Search Results (337)

Determining the causes of shrinkage porosity in Al-Si-Mg alloy castings with an increased proportion of secondary materials is very important and poses many problems. The reason for this is the existence of two opposing theories. One assumes that plate-like α-Al₅FeSi (β-Fe) phase segregations cause shrinkage porosity. At the same time, the other believes that thin, double-layered oxide films with air-filled voids are responsible for the porosity. To address this question, the popular commercial alloy AlSi7Mg0.6 (EN AC-42200) was selected for testing. This alloy was cast into three series: with increasing content from 0.3 to 0.8 wt.% Fe and a constant content of approx. 0.1 wt.% Mn, the second with increasing iron and manganese contents (Mn/Fe = 1/2) (both series cast by gravity), and the third series under low pressure (approx. 0.15 MPa) with increasing content from 0.8 wt.% to 1.3 wt.% Fe and a constant content of approx. 0.1 wt.% Mn. Based on DTA (Derivative Thermal Analysis) and DSC (Differential Scanning Calorimetry) tests, the order of crystallizing components in various Mn/Fe combinations was determined. It has been found that the most unfavorable phases in gravity castings are the primary crystallizing β-Al₅FeSi (β-Fe) phases (over 0.7 wt.% Fe), which are the leading cause of shrinkage porosity. After adding manganese to the alloy, thermal tests indicate that after the formation of α(Al) dendrites but before the eutectic α(Al) + β(Si), the Al₁₅(Fe,Mn)₃Si₂ phase crystallizes. In die-cast samples, plate-like α-Al₅FeSi (β-Fe) phase precipitates were also observed, but their share is small, and their average length does not exceed 20–30 µm. However, microstructural tests revealed the presence of rare oxides. It can therefore be assumed that in the AlSi7Mg0.6 alloy cast under pressure, the primary source of shrinkage porosity is not plate-like α-Al₅FeSi (β-Fe) phase precipitates, but double-layer oxide films. In all cases, it was found that the Mg₂Si phase formed at the end of crystallization does not affect shrinkage porosity. Full article

This work aims to evaluate the economic and environmental performance of hybrid additive manufacturing (HAM) chains with metal forming operations in comparison with conventional manufacturing approaches. The approach integrates processes such as Wire-Arc Directed Energy Deposition (DED-Arc), machining, and incremental sheet forming to combine material deposition, shaping, and finishing within a single processing chain. To support this, a process-based cost model (PBCM) was developed to estimate production costs by linking process parameters with technological and operational variables and implementing computer-assisted modeling of the processing chain for identification of the production costs and corresponding key cost drivers. In parallel, a cradle-to-gate Life Cycle Assessment (LCA) was performed to evaluate environmental impacts across the stages of the HAM chain. The results indicate that direct labor, material, and machine usage are the primary cost drivers in the HAM chain. Compared to conventional chains of machining from solid or die casting, HAM achieves high reductions in production cost, from 67.8% to 84.5%, and in environmental impact of up to one order of magnitude, due to lower material consumption and independence from dedicated tooling. Overall, this work provides an integrated framework for the economic and environmental assessment of HAM, laying the foundation for future industrial implementation. Full article

This study systematically investigates the microstructural characteristics and mechanical properties of resistance spot-welded joints in 3 mm thick non-heat-treatable die-cast AlSi₇MnMg alloy, with particular focus on the influence of element segregation and secondary phase behavior on fracture mechanisms and the process window. The results indicate that the weld nugget exhibits a typical dual structure consisting of columnar and equiaxed grain zones, with a corresponding “M”-shaped microhardness profile. Significant segregation of Si, Fe, and Mn elements at the nugget boundary was observed, leading to the formation of low-melting-point eutectic regions and secondary phase bands. These features induce microporosity along segregation trajectories, serving as crack initiation sites and resulting in a notably narrowed spot welding process window. From the perspective of microstructure and solute behavior during non-equilibrium solidification, this work elucidates the intrinsic mechanisms governing joint performance and process stability in non-heat-treatable die-cast aluminum alloys, providing a theoretical basis for their engineering applications. Full article

The morphology and distribution of intermetallic compounds (IMCs), such as Al₆Mn, Al₂Cu, and Al₁₂Fe₃Si₂, play a critical role in determining the mechanical properties of 3xxx series aluminum alloys. In this study, the compressive behavior of these IMCs was systematically investigated using the modified embedded atom method (MEAM) potential and the large-scale atomic/molecular massively parallel simulator (LAMMPS) under various temperatures and strain rates. The results show that as the temperature increases from 623 K to 823 K, both the compressive strength and elastic modulus of the IMCs decrease significantly. Al₁₂Fe₃Si₂ exhibits the lowest compressive strength, ranging from 1.1 to 9.8 GPa, while Al₂Cu demonstrates the highest compressive strength, ranging from 3.9 to 19.8 GPa. Within this temperature range, Al₆Mn and Al₃Fe show relatively poor stability. At a strain rate of 1 × 10¹⁰ s⁻¹, the thermal sensitivity coefficients for compressive strength are 0.010 and 0.008, and those for elastic modulus are 0.173 and 0.126, respectively. In contrast, Al₂Cu exhibits the best stability, with thermal sensitivity coefficients of 0.005 for compressive strength and 0.041 for elastic modulus. Furthermore, the influence of strain rate diminishes notably under lower temperatures. Across the entire temperature range, Al₂Cu displays the highest overall stability, with a strain rate sensitivity index ranging from 0.3527 to 0.3738. Full article

This work proposes a data-driven design framework for high-pressure die-cast (HPDC) aluminum alloys that integrates robust data refinement, machine learning (ML) modeling, explainability, and inverse design. A total of 1237 tensile-test records from T5-aged HPDC alloys were aggregated into a curated dataset of 382 unique composition–heat-treatment combinations. Four regression models—Ridge regression, Random Forest (RF), XGBoost (XGB), and a multilayer perceptron (MLP)—were trained to predict yield strength (YS), ultimate tensile strength (UTS), and elongation (EL). Tree-based ensemble models (XGB and RF) achieved the highest accuracy and stability, capturing nonlinear interactions inherent to industrial HPDC data. In particular, the XGB model exhibited the best predictive performance, achieving test R² values of 0.819 for UTS and 0.936 for EL, with corresponding RMSE values of 15.23 MPa and 1.112%, respectively. Feature-importance and SHapley Additive exPlanations (SHAP) analyses identified Mn, Si, Mg, Zn, and T5 aging temperature as the most influential variables, consistent with metallurgical considerations such as microstructural stabilization and precipitation strengthening. Finally, RF-based inverse design suggested new composition–process candidates satisfying UTS > 300 MPa and EL > 8%, a region scarcely represented in the experimental dataset. These results illustrate how interpretable ML can expand the feasible design space of HPDC aluminum alloys and support composition–process optimization in industrial applications. Full article

Aluminum alloy 2024 (AA2024) is widely used in the aerospace sector, where a fine, uniform, and equiaxed grain structure is crucial for achieving enhanced mechanical properties. This study examines the effect of dynamic solidification, assisted by mechanical vibrations and conformal cooling, on the microstructural evolution and mechanical properties of permanent mold-cast AA2024. Mechanical vibrations were applied during solidification in the frequency range of 15–45 Hz and acceleration of 0.5–1.5 g. Process parameters, including pouring temperature, die temperature, vibration frequency, and acceleration, were optimized using an L9 orthogonal array based on the Taguchi method. Analysis of variance (ANOVA) was performed to determine the significance of the aforementioned process parameters. In addition, the alloy’s microstructure was observed through a microscope, which revealed a transition from dendritic to non-dendritic microstructure due to dynamic solidification. The average grain size of the alloy was significantly reduced by 40.9%. Moreover, the values of hardness and Ultimate Tensile Strength (UTS) of the alloy were improved by 13.5% and 10.6%, respectively. Optimal results were obtained at a pouring temperature of 750 °C, die temperature of 150 °C, frequency of 45 Hz, and acceleration of 1.0 g. Moreover, uncertainty analysis for all three responses was also performed. Full article

Quality assurance in aluminum die casting is critical, as internal defects—such as porosity—can compromise structural integrity and significantly reduce component service life. In the cost-sensitive manufacturing environment of Germany, early and automated rejection of defective parts is essential to minimize scrap, rework, and energy waste. This study investigates the feasibility and performance of deep learning for automated defect detection in industrial X-ray images of two series-production aluminum die-cast components. A systematic methodology was employed: first, candidate object-detection frameworks (YOLOv5 vs. Faster R-CNN) were evaluated under real-time constraints (<2 s per image) on standard industrial hardware; subsequently, position-specific and single global models were trained on annotated datasets. A systematic hyperparameter study—focusing on input resolution, learning rate, and loss weights—was conducted to optimize accuracy and robustness. The best-performing models achieved F1-scores up to 0.87, with position-specific models outperforming the single global model on average. The approach was validated under real production conditions at Hengst SE (Nordwalde), demonstrating practical feasibility, strong acceptance among quality professionals, and significant potential to accelerate inspections and standardize decision-making. The results confirm that deep learning is a viable alternative to rule-based image processing and holds substantial promise for automating X-ray inspection workflows in aluminum die casting, contributing to both operational efficiency and sustainability goals. Full article

This study investigates the effects of anodizing and welding parameters on the microstructure and mechanical properties of laser-welded die-cast A356 aluminum alloy. The influence of different surface oxidation conditions, namely, no anodized film (NAF), single-sheet anodized film (SSAF), and double-sheet anodized films (DSAF), was assessed. The porosity, elemental distribution, and mechanical behavior was systematically analyzed. The results indicate that anodizing reduces the fusion zone (FZ) size by approximately 5%–15% and increases porosity, primarily due to the thermal-barrier effect, energy consumption during film decomposition, and hydrogen release. Welding speed and defocusing amount have a significant impact on heat input and melt-pool dynamics. Quantitative analysis revealed that lower welding speeds and positive defocusing amount increased the FZ size by 15% and porosity by 2%–5%. In contrast, optimized conditions (welding speed of 4 m/min and 0 mm defocus) enhanced gas evacuation and minimized pore formation. Elemental analysis showed that anodizing promoted Si enrichment and increased oxygen incorporation, with oxygen content rising by 10%–15%, from 0.78 wt% (NAF) to 1.31 wt% (DSAF). Microhardness testing revealed a reduction in heat-affected zone (HAZ) hardness due to thermal softening induced by anodizing, while FZ hardness peaked under optimized welding conditions, reaching a maximum value of 95.66 HV. Tensile testing indicated that anodized films enhance the yield strength (YS) of the fusion zone (FZ) but may reduce ductility. Under optimized welding conditions (4 m/min, 0 mm), the joints exhibited the best overall performance, achieving the YS of 125.28 ± 10.57 MPa, an ultimate tensile strength (UTS) of 193.18 ± 3.66 MPa, and an elongation of 3.46 ± 0.25%. These findings provide valuable insights for optimizing both anodizing and welding parameters to improve the mechanical properties of A356 joints. Full article

Recent global efforts to produce lightweight electrified vehicles have motivated the push toward advanced lightweight materials which led to the creation of novel alloys optimized for use in high-pressure die casting (HPDC). HPDC enables the fabrication of near-net-shape automotive parts, significantly reducing or eliminating additional machining steps. A key feature of HPDC is the extremely fast cooling that forces the alloy to solidify within only a few seconds. Because of these rapid cooling conditions, it becomes essential to accurately evaluate the thermophysical behavior of newly designed lightweight alloys during severe quenching. Precisely quantifying these material properties is crucial for properly controlling HPDC operations and for building reliable numerical models that simulate filling and solidification. The thermophysical characteristics of such alloys vary markedly with temperature, especially when the material undergoes the fast solidification typical of HPDC. Therefore, understanding how these properties change with temperature during intense cooling becomes a critical requirement in alloy development. To address this need, a dedicated experimental system was designed to solidify molten metal samples under controlled and variable cooling conditions by applying multiple impinging water jets. An inverse heat-transfer algorithm was formulated to extract temperature-dependent thermal conductivity and diffusivity of the alloy as it solidifies under rapid cooling. To verify the reliability of both the inverse model and the measurements, experiments were performed using pure Tin, a reference material with well-documented thermophysical properties. The computed thermophysical properties of Tin were benchmarked against values reported in the literature and demonstrated reasonable consistency, with a maximum deviation of 13.6%. Full article

This study investigates the melt-flow behavior of the AlSi₉Cu₃ alloy during high-pressure die casting using a combined experimental and numerical approach. A transparent die and a high-speed camera were used to capture the transient motion of the melt front, while a validated computational model reproduced the filling dynamics under identical boundary conditions. The influence of the gating-system geometry—particularly the gate thickness, flow-path length, and inlet cross-section—was analyzed with respect to filling velocity, filling time, and flow stability. To quantify hydraulic losses that arise in practical die-casting conditions, an empirical correction coefficient k₂ was introduced. Its value was obtained by regression analysis based on ten repeated measurements of filling time for each configuration. The deviation between the simulated and experimental velocities did not exceed 5%, demonstrating the reliability of the numerical model within the tested parameter range. The results show that the optimized gating design reduces flow instability, suppresses air entrapment zones, and yields a more uniform velocity distribution across the cavity. The empirical relations derived involving k₂ provide a practical tool for preliminary design of gating systems, enabling faster optimization without extensive trial-and-error procedures. The methodology presented in this work offers a validated basis for improving gating-system performance in high-pressure die casting of aluminum alloys. Full article

Large functionally integrated casting and electrification are rapidly changing the high-pressure die-casting industry. The requirements for these new castings differ from those of the previous ones. Load-bearing capability, fatigue, ductility, and crashworthiness all increase, and the foundry’s readiness for this varies and is challenging. At the same time, the carbon footprint needs to be reduced, meaning that recycled, secondary aluminium usage is required, making the challenge of attaining the required component performance significantly more difficult. The current paper examined the conditions and requirements to manage and reach the required targets, both from a material standpoint as well as from a climate impact and resource-efficiency perspective. Full article

Al–Si–Cu alloy is one of the aluminum die-cast alloys widely used in industry. Due to the presence of Si and Cu elements in the Al–Si–Cu alloy, the corrosion resistance of the Al–Si–Cu alloy is lowered. Thus, developing a corrosion-resistant film on the Al–Si–Cu alloy is necessary. A layered double hydroxide (LDH) film is recognized as a promising corrosion-resistant coating. LDHs exhibit a distinct structure where positively charged basic layers (metal hydroxides) are interleaved with intermediate layers that accommodate charge-compensating anions and hydration water. The positively charged layers allow for the exchange of anions as interlayers, enabling the incorporation of various anions into the interlayer. The difference in the anion species in the interlayer of the LDH films can affect corrosion-resistant performance. In this study, we aimed to prepare Mg–Al LDH films intercalated with different anions (NO₃⁻, MoO₄²⁻, VO₄³⁻, and PO₄³⁻) and investigate the corrosion resistance of the LDH films. The films were prepared on die-cast Al–Si–Cu alloys using steam coating and immersion processes. The prepared LDH films were characterized by XRD, SEM, FT-IR, and electrochemical measurements. The electrochemical measurements revealed that Mg–Al LDH films intercalated with MoO₄²⁻ showed the most superior corrosion resistance among all films prepared in this study. Full article

Acquiring reliable thermal data during the high-pressure die-casting (HPDC) process remains a significant challenge due to its complexity and rapidly evolving thermal environment. In industrial settings, the influence of process parameters is typically evaluated after solidification by examining the final casting quality, as direct temperature measurements within the die during operation are difficult to obtain. Additionally, most casting simulation tools lack accurate correlations for the interfacial heat transfer coefficient (IHTC) as a function of process parameters. To address this limitation, a laboratory-scale hot chamber die-casting (HCDC) apparatus was developed to replicate the fluid flow and the thermal conditions of industrial HPDC operation while enabling direct thermal measurements inside the die cavity using embedded thermocouples. The molten metal temperature was estimated using the lumped capacitance method, and the IHTC was determined through a custom inverse heat conduction algorithm incorporating an adaptive forward time-stepping scheme. This algorithm was validated by solving the forward heat conduction problem using the ANSYS 2025 R1 Transient Thermal solver. The experimentally obtained IHTC values showed good agreement with those measured during industrial HPDC trials, with a maximum deviation of about 14% in the peak value, while the full width at half maximum (FWHM) differed by less than 12%. These results confirm that the developed HCDC setup can reliably reproduce industrial thermal conditions and generate high-quality thermal data that can be used in numerical casting simulations. Full article

The significant increase in the importance of silumin recycling in the context of sustainable development is driven by tangible ecological and economic benefits. However, the primary technological challenge associated with using scrap is the accumulation of iron, which promotes the formation of undesirable sludge particles, degrading the alloy’s mechanical properties. This paper presents a description of the phase transformations in an AlSi7Mg alloy with increased iron and manganese content. Analysis of data from Differential Scanning Calorimetry (DSC) revealed the primary crystallisation of sludge particles (SP) and the pre-eutectic precipitation of the α-Al₁₅(Fe,Mn)₃Si₂ phase, which replaced the β-Al₅FeSi phase. The remaining constituents of the AlSi7Mg alloy structure—α(Al) solid solution dendrites, the α(Al)+β(Si) eutectic, and the Mg₂Si phase—crystallise regardless of the iron, manganese, and chromium content. It was established that the increase in the crystallisation temperature of SP, rich mainly in the elements mentioned above, is directly proportional to the increase in the value of the sludge factor (SF) and ranges from 620 °C (for SF~1.3%) to approx. 645 °C (for SF~3.1%). SEM studies revealed that the combined increase in iron and manganese content not only raises the precipitation temperature of SP but also alters its morphology from single polyhedra to compact, “cluster-like” structures. To avoid the presence of sludge particles in the AlSi7Mg alloy, which have an unfavourable morphology and reduce the yield of the melting process, the SF for high-pressure die-casting should not exceed 2.0%. Full article

RT-DETR (Real-Time DEtection TRansformer) has recently emerged as a promising model for object detection in images, yet its performance on small objects remains limited, particularly in terms of robustness. While various approaches have been explored, developing effective solutions for reliable small object detection remains a significant challenge. This paper introduces an adapted variant of RT-DETR, specifically designed to enhance robustness in small object detection. The model was first designed on one dataset and subsequently transferred to others to validate generalization. Key contributions include replacing components of the feed-forward neural network (FFNN) within a hybrid encoder with Hebbian, randomized, and Oja-inspired layers; introducing a modified loss function; and applying multi-scale feature fusion with fuzzy attention to refine encoder representations. The proposed model is evaluated on the Al-Cast Detection X-ray dataset, which contains small components from high-pressure die-casting machines, and the PCB quality inspection dataset, which features tiny hole anomalies. The results show that the optimized model achieves an mAP of 0.513 for small objects—an improvement from the 0.389 of the baseline RT-DETR model on the Al-Cast dataset—confirming its effectiveness. In addition, this paper contributes a mini-literature review of recent RT-DETR enhancements, situating our work within current research trends and providing context for future development. Full article