Clustering Solutions Research Articles

Automatic Generation Technology of Scrap Denim Clothes in Quilting Art Patterns by Integrating Computer Vision.

The combination of computer vision and advanced Fuzzy C-Means algorithms, such as Hybrid OK-Means Fuzzy C-Means (HOFCM), provides a revolutionary way to automatically generate technology of Scrap Denim Clothes in Quilting Art Patterns. The research focuses on the creation of an automatic generating technique specifically designed for making quilting designs from scrap denim clothing. This study investigates the effectiveness of HOFCM in improving the process of creating quilted art patterns from discarded denim materials. Extensive experimentation and analysis have shown that HOFCM dramatically decreases computational time while improving clustering solution quality, particularly when dealing with huge, high-dimensional datasets like scrap denim cloth analysis. The study emphasizes HOFCM's potential to speed up the quilting art pattern generation process, providing designers and artists with a more efficient and precise way to transform old denim clothing into visually appealing designs. The findings highlight HOFCM's transformative impact on automatic generation technology for quilting art patterns, paving the path for innovation and originality in textile art and design. HOFCM significantly lowers time by up to 93.94% compared to regular Fuzzy C-Means on four real and one synthetic datasets. The worst-case scenario resulted in a 2.51% reduction in solution quality.

On the atypical solutions of the symmetric binary perceptron

We study the random binary symmetric perceptron problem, focusing on the behavior of rare high-margin solutions. While most solutions are isolated, we demonstrate that these rare solutions are part of clusters of extensive entropy, heuristically corresponding to non-trivial fixed points of an approximate message-passing algorithm. We enumerate these clusters via a local entropy, defined as a Franz–Parisi potential, which we rigorously evaluate using the first and second moment methods in the limit of a small constraint density α (corresponding to vanishing margin κ ) under a certain assumption on the concentration of the entropy. This examination unveils several intriguing phenomena: (i) we demonstrate that these clusters have an entropic barrier in the sense that the entropy as a function of the distance from the reference high-margin solution is non-monotone when κ⩽1.429−α/log⁡α , while it is monotone otherwise, and that they have an energetic barrier in the sense that there are no solutions at an intermediate distance from the reference solution when κ⩽1.239−α/log⁡α . The critical scaling of the margin κ in −α/log⁡α corresponds to the one obtained from the earlier work of Gamarnik et al (2022 (arXiv:2203.15667)) for the overlap-gap property, a phenomenon known to present a barrier to certain efficient algorithms. (ii) We establish using the replica method that the complexity (the logarithm of the number of clusters of such solutions) versus entropy (the logarithm of the number of solutions in the clusters) curves are partly non-concave and correspond to very large values of the Parisi parameter, with the equilibrium being reached when the Parisi parameter diverges.

Nodal cluster solutions for the Brezis–Nirenberg problem in dimensions N≥7\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N\\ge 7$$\\end{document}

We show that the classical Brezis–Nirenberg problem Δu+|u|4N-2u+εu=0,inΩ,u=0,on∂Ω\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\begin{aligned} \\Delta u + |u|^{4 \\over N-2} u + \\varepsilon u = 0,\\quad {\ ext{ in }} \\quad \\Omega , \\quad u= 0, \\quad {\ ext{ on }} \\quad \\partial \\Omega \\end{aligned}$$\\end{document}admits nodal solutions clustering around a point on the boundary of Ω\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega $$\\end{document} as ε→0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varepsilon \\rightarrow 0$$\\end{document}, for smooth bounded domains Ω⊂RN\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Omega \\subset {\\mathbb {R}^N}$$\\end{document} in dimensions N≥7\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N\\ge 7$$\\end{document}.

Anomalous twin boundary formation in magnesium alloys by rapid solidification

The aim of this work is to investigate the formation of an anomalously high fraction of twin boundaries (TBs) during laser directed energy deposition (DED) of a Mg-3Al-1 Zn (wt.%) alloy. With that goal, single tracks are deposited using different combinations of scan speed and powder feed rate. The melt pool dimensions are related to the DED parameters. Irrespective of the processing conditions, the resulting microstructures are always formed by fine grains with a very weak texture. However, some DED parameter combinations give rise to a fraction of TBs that is significantly higher than that corresponding to a random texture. These TBs are characterized by an irregular morphology and by the presence of segregated atoms, solute clusters, and nanoparticles. Additionally, the lattice in the vicinity of the TBs is highly distorted and contains a high population of stacking faults. All these characteristics set them apart from conventional tensile TBs, which exhibit a large degree of coherency and limited segregation. Rapid solidification is thus presented here as a novel avenue to design TBs with unique properties in Mg alloys. This work paves the way for future studies on grain boundary design via additive manufacturing methods, a field that is still in its infancy.

Redefined ion association constants have consequences for calcium phosphate nucleation and biomineralization

Calcium orthophosphates (CaPs), as hydroxyapatite (HAP) in bones and teeth are the most important biomineral for humankind. While clusters in CaP nucleation have long been known, their speciation and mechanistic pathways to HAP remain debated. Evidently, mineral nucleation begins with two ions interacting in solution, fundamentally underlying solute clustering. Here, we explore CaP ion association using potentiometric methods and computer simulations. Our results agree with literature association constants for Ca2+ and H2PO4−, and Ca2+ and HPO42-, but not for Ca2+ and PO43− ions, which previously has been strongly overestimated by two orders of magnitude. Our data suggests that the discrepancy is due to a subtle, premature phase separation that can occur at low ion activity products, especially at higher pH. We provide an important revision of long used literature constants, where association of Ca2+ and PO43− actually becomes negligible below pH 9.0, in contrast to previous values. Instead, [CaHPO4]0 dominates the aqueous CaP speciation between pH ~6–10. Consequently, calcium hydrogen phosphate association is critical in cluster-based precipitation in the near-neutral pH regime, e.g., in biomineralization. The revised thermodynamics reveal significant and thus far unexplored multi-anion association in computer simulations, constituting a kinetic trap that further complicates aqueous calcium phosphate speciation.

Dimerization and Dissociation: The Rate-Controlling steps of molecular Self-Assembly during solution nucleation determined by variation of gas media

To further probe into the link between solution chemistry and nucleation processes experimental and computational data are presented on unsaturated, saturated, and supersaturated solutions and in situ monitoring of nucleation chemistry of p-chlorobenzoic acid (PCBA), p-methoxybenzoic acid (PMBA) and p-nitrobenzoic acid (PNBA), three benzoic acid derivatives with different properties of para-substituents. By variation of the gas media, we were able to establish that hydrogen-bonded dimers (HD) are the basic building unite in solution for clusters and nucleuses despite of the gases and solvents, and there is a strong link between the supersaturated solution chemistry and the intrinsic supersaturation ratio, and the nucleation kinetics. Two different molecular self-assembly pathways and the rate-controlling steps were discussed, which paves new aspects for understanding the role of solution chemistry in directing nucleation processes.

Exploring Indonesian Panic Buyers’ Typologies in Time of Covid-19 Pandemic

Purpose – This study aims to investigate hitherto unresearched panic buyer (shopper) typologies among Indonesian consumers as a result of the COVID-19 outbreak. In this study, a panic buyer (shopper) is described as a customer who worries about their stock during a pandemic and decides to purchase things in bulk or in greater quantities at the start of the pandemic.
 Design/methodology/approach – To identify probable groupings of the items, an exploratory factor analysis is carried out using principal component analysis and varimax rotation. Then to identify consumer groups, a two-step cluster analysis was conducted. Lastly, an ANOVA test is run on the whole set of variables to identify differences between the detected clusters in order to validate the cluster solutions.
 Findings – Five components that served as the foundation for the typology emerged from exploratory factor analysis. Using behaviour and attitude factors including "consumption savers," "adapters reluctant," "preservers of social consciousness," "online shopping shifters," and "all-around adapters," five clusters were then defined.
 Research limitations/implications – This study contributes significantly to the body of knowledge on consumer behavior during health crises (Covid-19) by putting into context the frantic shoppers that we frequently observe during these situations. It's noteworthy that throughout the epidemic, no panic buyers (shoppers) voiced worry about health problems.
 Practical implications – This study provides essential practical information for marketing professionals. For instance, it may be necessary to consider research showing that panic buyers (shoppers) tend to be consumption savers during a pandemic when developing a price plan.
 Originality/value – Despite the fact that there have been many studies on panic behavior during COVID-19, most of them tend to concentrate on medical problems. There are now a few studies that examine several sorts of panic buyers (shoppers) based on their purchasing behaviors and attitudes. By categorizing various categories of panic buyers (shoppers) among 121 decision-makers in families, this study aims to close this literature gap.

Cluster analysis applied to obtaining reference models for building thermal performance studies

Cluster analysis has often been used to obtain reference models. However, using different methods in each analysis step may yield different results. Furthermore, the validation of the results is often neglected. This study aims to develop a cluster analysis method to obtain reference models for use in thermal performance studies of buildings from different clustering configurations. The procedure proposed is composed of six steps: (1) initial database composition, (2) data matrices formation, (3) cluster analysis, (4) determination of reference models, (5) methods validation and (6) results interpretation. Different data treatments, similarity measures and partitioning algorithms were combined in the cluster analysis to define the method with the best cluster formation. The weighting factor was the data treatment that most contributed to obtaining suitable clustering solutions. City-block, Euclidean Distance and Squared Euclidean Distance similarity measures resulted in suitable formations as well as Complete Linkage, Ward and K-means algorithms. Two clusters were obtained from the cluster methodology, for which two reference models were determined. Hypothesis tests showed that clusters differ for most performance indicators. It was concluded that the procedure proposed could identify the combination of methods that results in the best application of cluster analysis. The main contribution of this paper is to introduce a procedure for validating clusters. This procedure is an applicable technique to obtain reference models.

Tetrameric self-assembling of water-lean solvents enables carbamate anhydride-based CO2 capture chemistry

Carbon capture, utilization and storage is a key yet cost-intensive technology for the fight against climate change. Single-component water-lean solvents have emerged as promising materials for post-combustion CO2 capture, but little is known regarding their mechanism of action. Here we present a combined experimental and modelling study of single-component water-lean solvents, and we find that CO2 capture is accompanied by the self-assembly of reverse-micelle-like tetrameric clusters in solution. This spontaneous aggregation leads to stepwise cooperative capture phenomena with highly contrasting mechanistic and thermodynamic features. The emergence of well-defined supramolecular architectures displaying a hydrogen-bonded internal core, reminiscent of enzymatic active sites, enables the formation of CO2-containing molecular species such as carbamic acid, carbamic anhydride and alkoxy carbamic anhydrides. This system extends the scope of adducts and mechanisms observed during carbon capture. It opens the way to materials with a higher CO2 storage capacity and provides a means for carbamates to potentially act as initiators for future oligomerization or polymerization of CO2.

Spatial optical soliton cluster solutions in strongly nonlocal nonlinear media

A (2+1)-dimensional variable coefficient nonlinear Schrödinger equation (NLSE) is described under strongly nonlocal nonlinear media. The soliton cluster solutions are obtained by using the improved self-similar reduction and F-expansion method. The regulation of nonlinear coefficients on the dynamics of spatial optical solitons and the self-similar propagation characteristics of the optical soliton clusters are investigated. It is observed that dark solitons transmitted in nonlinear media show two different spatial distributions. It is also found that the peak intensity of the soliton cluster only fluctuates in a small range during the propagation process, but the soliton cluster after harmonic potential modulation has some loss during the propagation process. But the loss is not as dramatic as when bright solitons are modulated. A new kind of soliton cluster is formed after the modulation of dark solitons, which has great practical significance such as application in logic gates, all-optical devices.

Unexpected nucleation mechanism of T1 precipitates by Eshelby inclusion with unstable stacking faults

AbstractAluminum‐lithium (Al‐Li) alloy is one of the most promising lightweight structural materials in the aeronautic and aerospace industries. The key to achieving their excellent mechanical properties lies in tailoring T1 strengthening precipitates; however, the nucleation of such nanoparticles remains unknown. Combining atomic resolution HAADF‐STEM with first‐principles calculations based on the density functional theory (DFT), here, we report a counterintuitive nucleation mechanism of the T1 that evolves from an Eshelby inclusion with unstable stacking faults. This precursor is accelerated by Ag‐Mg clusters to reduce the barrier, forming the structural framework. In addition, these Ag‐Mg clusters trap the free Cu and Li to prepare the chemical compositions for T1. Our findings provide a new perspective on the phase transformations of complex precipitates through solute clusters in terms of geometric structure and chemical bonding functions.

An analogue on/off state-switching control method suitable for inverter-based air conditioner load cluster participating in demand response

With the significant trend towards variable frequency in heating ventilating and air-conditioner (HVAC) loads, the inverter-based air conditioner load (IACL) clusters are poised to become the mainstay in participating in demand response (DR). During the process of DR, once the indoor temperature exceeds the comfort constraint, HVAC load must be forced to exit DR, which brings correlation to the behaviour of the preceding and following periods, making cluster optimization modelling and solution difficult. In order to solve the problems, this paper proposes an analogue ON/OFF state-switching (AOSS) control method suitable for IACL cluster participating in DR. Firstly, the critical working point corresponding to the comfort constraint is analysed for eliminating the temporal coupling for IACL. Then, an AOSS control method is proposed based on the critical point analysis. The feasibility of the proposed AOSS control is validated by the experimental results, with the Hisense KFR-75W/T08SBp-A2 inverter-based air conditioning DR experimental platform. Furthermore, by using the proposed AOSS control, a day-ahead scheduling model is constructed, and, the traditional state-queuing (SQ) model is transplanted to the IACL cluster for short-term power control. And the case studies verified the effectiveness and applicability of the day-ahead scheduling model and the transplanted SQ model.

An Improved Evolutionary Multi-Objective Clustering Algorithm Based on Autoencoder

Evolutionary multi-objective clustering (EMOC) algorithms have gained popularity recently, as they can obtain a set of clustering solutions in a single run by optimizing multiple objectives. Particularly, in one type of EMOC algorithm, the number of clusters k is taken as one of the multiple objectives to obtain a set of clustering solutions with different k. However, the numbers of clusters k and other objectives are not always in conflict, so it is impossible to obtain the clustering solutions with all different k in a single run. Therefore, evolutionary multi-objective k-clustering (EMO-KC) has recently been proposed to ensure this conflict. However, EMO-KC could not obtain good clustering accuracy on high-dimensional datasets. Moreover, EMO-KC’s validity is not ensured as one of its objectives (SSDexp, which is transformed from the sum of squared distances (SSD)) could not be effectively optimized and it could not avoid invalid solutions in its initialization. In this paper, an improved evolutionary multi-objective clustering algorithm based on autoencoder (AE-IEMOKC) is proposed to improve the accuracy and ensure the validity of EMO-KC. The proposed AE-IEMOKC is established by combining an autoencoder with an improved version of EMO-KC (IEMO-KC) for better accuracy, where IEMO-KC is improved based on EMO-KC by proposing a scaling factor to help effectively optimize the objective of SSDexp and introducing a valid initialization to avoid the invalid solutions. Experimental results on several datasets demonstrate the accuracy and validity of AE-IEMOKC. The results of this paper may provide some useful information for other EMOC algorithms to improve accuracy and convergence.

Effect of non-clustering element C and Mo on irradiation-induced clustering in reactor pressure vessel steels

Commercial reactor pressure vessel steels in nuclear power plant applications contain C and other minor non-clustering elements in addition to the clustering elements of Mn, Ni and Si. However, the effect of such non-clustering elements on irradiation-induced solute clustering in ferritic steels has seldom been addressed. Herein, we reveal that a commercial RPV steel and its Fe-Mn-Ni-Si model alloy with coarse/fine grains subjected to ion irradiation exhibits different irradiation-induced clustering, particularly in chemistry, mean size, and number density of irradiation-induced MnNiSi-clusters. The different chemical compositions of the clusters indicate different thermodynamics in driving solute clustering in RPV steel and its model alloy. The different number density and size evolutions of the clusters indicate different kinetics in governing irradiation-induced clustering. The presence of C and Mo, as non-clustering elements, is primarily responsible for the different clustering behaviors of Ni, Mn and Si in the two materials exposed to ion irradiation. The research finding is of significant implication for modelling and fundamental understanding irradiation-induced clustering in commercial RPV steels.

Influence of different cycle strain amplitudes on the microstructure and properties of 2024 aluminum alloy

The effect of varying strain amplitudes on the tensile properties and microstructure of aluminum alloy 2024 after a solution treatment (ST) of 495 °C for 1 h followed by cyclic strengthening at room temperature in the presence of various strain amplitudes is methodically investigated. Cyclic strain treatment substantially leads to the enhancement of the strength and plasticity of the alloy. Different strain amplitudes result in distinct strengthening mechanisms in the tested specimens. The specimens treated with low strain amplitudes (Δεt/2 = 0.1–0.4%) exhibit the least changes in the dislocation cell size and the dislocation density. The strengthening is mainly attributed to the precise adjustment of solute cluster precipitation. In specimens treated with high strain amplitudes (Δεt/2 = 0.5–1.1%), various strain amplitudes result in various dislocation configurations. The S-phase precipitation occurs selectively in the dislocation cell walls, forming solute cluster-composite dislocation loops within the dislocation cells, enhancing both the strength and plasticity of the alloy. The rational cyclic strain treatment not only yields the achievement of the precise control of precipitate phases, but also regulates the dislocation configurations. The obtained results reveal that the high-density cluster-composite dislocation loops are capable of resolving the conflict between increasing dislocation density and reducing dislocation pile-up, providing new insights into alloy strengthening.

Moving from classical towards machine learning stances for bus passengers’ alighting estimation: A comparison of state-of-the-art approaches in the city of Lisbon

Passenger alighting estimation is a critical task in public transport (PT) management, especially for entry-only Automatic Fare Collection (AFC) transport systems where passenger alighting are not recorded. Effective estimation methods are necessary for trip analysis and route planning, offering valuable insights into passengers’ mobility patterns and, subsequently, improving the quality of service. However, the stochastic nature of passenger behaviour challenges the degree of successful alighting estimates. A classic approach to infer the alighting stops of passengers is the use of trip-chaining principles. Since these principles are dispersed across the literature in the field, their comprehensive review is pivotal to establish the best practice for alighting estimation. Still, trip-chaining approaches are unable to infer the alighting of non-commuting passengers. This paper addresses these two research gaps by: i) providing a critical overview of the existing principles and methods for alighting estimation; ii) proposing an approach to improve alighting estimation that consistently integrates the most effective state-of-the-art principles on trip-chaining; and iii) further introducing a frequent pattern mining and density-based clustering solutions to support alighting estimation for non-commuting passengers. Considering the public bus transport in Lisbon city as the guiding case study, the achieved estimation rate by the proposed assembled model is 92%. Moreover, the density-based clustering solution is found to improve the estimation of 11pp against classic trip-chaining principles. Furthermore, the proposed model and acquired results yield actionable value to enhance PT operations and services, ultimately leading to improved bus routing and quality of service.

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups similar items within a dataset into non-overlapping groups. Grey wolf hunting behaviour served as the model for grey wolf optimizer, however, it frequently lacks the exploration and exploitation capabilities that are essential for efficient data clustering. This work mainly focuses on enhancing the grey wolf optimizer using a new weight factor and the K-means algorithm concepts in order to increase variety and avoid premature convergence. Using a partitional clustering-inspired fitness function, the K-means clustering-based grey wolf optimizer was extensively evaluated on ten numerical functions and multiple real-world datasets with varying levels of complexity and dimensionality. The methodology is based on incorporating the K-means algorithm concept for the purpose of refining initial solutions and adding a weight factor to increase the diversity of solutions during the optimization phase. The results show that the K-means clustering-based grey wolf optimizer performs much better than the standard grey wolf optimizer in discovering optimal clustering solutions, indicating a higher capacity for effective exploration and exploitation of the solution space. The study found that the K-means clustering-based grey wolf optimizer was able to produce high-quality cluster centres in fewer iterations, demonstrating its efficacy and efficiency on various datasets. Finally, the study demonstrates the robustness and dependability of the K-means clustering-based grey wolf optimizer in resolving data clustering issues, which represents a significant advancement over conventional techniques. In addition to addressing the shortcomings of the initial algorithm, the incorporation of K-means and the innovative weight factor into the grey wolf optimizer establishes a new standard for further study in metaheuristic clustering algorithms. The performance of the K-means clustering-based grey wolf optimizer is around 34% better than the original grey wolf optimizer algorithm for both numerical test problems and data clustering problems.

Critical Conditions Regulating the Gelation in Macroionic Cluster Solutions.

The critical gelation conditions observed in dilute aqueous solutions of multiple nanoscale uranyl peroxide molecular clusters are reported, in the presence of multivalent cations. This gelation is dominantly driven by counterion-mediated attraction. The gelation areas in the corresponding phase diagrams all appear in similar locations, with a characteristic triangle shape outlining three critical boundary conditions, corresponding to the critical cluster concentration, cation/cluster ratio, and the degree of counterion association with increasing cluster concentration. These interesting phrasal observations reveal general conditions for gelation driven by electrostatic interactions in hydrophilic macroionic solutions.

Kinetic Monte Carlo simulations of solute clustering during quenching and aging of Al–Mg–Zn alloys

The physical mechanisms behind cluster formation during quenching and aging of age-hardening metallic alloys are poorly understood based on classical nucleation and growth theories, especially in multicomponent alloys with supersaturated vacancies and fast-diffusing solute atoms. Here, solute clustering in an Al–Mg–Zn alloy during quenching immediately after high-temperature (800 K) solution treatment is modeled with a newly developed kinetic Monte Carlo (kMC) framework. This includes accurate surrogate models trained using first-principles-calculated data to predict vacancy migration energetics on the fly as functions of local lattice occupations. First, kMC simulations at constant aging temperatures revealed that temporal evolution of the number, sizes, and compositions of solute clusters change with aging temperature. Such changes are consistent with our analyses based on classical nucleation theory (CNT) and Monte Carlo simulations. Second, the kMC algorithm was revised for simulating quenching processes by iteratively updating the kMC temperature based on simulated cooling rate profiles. These quenching simulations show that rapid solute clustering mainly occurs from ∼600 to ∼400 K during cooling. Below ∼400 K, the clustering is suppressed to a steady state because vacancies are trapped by stable clusters with sizes of ∼2 nm and high magnitudes of formation energies. The configurations of these steady-state clusters closely resemble the solute clusters we observed experimentally in 7XXX series Al–Mg–Zn-based alloy samples immediately after quenching. A two-stage vacancy trapping mechanism revealed in our simulations can provide physical guidance to tune precipitation kinetics during natural and artificial aging.

Microstructural evolution and hardening phenomenon caused by aging of AlSi10Mg alloy by laser powder bed fusion

Microstructures and age-hardening phenomena of directly aged (artificial aged) AlSi10Mg alloys fabricated by laser powder bed fusion (LPBF) were characterized using scanning transmission electron microscopy, atom probe tomography, and Vickers hardness testing. The microstructure derived from overlapping melt pools has a full cellular structure consisting of eutectic Si walls surrounding α-Al cells. In the initial stage of aging, solute clusters with density on the order of 1024/m3 were formed in α-Al cells. By prolonging the aging time further, fine Si particles of about 50 nm in diameter precipitated. Before Si precipitation, the hardness of the aged sample was clearly greater than that of the as-built state. With further aging time, the hardness increased further because of the Si precipitation. Cluster analysis revealed that the number density and the size of clusters increased from as-built state by aging, whereas the types of the solute clusters remained almost unchanged by aging. The results indicate that the nanoscale clusters within the α-Al cells, which increase via aging, produce age-hardening effect.