around the grain boundaries [845]. Small amounts of Sn and Ag may markedly reduce the PFZ and thus cause the precipitation-hardening response in fine-grained 7xxx alloys to recover. This has been attributed to the strong interaction of Sn/Ag with the main solutes [845,846], but it may also result from the interaction of these elements with vacancies. In an Al-Zn-Mg-Cu alloy with low Zn and Mg content (with low strength but excellent formability), Ag induces pronounced enhancement of the age-hardening response [847]. Ag aggregates with other alloying elements at the very early aging stage and increases the number density of fine clusters.

When producing cast Al-Si-Mg-Cu alloys from scrap, the situation is very similar to that of 6xxx alloys and special attention must be paid to the consumption of the main alloying elements by unavoidable formation of IMCs [848]. For example, the amount of Ni is controlled to be below 0.16 wt%; otherwise the decrease in mechanical properties would be obvious [849]. However, it is known to be a beneficial alloying element in heat-resistant Al-Si casting alloys [850]. Current studies on the effects of impurities in commercial Al-Cu-Li-Mg-Ag alloys are very limited. It would be very demanding to produce this kind of alloy, considering the strict control of alloying elements and the complex interaction of Li with other phases [851].

Based on above analysis, multiple interactions between impurities and main alloying elements determine the variety of impurities' effects on precipitation hardening in Al-alloys. Several representative interactions are illustrated in Fig. 49. Beneficial impurities can usually aggregate with or replace the alloying elements in precipitation phases. They promote heterogeneous nucleation and growth of
fine hardening precipitates. DFT and Monte Carlo simulations reveal that beneficial impurities generally prefer to bind with vacancies and main alloying elements [846,852]. In contrast, the most harmful impurities are those that tend to form (coarse) intermetallics with the alloying elements during casting.

### 5.1.3. Multiple precipitation reactions in mixed aluminum alloys made from scrap

When different types of aluminum alloy are mixed together in the same melt, the recycled aluminum alloys obtained contain multiple components which can affect precipitation reactions in multiple ways [853–856]. When crossing established composition bounds, new phases can form. Knowledge about the interactions among them is of great significance, particularly for mechanical and corrosion properties. From a structural and compositional perspective, the precipitation interaction between different phases in the mixed alloy can be categorized into three types: (i) replacement of alloying elements which usually occupy atomic sites with impurity elements; (ii) interfacial segregation of impurity elements; and (iii) composite and “backpack” precipitates composed of different phases.

Various alloys of the same alloy system, targeted for different applications, have been developed by altering the ratio and total content of the differing alloying elements. The Mg/Si ratio changes the Mg:Si ratio in the pre-β" precipitates [855] and thus the peak hardness in the Al-Mg-Si alloy (e.g. 6061 and 6016). The Cu level has a substantial effect on the precipitation of Cu-containing phases in Al-Mg-Si-Cu alloys [379,809]. HAADF-STEM observations [379,809] reveal Cu substitution of Mg1 and/or Si3/Al sites in the β" phase (Fig. 50a-b) in the Cu-augmented Al-Mg-Si alloy (e.g. 6013 and 6056). Periodic Cu segregation at the habit plane of the lath-like β' precipitates has also been observed in the over-aged stage [857], indicating a strong interaction between the Cu atoms and the precipitates. When the Cu content is higher (still usually remaining below 1.0 wt% in the 6xxx alloy specification), C and Q phase [858] and their precursors are formed [809,811]. Another prominent feature of the interaction between precipitates is the formation of composite (or hybrid) precipitates containing different precipitate types [379,809]. These composite or backpack precipitates usually appear disordered and incorporate everal sub-structures of different well-defined phases in Al-Mg-Si-Cu alloys. The precipitates characterized by a disordered structure with β" substructure (low density cylinder, LDC) and Q' substructure (Cu triangle sub-structural unit) are frequently observed in under-aged Al-Mg-Si-Cu alloys [809]. The composite precipitates including β" and precursors of Q'/C phases form in the peak-aged state [809,855].

These precipitation scenarios in the Al-Mg-Si-Cu alloy reflect the precipitation reactions that may occur in the 6xxx alloy when mixed with a small portion of 2xxx alloy. The influence of Zn on the precipitates and age hardening in the Al-Mg-Si-(Cu) alloys have also been reported recently [831,859,860], and is helpful in understanding the possible precipitation reactions in mixed 6xxx and 7xxx alloys. Saito et. al. [831] found that adding Zn (up to 1 wt%) slightly improved the age-hardening response due to the increased number densities of needle-like precipitates in Al-Mg-Si alloy. Zn atoms tend to occupy different sites in the atomic columns of the precipitates rather than form the precipitates of the Al-Zn-Mg alloy system. Zn atoms also prefer to segregate and form a continuous Zn film along grain boundaries, leading to the high intergranular corrosion (IGC) susceptibility in peak-aged Zn-augmented alloys (~1 wt %). HAADF-STEM imaging and DFT calculations suggest the favorability of incorporation of Zn at Si3/Al sites into the β" phase (Fig. 50c) [860], similar to what was observed for Cu. However, the Zn-Zn interaction is relatively weak, explaining the random distribution of Zn along the growth direction of the particles. Clarifying the amount of foreign solute atoms that a precipitate can accommodate is important if we are to understand these kinds of precipitation interaction in impurity-contaminated alloys. When the added Zn content reaches up to 3.0 wt% in an Al-Mg-Si-Cu alloy with a high Mg/Si ratio, the yield strength increases by 50 MPa, while the elongation remains almost unchanged [859]. This is due to the refined size of needle-like precipitates and the increased number density of solute-rich particles. The tendency of Zn to partition in these particles occurs because of the strong interaction of Zn with other solutes, like Mg, Si and Cu.

In an Al-Cu-Mg-Si alloy with a Cu content of ~ 5 wt% (similar to the 2xxx alloy mixed with a small portion of a 6xxx alloy), the hybrid precipitates include fragments of the C phase, the Q' phase, GPB and the θ' phase [856]. This observation suggests that the precipitates formed in such mixed alloys, stemming from different scrap sorts, do not just contain simple mixtures of phases from the Al-Mg-Si-(Cu) and Al-Cu-(Mg) alloy systems. Different configurations of various phases and their sub-structural units, e.g. the LDC of β" surrounded by GPB zone and monoatomic Cu layer (GP I), have been identified (Fig. 50d), and the stabilities of these structures have been rationalized by DFT calculations [856]. Such types of interaction between various phases may markedly affect the morphology of the main strengthening phase, e.g. the θ" phase formed on the broad interface of C-type precipitates (Fig. 50e) [842].

A systematic investigation of the effect of 1–4 wt% Zn on the peak-aged precipitation in a mixed Al-Cu-Mg (2xxx) and Al-Zn-Mg (7xxx) model alloy, which mimics the use of scrap from different alloys groups, was performed by Wenner et. al. [854]. Alloys with 1% and 4% Zn contain mainly S (2xxx system) and η-type (7xxx system) precipitates, respectively. The alloy with a medium Zn content (2.5 wt%) is hardened by both S and η-type phases. Although these phases maintain their original native structures, compositional modification was identified by high resolution elemental mapping. DFT calculations show that replacement of Zn by Cu in the η-type phase is preferred if the substituted Zn is below 25%, while the substitution of Al by Zn in the S phase was observed to be the most favorable configuration [854]. The interaction of the precipitation is manifested in the non-stoichiometric compositions of the hardening phases.

An attempt to study the precipitation system in an Al-Zn-Cu-Mg-Si alloy (which mimics a blend of 2xxx, 6xxx and 7xxx alloys) has also been reported [853]. The precipitates encompass phases from three main classes of age-hardening alloy [48,55,494,861,862]: Al-Cu–(Mg), Al–Mg–Si–Cu, and Al–Zn–Mg. Si-containing disordered precipitates composed of substructures of the C phase and GPB form first and grow along the 〈001〉 Al lattice direction. These needle-like precipitates are dominant until peak-aging. During over-aging, a variety of phases including S, θ', η and Ω occur concomitantly. The combined C/θ' phase was revealed (see Fig. 50f), and the segregation of Zn at the broad interface of Ω (Fig. 50g), demonstrating the interaction between various phases. η-type precipitates were
<figure><img src="image_51.png" /><figcaption>**Fig. 49.** Characteristic interactions between precipitation phases and minor impurities in recycled Al-alloys. (a) High-resolution HAADF-STEM images displaying the composite β'/β" phase containing Sn in the Sn-added Al-Mg-Si alloy, as confirmed by elemental mapping [832]. The right-hand panel shows the formation energy calculation result revealing the favorable substitution of Si in β' by Sn. (b) Atom probe tomography (APT) elemental concentration iso-surface results showing the distribution of precipitates in the 6082 alloy with added Cd [834]. (c) High-resolution HAADF-STEM images and elemental maps showing the Au segregation in the hardening θ' phase [841]. The preferential anchoring positions of Au are confirmed by first-principles calculations. TEM images in (a, c) were taken along (001) Al. The images are reproduced with permission.</figcaption></figure>

only rarely observed despite the high Zn content (4 wt%). This may be explained by the fact that Zn prefers to locate in the Cu-containing phases and that there is a high solubility of Zn in the Al matrix. Previous studies have demonstrated that small changes in alloy composition can alter precipitation significantly [863]. Producing Al-alloys from mixed scrap / composition origin can lead to backpack-like phase agglomerates and phases different from those observed in the established classical alloy systems [55]. A multitude of intergrown precipitate phases already reflect the complex precipitation reaction in the mixed alloys, not to mention those in the Li-containing mixed Al-alloy (Al-Cu-Li alloy and other alloy series). In addition, their growth and strengthening mechanisms remain to be explored.

## 5.2. Mitigation of scrap related issues by alloying

While some impurity additions can have beneficial effects on properties such as strength, one of the biggest challenges in recycling aluminum alloys is suppressing the harmful effects of impurity elements (e.g. Fe and Si). It is well recognized that the high Fe content in aluminum alloys can cause a significant decrease in tensile properties, especially in ductility [864]. Fe has a low solubility in aluminum
<figure><img src="image_52.png" /><figcaption>**Fig. 50.** Characteristic interactions between a precipitation phase and another phase or foreign solutes in mixed aluminum alloys: (a) native atomic structure of the β'' phase projected along the b-axis [860]; HAADF-STEM images of the cross-section of the β'' phase with the Mg1 and/or Si3/Al sites replaced by Cu (b) [809] and Zn (c) [860]; (d) substructures of the C phase decorated by units of GPB [856]; (e) the inter-connected θ'' and C phases [842]; (f) the combined θ''-C plate [853]; (g) elemental maps and high-resolution STEM image showing the Zn enrichment at the interfacial layer between the Ω phase and the Al matrix [853]. The HAADF-STEM images in (b-f) are taken along (001) Al while that in (g) is along (112) Al. The images are reproduced with permission.</figcaption></figure>

alloys and often precipitates in the form of Fe-rich intermetallic compounds during solidification [249]. These intermetallic compounds usually have a plate-like or needle-like morphology, which are harmful to castings due to stress concentration. In addition, they are unstable and prone to breakage, which can damage the integrity of the castings and increase the processing cost. In order to produce high-quality recycling aluminum alloys, measures must be taken to eliminate or reduce the negative effects of Fe. In this regard, the most common approach is to alloy with neutralizing elements as shown in Fig. 51 [865–869]. Previous studies have demonstrated that adding trace elements, such as Mn, Ce, Y, can change the nucleation and growth kinetics of Fe-rich intermetallic compounds, e.g. facilitate their heterogeneous nucleation [865,866]. The trace elements may segregate in the alloy prior to nucleation and growth, thus affecting the formation kinetics of Fe-rich intermetallic compounds. Adding appropriate trace elements (e.g. Mn and Y) in that context not only helps to promote the refinement of the otherwise coarser needle-like β-Al₅FeSi phase in the recycled alloy,
but also helps to reduce the volume fraction of that phase significantly. Simultaneously, it can also transform the α-Al~8~Fe~2~Si phase into an α-Al~8~(Fe~2~Mn)Si phase variant with a fine, rounded shape. The occurrence of α-Al~12~(Fe,Mn)~3~Si has also been reported.

Direct chill casting simulations have been used to study the effect of different amounts of Ni and V on the intermetallic second phases in aluminum alloys containing Fe [249,731,867]. Adding Ni promotes the formation of Al~3~Fe and α-Al~8~Fe~2~Si and inhibits the nucleation and growth of metastable Al~m~Fe [867], while a new Ni-rich Al~9~FeNi phase is observed at high Ni content. These alloying effects have been observed in different Al-alloy systems containing large amounts of Fe, including Al-Cu [248,249], Al-Mg [867] and Al-Si [731]. In contrast, adding V strongly promotes the precipitation of Al~m~Fe and reduces the critical cooling rate for its formation [867]. Adding Sc and Cu is also effective in improving the properties of the alloys [870,871]. Sc can transform the needle-like β-Al~5~FeSi phase into a relatively harmless spherical Sc-Fe phase. Because the hardness and elastic modulus of the Sc-Fe phase are lower than those of the β-Al~5~FeSi phase, the cracking propensity in the Al matrix is evidently lowered. In addition, the spherical Sc-Fe phase can improve the fluidity of the Al-alloy during solidification, thereby reducing the dendritic shrinkage [870,871].

Si is another harmful impurity in many recycled aluminum alloys, but contradictory results exist regarding the influence of Si on the recycled alloys [840,872]. Questions surround (i) whether Si is an impurity or a beneficial alloying element; and (ii) the range in which Si can improve the mechanical properties of Al-alloys. On one hand, Si can transform the morphology of Fe-rich intermetallic compounds from a harmful lath-like to a less harmful Chinese script shape. On the other, the Si may reduce ductility. In order to eliminate the negative effect of Si in recycled Al-alloys, small amounts of chemical modifiers, e.g. Na, Sr, Ca, Ba and Eu, are usually added to melt the recycled Al-Si based alloy [866,873]. Adding chemical modifiers can change the morphology of the eutectic Si phase and affect the nucleation of the eutectic grains. It also promotes the morphological transformation of the eutectic Si phase from coarse plate to fine fiber.

Al-Cu based alloys have relatively good fatigue properties, high specific strength and good heat resistance. However, their casting properties, such as hot crack sensitivity and fluidity in cast Al-Cu alloys, are relatively poor [874]. Alloying Si in Al-Cu alloys can reduce thermal cracking sensitivity [840]. However, the Si content needs to be strictly controlled to obtain optimized properties. The mechanical properties of the Al-Cu-Mn-Fe alloy decreases slightly for an Si content below 1.0 wt% [840]. This has been attributed to the coupling effects between the increase in nano-scale α-Al~8~Fe~2~Si particles, the grain size coarsening and the formation of a grain boundary Al~2~Cu phase. When the Si content is 1.5%, the mechanical properties of the Al-Cu alloy are significantly reduced, mainly due to the formation of excessive Si particles and the aggregation of the second phases. As the most widely used cast aluminum alloy, the Al-Si alloy has been widely used in automotive components. Here, in order to improve the strength of Al-Si castings, a small amount of Mg is added to improve age hardening. Adding Mg can also effectively modify some of the Fe-rich phases and improve the alloy's performance [864]. When a small amount of Mg is added, a fraction of the α-Al~8~Fe~2~Si particles in the alloy will be transformed into the flake-like Al~13~Fe~4~ phase. With a further increase in Mg, the morphology of the Al~13~Fe~4~ phase evolves from flake-like to petal-like. This evolution mechanism is possibly related to the Mg/Si combination and the increase in undercooling [864].

In order to design new alloys without impairing the properties of recycling alloys, aluminum scrap needs to be remelted and alloyed during secondary production. However, various alloying elements and many processing steps make it difficult to design the alloy on demand. Bayesian optimization has recently been used as the basis for adaptive experimental optimization to explore and optimize multivariate problems [771]. The optimization of composition to maximize alloy strength and reduce the overall heat treatment time to maintain cost-effectiveness has been achieved. The experimentally designed 7xxx alloy features maximum yield and ultimate tensile strengths of 729 MPa and 761 MPa, respectively. Its mechanical properties are equivalent to or exceed those of current high-strength 7xxx series Al-alloys. Exploiting advances in the understanding of thermodynamics and diffusion kinetics, Kampmann-Wagner numerical simulations of precipitation (see also section 4.5) have been used to study the effect of adding alloying elements (e.g. Cr and Zn) [876]. Such simulations can help to reveal the behavior of each element during nucleation, growth and coarsening. With the further improvement of the nucleation model, the proposed model has the potential to guide the design or optimization of chemical composition and heat treatment processes for recycling Al-alloys.

### 5.3. Damage initiation through precipitates related to tramp elements

Deterioration of the toughness and ductility in alloys is attributed to the nucleation and propagation of damage. To study damage initiation, one needs to address damage-susceptible regions in alloys' microstructure. Coarse particles are important in this regard, because void/crack nucleation is more likely to start at the largest inclusions due to the localized stress concentration. The largest constituents in Al 6xxx alloys are usually Al-Fe-Si-based intermetallic phases (IMPs), which occupy a considerable volume fraction of the microstructure (~1%) and have an average size of 1 ~ 20 µm [100,877]. During the age-hardening of Al 6xxx alloys, different precipitates with a wide range of length scales, ranging from a few nm-sized Guinier-Preston (GP) zones to microscale-sized platelet-like β-Mg~2~Si precipitates, are also found [877,878]. In addition to IMPs, microscale Mg~2~Si particles are therefore regarded as possible damage initiation regions in the microstructure of these alloys [877,879,880]. Besides the size of precipitates, their morphology; their alignment and its angle relative to the loading axis; the particle-matrix interface, and their distribution also can influence damage initiation [346,879,881,882]. In the following, the major characteristics of the above-mentioned precipitates and inclusions are addressed in the context of damage initiation in Al 6xxx alloys.

Hannard et al. [883] investigated the fracture behavior of three 6xxx alloys, 6005A, 6061, and 6056, processed using different heat treatments. The volume fraction of the secondary phase particles was kept almost constant for all materials. They found that the particle size distribution is the predominant factor in the decrease of fracture strain with an enhancement of yield strength. The best result was obtained for the alloy with the smallest average distribution of particles. Particles of comparable sizes >5 µm are mostly prone to fracture (around 80% of the particles), while in contrast those of smaller sizes (≤1 µm) do not break at the same strain level,