Alloy Composition Research Articles

AN ASSESSMENT OF PHASE CHANGE MATERIAL, PREPARATION TECHNIQUES AND CONTAINMENT MATERIAL WITH DIFFERENT MOLTEN SALTS FOR THERMAL ENERGY STORAGE SYSTEM

Molten salts are extensively used as high-temperature heat transfer media and thermal energy storage (TES) materials in concentrating solar power (CSP) plants. Molten salt corrosion is a critical factor influencing the safe operation of the system. This work comparatively summarizes various factors influencing molten salt corrosion, including temperature, impurities, alloy composition and gas atmosphere. Additionally, the corrosion mechanisms of nitrate, carbonate, chloride, and fluoride-based mixture salts are meticulously reviewed. The demand for high-temperature storage is indeed increasing, driven by the need for more efficient and sustainable energy solutions. High-temperature storage systems, particularly those utilizing phase change materials (PCMs) and molten salts, play a key role in enhancing energy efficiency and reducing carbon footprint. By improving the efficiency of energy storage and reducing the need for fossil fuels, high-temperature storage systems help decrease greenhouse gas emissions, contributing to the fight against global warming. The paper focused on enhancing the corrosion resistance of container materials in terms of improving PCM thermal conductivity and stability.

Sintered Nd-Fe-B magnets from gas-atomized powders and the effect of lubricants

The production of sintered Nd-Fe-B magnets currently relies on alloys solidified via strip casting. However, the spacing of Nd2Fe14B lamellae in the strip-cast alloys is not optimal for obtaining monocrystalline powders with a mean particle size significantly smaller than 3 μm. Magnets made of finer monocrystalline powder are expected to have smaller grains and a higher coercivity, which would reduce current reliance on scarce and expensive heavy-rare-earth elements. Because of finer Nd2Fe14B grains, gas atomized Nd-Fe-B alloys generally yield more homogeneous fine powders than the strip-cast alloys. In this study, sintered Nd-Fe-B magnets were prepared from gas-atomized alloy and their properties were compared with those of magnets made from the strip-cast alloy of the same chemical composition. The gas-atomized alloy was found to contain an additional metastable phase of the TbCu7-type structure. As compared to the strip-cast alloy, the gas-atomized alloy yielded a jet-milled powder with smaller mean particle diameter and sintered magnets with finer grains and a higher coercivity. The metastable phase specific to the gas-atomized alloys was eventually eliminated during the sintering process without damaging the magnetic properties. Treatment of the new ultrafine powders prior to their compaction in a magnetic field with lubricants ensured a higher degree of the crystallographic alignment and lead to a higher coercivity while still maintaining high remanence. In particular, a magnet prepared from the gas-atomized alloy treated with methyl octanoate exhibited a remanence of 1.38 T, a coercivity of 1278 kA/m, and a maximum energy product of 359 kJ/m3. The results indicate that a simultaneous utilization of the gas-atomized alloys and appropriate lubricants may reduce the need for the heavy rare earths, making the magnets more sustainable.

Tuning the performances of smoke-light signaling agents by Zn-Mg alloys

In this study, the thermal behavior, combustion behavior, and smoke/light signal effects of smoke-light signaling agents were regulated using Zn-Mg alloys with different Mg contents (10 wt%, 20 wt%, 30 wt%, 50 wt%, 70 wt%) and particle sizes (100–200 mesh, 200–325 mesh, >325 mesh) under an oxygen balance of −20. The Zn-Mg alloy’s morphology and phase composition were characterized using SEM/EDS and XRD. The thermal performance was analyzed with TG/DSC, while the combustion process was recorded using high-speed camera and infrared thermal imaging. The visible light spectrophotometer and smoke chamber experiment tested the smoke and light effects. The results showed that increasing Mg content raised the combustion temperature to over 1400 °C, enhanced luminous intensity to over 5000 cd, reduced solid residue to under 6 %, and shortened combustion time from 7.9 s to 2.24 s. Smaller particle sizes also improved these effects. Alloys with higher Zn content produced denser smoke but had higher solid residue. This study demonstrates the potential for precise control over combustion and smoke-light effects by adjusting alloy composition and particle size, making Zn-Mg alloys promising candidates for future pyrotechnic formulations.

Effect of La–Ni@3DG catalyst concentration on the hydrogen storage properties of MgH2

This research paper explores the influence of varying the quantity of La–Ni@3DG catalyst on the hydrogen storage capabilities of MgH2. La–Ni@3DG catalyst was prepared using lanthanum chloride heptahydrate and nickel chloride hexahydrate as raw materials combined with three-dimensional graphene, then mixed with MgH2 via ball milling to prepare MgH2 + x wt.% La–Ni@3DG (x = 0, 3, 5, 7) composite alloy. X-ray diffraction analysis showed that the composite alloy was mainly composed of the MgH2 phase, with the presence of Mg and (La, Ni) phases detected. With increased La–Ni@3DG addition, the broadening of the MgH2 diffraction peak significantly improved, indicating the catalyst promoted alloy phase structure uniformity and crystallinity. The composite alloy forms new phases such as LaH3, LaNi3H6, and Mg2NiH4 during hydrogen absorption. These phases cooperate with in-situ formed LaH3 and Mg2Ni to significantly improve the reversible hydrogen absorption and desorption properties of MgH2. Adding La–Ni@3DG catalyst has a significant positive effect on the hydrogen absorption and desorption of MgH2, leading to a considerable improvement. At 553 K, the composite alloy reaches hydrogen absorption equilibrium faster than pure MgH2, and the temperature for hydrogen absorption is significantly reduced to 373 K. The dehydrogenation performance is also improved, with 5 wt.% La–Ni@3DG is showing the best dehydrogenation performance at 553 K. The initial dehydrogenation temperature commences at approximately 505 K, while the dehydrogenation activation energy attains its minimum value: a remarkably low 109.91 kJ/mol H2. Additionally, the addition of catalyst reduces the pressure of the hydrogen absorption and desorption platform, effectively reducing the hysteresis effect during hydrogen absorption and desorption when the catalyst content is 5 wt.%, the enthalpy change and entropy change of the composite alloy reach a minimum, −70.45 kJ/mol H2 and 74.38 kJ/mol H2 (hydrogen absorption), −101.93 J/K/mol H2 and 103.68 J/K/mol H2 (hydrogen desorption), respectively.

High performance plain carbon steels obtained through 3D-printing

Over the last century, improvement in mechanical performance of structural metals has primarily been achieved by creating more and more complex chemical compositions. Such compositional complexity raises costs, creates supply vulnerability, and complicates recycling. As a relatively new metal processing technique, metal 3D-printing provides a possibility to revisit and simplify alloy compositions, achieving alloy plainification, which enables simpler materials to be used versatilely. Here, we demonstrate that high performance simple plain carbon steels can be produced through 3D-printing. Our 3D-printed plain carbon steels achieve tensile and impact properties comparable, or even superior to those of ultra-high strength alloy steels such as Maraging steels. The sequential micro-scale melting and solidification intrinsic to 3D-printing provides sufficient cooling to directly form martensite and/or bainite, strengthening the steels while maintaining microstructural and property homogeneity without dimensional limitations or heat treatment distortion and cracking. By manipulating 3D-printing parameters, we can tailor the microstructure, thereby control the properties for customized applications. This offers a scalable approach to reduce alloy complexity without compromising mechanical performance and highlights the opportunities for the 3D-printing to help drive alloy plainification.

Tailoring hydrogen diffusion pathways in Mg-Ni alloys through Gd addition: A combined experimental and computational study

Magnesium-based alloys represent a promising avenue for solid-state hydrogen storage, yet their practical implementation is impeded by sluggish kinetics and thermodynamic stability. This study presents a systematic investigation into the effects of gadolinium (Gd) and nickel (Ni) content on the microstructure and hydrogen storage properties of Mg-Ni-Gd alloys. Through advanced characterization techniques and density functional theory calculations, we elucidate that Gd preferentially occupies sites in the Mg2Ni phase rather than the Mg matrix, facilitating hydrogen diffusion. The Mg-7.8Ni-2.2Gd alloy demonstrates superior performance, absorbing 5.8 wt% hydrogen at 300 °C, compared to 5.3 wt% for Mg-10Ni under identical conditions. Remarkably, this composition exhibits excellent low-temperature kinetics, absorbing 4.5 wt% hydrogen within 30 min at 120 °C and maintaining 99 % capacity retention after 128 cycles. Post-initial hydrogenation, Gd accumulates at Mg2Ni surfaces, amplifying its role as a “hydrogen pump”. Kinetic analysis using the Chou model for absorption and the Johnson-Mehl-Avrami-Kolmogorov model for desorption provides quantitative insights into the improved hydrogen storage mechanisms. Our computational studies corroborate these experimental findings, revealing that Gd doping in Mg2Ni substantially improves the hydrogen absorption ability, and surface penetration barrier of H atom deeply decreased. This work not only advances our understanding of the complex interplay between composition, microstructure, and hydrogen storage properties in Mg-based alloys but also establishes design principles for high-performance hydrogen storage materials.

Wear Resistance Design of Laser Cladding Ni-Based Self-Fluxing Alloy Coating Using Machine Learning.

To improve the collaborative design of laser cladding Ni-based self-fluxing alloy (SFA) wear-resistant coatings, machine learning methods were applied. A comprehensive database was constructed from the literature, linking alloy composition, processing parameters, testing conditions, and the wear properties of Ni-based SFA coatings. Feature correlation analysis using Pearson's correlation coefficient and feature importance assessment via the random forest (RF) model highlighted the significant impact of C and B elements. The predictive performance of five classical machine learning algorithms was evaluated using metrics such as the squared correlation coefficient (R²) and mean absolute error (MAE). The RF model, which exhibited the best overall performance, was further combined with a genetic algorithm (GA) to optimize both composition and processing parameters collaboratively. This integrated RF-GA optimization system significantly enhanced efficiency and successfully designed multiple composition and process plans. The optimized alloy demonstrated superior wear resistance with an average friction coefficient of only 0.34, attributed to an enhanced solid solution strengthening effect (110 MPa) and increased hard phase content (52%), such as Ni₃Si, CrB, and NbC. These results provide valuable methodological insights and theoretical support for the preparation of laser cladding coatings and enable efficient process optimization for other laser processing applications.

Predicting the Curie temperature of magnetic materials with automated calculations across chemistries and structures

We develop a technique for predicting the Curie temperature of magnetic materials using density functional theory calculations suitable to include in high-throughput frameworks. We apply four different models, including physically relevant observables, and assess numerical constants by studying 32 ferro- and ferrimagnets. With the best-performing model, the Curie temperature can be predicted with a mean absolute error of approximately 126 K. As predictive factors, the models consider either the energy differences between the magnetic ground state and a magnetically disordered paramagnetic state, or the average constraining fields acting on magnetic moments in a disordered local moments calculation. Additionally, the energy differences are refined by incorporating the magnetic entropy of the paramagnetic state and the number of nearest magnetic neighbors of the magnetic atoms. The most advanced model is found to extend well into Fe1−xCox alloys, indicating the potential efficacy of utilizing our model in designing materials with tailored Curie temperatures by altering alloy compositions. This examination can illuminate the factors influencing magnetic transition temperatures in magnetic materials and provide insights into how they can be employed to make quantitative predictions of Curie temperatures. Our approach is not restricted to specific crystal structures or chemical compositions. It offers a more cost-effective alternative, in terms of human time and need for hands-on oversight, to other density functional theory methods for predicting the Curie temperature. As a result, it provides a practical strategy for conducting high-throughput screening for new technologically applicable magnetic materials. Alternatively, it can complement ML-based screening of magnetic materials by integrating physical principles into such approaches, thereby enhancing their prediction accuracy. Published by the American Physical Society 2024

Time series forecasting of multiphase microstructure evolution using deep learning

Microstructure evolution, which plays a critical role in determining materials properties, is commonly simulated by the high-fidelity but computationally expensive phase-field method. To address this, we approximate microstructure evolution as a time series forecasting problem within the domain of deep learning. Our approach involves implementing a cost-effective surrogate model that accurately predicts the spatiotemporal evolution of microstructures, taking an example of spinodal decomposition in binary and ternary mixtures. Our surrogate model combines a convolutional autoencoder to reduce the dimensional representation of these microstructures with convolutional recurrent neural networks to forecast their temporal evolution. We use different variants of recurrent neural networks to compare their efficacy in developing surrogate models for phase-field predictions. On average, our deep learning framework demonstrates excellent accuracy and speedup relative to the “ground truth” phase-field simulations. We use quantitative measures to demonstrate how surrogate model predictions can effectively replace the phase-field timesteps without compromising accuracy in predicting the long-term evolution trajectory. Additionally, by emulating a transfer learning approach, our framework performs satisfactorily in predicting new microstructures resulting from alloy composition and physics unknown to the model. Therefore, our approach offers a useful data-driven alternative and accelerator to the materials microstructure simulation workflow.

Chemical disorder effects on Gilbert damping of FeCo alloys

The impact of the local chemical environment on the Gilbert damping in the binary alloy Fe100−xCox is investigated, using computations based on density functional theory. By varying the alloy composition x as well as Fe-Co atom positions we reveal that the effective damping of the alloy is highly sensitive to the nearest-neighbor environment, especially to the amount of Co and the average distance between Co-Co atoms at nearest-neighbor sites. Both lead to a significant local increase (up to an order of magnitude) of the effective Gilbert damping, originating mainly from variations of the density of states at the Fermi energy. In a global perspective (i.e., making a configuration average for a real material), those differences in damping are masked by statistical averages. When low-temperature explicit atomistic dynamics simulations are performed, the impact of short-range disorder on local dynamics is observed to also alter the overall relaxation rate. Our results illustrate the possibility of local chemical engineering of the Gilbert damping, which may stimulate the study of new ways to tune and control materials aiming for spintronics applications. Published by the American Physical Society 2024

High-Throughput UV-Induced Synthesis and Screening of Alloy Electrocatalysts.

The combination of different elements in alloy catalysts can lead to improved activity as it provides opportunities to tune the electronic structures of surface atoms. However, the synthesis and performance screening of alloy catalysts through a vast chemical space are cost- and labor-intensive. Herein, a UV-induced, high-throughput method is reported for the synthesis and screening of alloy electrocatalysts in a fast and low-cost manner. A platform that integrates 37 mini-reaction-cells enables simultaneous UV-induced photodeposition of alloy nanoparticles with up to 37 compositions. These mini-reaction-cells further allow a transfer-free, high-throughput electrochemical performance screening. Binary (PtPd, PtIr, PdIr), ternary (PtPdIr, PtRuIr) and quaternary (PtPdRuIr) alloys have been synthesized with the activity of the binary alloys (57 compositions) for hydrogen evolution reaction (HER) and oxygen reduction reaction (ORR) being screened. The predicted high performance of identified alloy compositions are subsequently validated by standard measurements using a rotating disk electrode configuration. It is found that the as-synthesized alloy nanoparticles are rich in twin boundaries and thus possess lattice strain. Density functional theory calculation implies that the high ORR activity of the screened Pt0.75Pd0.25 alloy originates from the interplay between the differentiated adsorption sites because of alloying and the strain-induced modulation of the d-band center.

Production of Ni[sbnd]Fe alloy by combined recycling of waste nickel-metal hydride batteries and waste toner powder

This paper elucidates a novel and sustainable way of bringing two major sub e-waste streams (waste electrodes of Ni-MH battery and waste toner powder) together to manufacture NiFe alloy. Reduction of oxides (present in the Ni-MH battery electrode) with carbon sourced from waste toner was performed at 1550 °C which observed the formation of NiFe alloy as reaction proceeded to 1 h. Percentages of waste toner in the 2 g feed material containing waste electrode mass was varied to study the metal/slag formation and separation alike. The product and slag phases were both analysed by X-ray powder diffraction, Scanning electron microscopy, Energy dispersive x-ray spectroscopy, Laser induced breakdown spectrometer to confirm the formation and metallic composition of the NiFe alloy (>75 % Ni and > 14 % Fe) and the mixture of rare earth oxides present in the slag phase. In addition to manufacturing the metallic alloy, which evinces a possibility of being used as a feedstock in industrial applications, this innovative recycling technique also brings down the burden on landfills.

Edge dislocations, alloy composition, and grain boundaries effects on the mechanical properties in NiCo binary alloy

In this research, we investigated the mechanical properties of NiCo binary alloy both with and without grain boundaries, across various alloy compositions. We investigated the effects of edge dislocation, alloy compositions, and grain boundaries on the mechanical properties of FCC NiCo\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NiCo$$\\end{document} using molecular dynamics (MD) simulations. By analyzing the influence of the grain boundaries with different alloy compositions on several mechanical properties, we were able to gain a deeper understanding of how these characteristics were modified. The elastic moduli increased with increasing grain boundaries for a specific chemical composition, indicating that NiCo\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NiCo$$\\end{document} becomes more rigid. The anisotropy factor analysis showed that NiCo\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NiCo$$\\end{document} have a natural tendency toward anisotropy, which is further influenced by the presence of grain boundaries. Grain boundaries have a significant effect on strength and ductility across a wide range of alloy compositions. These results indicate that a deeper comprehension of these implications can aid in designing improved binary alloy with superior mechanical characteristics.

Role of Short-Range Order on Diffusion Coefficients in the Li-Mg Alloy.

Li-Mg alloys are important because of their beneficial role in fostering uniform plating and stripping of lithium in all-solid-state batteries. The alloy forms a solid solution on the BCC crystal structure when the lithium content is greater than x ≈ 0.3. The activation barriers of lithium and magnesium exchanges with a vacancy, crucial for substitutional diffusion, are predicted to be exceptionally low and almost identical, with negligible dependence on the alloy composition. The equilibrium vacancy concentration at room temperature is predicted to be very low, and it also remains almost constant with no dependence on Mg content in the alloy (for ). Nevertheless, both experiments and kinetic Monte Carlo simulations indicate that the tracer diffusion coefficients decrease by almost an order of magnitude with the addition of Mg to the alloy. In this contribution, the crucial role that chemical short-range order plays in affecting the diffusion coefficients is studied. Chemical short-range order is found to increase the effective activation barrier for lithium and magnesium diffusion by making successive atomic hops with vacancies correlated.

Machine learning algorithms for optimization of magnetocaloric effect in all-d-metal Heusler alloys

This study examines the application of machine learning algorithms, specifically the Random Forest regression model, to optimize the magnetocaloric effect in all-d-metal Heusler alloys. The model was trained using descriptors related to the mean properties of individual atoms, the properties of simple compounds in their ground state, and measures of chemical disorder. It demonstrated high accuracy in predicting structural properties, while exhibiting moderate accuracy in predicting magnetic properties. To identify optimal alloy compositions, a genetic algorithm was used to find those with the greatest differences in magnetization during martensitic transitions. Using this combined approach, the Ni–Co–Mn–Ti alloy system was thoroughly explored, resulting in the discovery of an alloy with a maximum magnetization difference. These results are consistent with previous research based on density functional theory and highlight the effectiveness of integrating machine learning with genetic algorithms for the discovery of new materials with outstanding magnetocaloric properties. The study emphasizes the need for further refinement of models capable of accurately predicting complex magnetic interactions, which is essential for fully leveraging the potential of all-d-metal Heusler alloys in practical applications.

Origin and Potentialities of the Selective Host‐Matrix Effect in Hydrogenated III‐V‐N Alloys.

AbstractIn a previous study, puzzling experimental results regarding the neutralization of N effects on the bandgap energy in the hydrogenated In0.21Ga0.79As0.975N0.025 alloy were explained by theoretical investigations revealing the occurrence of a novel phenomenon: thermal annealing changes the InGaAs host‐matrix by making it able to exert a selection of the N–H complexes responsible of the N neutralization. In view of technological applications of this effect, the underlying selective host‐matrix model was carefully checked here by theoretically investigating InGaAsN alloys of different compositions ranging from Ga‐rich to In‐rich. As a most important result, such an extensive investigation inspired a comprehensive explanation of the origin of the host‐matrix effect which clarifies how the effect works, firmly establishes its occurrence, gives indications for a bandgap tuning based on a matrix design, sets conditions for its extension to other III‐V‐N alloys and suggests ways to induce fine changes of the alloy mechanical behavior.

Influence of Cu and Co addition on metallurgical and wear characteristics of AlCrFeNi high entropy alloy

The creation of new alloys with improved qualities has become essential in modern industries for high-performance materials. This work’s main objective is to use vacuum arc melting (VAM) to synthesize two different high-entropy alloys (HEAs): AlCrFeNiCu and AlCrFeNiCo. The mechanical properties, phase composition, grain boundaries, and alloy composition of the HEAs were studied. The predominant crystal structure, the Body-Centred Cubic (BCC) phase was obtained for both alloys. Significantly, the Co-containing HEA showed a smaller particle size than the Cu-containing HEA, which led to a 14.21% increase in microhardness. It indicates that the Co-based HEA will likely perform better than the Cu-based HEA in applications prone to abrasion, and indentation, and requiring high hardness levels based on the observed microstructure and hardness parameters. According to wear surface morphology studies, main effects analysis and ANOVA show that increasing loads and sliding distances increase wear rate, whereas sliding velocity has less effect. The best wear rate-reducing parameters are 10 N, 0.5 m/s, and 500 m for Cu-containing HEAs, and the same can be predicted using regression analysis. The study categorizes the intricate worn surface structure by describing different surface properties and wear mechanisms, such as grooves, delamination, adhesive wear, and pitting.

A multi-scale modeling framework for solidification cracking during welding

A multi-scale multi-physics modeling framework has been developed to predict solidification cracking susceptibility (SCS) during welding. The framework integrates a thermo-mechanical finite element model to simulate temperature and strain rate profiles during welding, a cellular automata model to simulate the solidified microstructure in the weld pool, and a granular model to calculate the pressure drop in the mushy zone. Verification was achieved by comparing the model’s predictions with welding experiments on two steels, demonstrating its capability to accurately capture the effects of process parameters, grain refinement, and alloy composition on SCS. Results indicate that increasing welding velocity, while maintaining a constant power-to-velocity ratio, extends the size of the mushy zone and increases the maximum pressure drop in the mushy zone, leading to higher SCS. Grain refinement decreases separation velocities and the permeability of liquid channels, which increases SCS, but it also raises the coalescence temperature, resulting in an overall reduction in SCS. Alloy composition impacts SCS through thermal diffusivity and segregation. Lower thermal diffusivity or stronger segregation tends to elongate the mushy zone, resulting in an increase in SCS. This framework provides a robust tool for understanding the mechanisms of solidification cracking, optimizing welding parameters to prevent its occurrence, and comparing SCS of different compositions during alloy design.

Characteristics of foundries and alloy composition of modern and contemporary Korean artistic bronze

This study investigated the changes in foundries and production techniques of modern and contemporary Korean artistic bronze constructions from the 1910s to the 2000s. To determine the characteristics of foundries and changes in alloy composition based on era, interviews with key sculptors and foundry technicians, analysis of archival materials related to casting, and nondestructive X-ray fluorescence analysis of 92 bronze sculptures by Manlin Choi, were conducted. This study found that foundries for artistic bronze underwent technical and organizational changes from the 1910s to the 2000s. Modern casting techniques and foreign technologies have been introduced since the late 1960s that have resulted in various production methods. The portable X-ray fluorescence analysis of Manlin Choi’s 92 bronze sculptures revealed distinct differences in alloy composition depending on the foundry, findings that have also been supported by records (contracts, receipts, payment confirmations). This study systematically organizes the changes in foundries and production techniques of modern and contemporary Korean artistic bronze, identifying correlations between alloy composition and foundries, and is expected to provide important data for the future conservation and scientific authentication of artistic bronze.

Estimation of Quality of Seam Welds in AlMgSi(Cu) Extrusion by Using an Original Device for Weldability Testing.

Extrusion welding of AlMgSi(Cu) alloys is carried out by using porthole dies, as a result of which hollow shapes are formed with longitudinal seam welds. In the case of the inappropriate selection of the chemical composition of the aluminium alloy or improper metal welding conditions, the weld may have reduced strength in relation to that of the base material, thus weakening the strength of structures based on aluminium extrudates. The prediction of metal welding conditions, depending on the chemical composition of the alloy, the temperature and the unit welding pressures, effectively supports the design of porthole dies, thus significantly reducing the number of necessary extrusion tests and die geometry corrections needed during its implementation in industrial practice, and consequently significantly reducing production costs. In this work, an original laboratory test device simulating the behaviour of metal in a welding chamber of a porthole die was applied to examine the ability of AlMgSi(Cu) alloys to produce high-quality joints. Two different chemical compositions of AlMgSi(Cu) aluminium alloys differing in Mg, Si and Cu contents were used: alloy no. 1A (0.68% wt. Mg, 1.04% wt. Si, 0.61% wt. Cu) and alloy no. 3A (0.8% wt. Mg, 1.21% wt. Si, 1.22% wt. Cu). The weldability tests were carried out under various welding temperatures of 450, 500 and 550 °C and under various welding pressures of 150 MPa, 250 MPa and 350 MPa. The microstructural changes in the produced welds were evaluated with the use of OM and SEM/EDS with chemical analysis in micro-areas, whereas the mechanical effects were evaluated by using a static tensile test. Samples after static tensile testing were subjected to fractographic tests to determine the nature of the fractures. The highest values of relative weld strength were obtained under the highest welding temperature of 550 °C and the highest unit welding pressure of 350 MPa: 87% for alloy number 1/1A (high-strength weld), and 62% for alloy number 6/3A (medium-strength weld). Finally, the extrusion tests were performed in industrial conditions with an examination of the EBSD structure and strength of the longitudinal welds. High values of relative weld strength for extrudates from alloy no. 1/1A and alloy no. 3A, 96% and 89%, respectively, were found, which confirmed the previous weldability testing results.