Search Results (13,575)

Brownfields are abundant, widely dispersed, and subject to complex contamination, resulting in waste land, ecological degradation, and barriers to economic growth. The accurate identification of brownfield sites is key to formulating effective remediation and reuse strategies. However, the heterogeneity of surface features poses significant challenges for identifying various types of brownfields across entire urban areas. To address these challenges, this study proposes a “Target–Classification–Modification” (TCM) method for brownfield identification, which was applied to Tangshan City, China. This method consists of a three-stage process: target area localization, visual interpretation and classification, and site-level modification. It leverages integrated multi-source open-access data and clear rules for subtype classification and the determination of spatial boundaries and abandonment status. The results for Tangshan show that (1) the overall accuracy of the TCM method reached 84.9%; (2) a total of 1706 brownfield sites were identified, including 422 raw-material mining sites, 576 raw-material manufacturing sites, and 708 non-raw-material manufacturing sites; (3) subtype analysis revealed distinct spatial distribution and morphological patterns, driven by resource endowments, transportation networks, and industrial space organization. The TCM method improved the identification efficiency by 34.7% through precise target-area localization. It offers well-defined criteria to distinguish different brownfield subtypes. In addition, it employs a multi-approach strategy to determine the abandonment status, further enhancing accuracy. This method is scalable and widely applicable, providing support for urban-scale brownfield research and practice. Full article

This article explores the use of ultrasonic nondestructive evaluation for qualification of laser-DED IN718 samples. The main goal of this article is to identify potential ultrasonic parameters which have highest sensitivity to microstructral changes that result from fabrication of DED samples. The ultrasonic qualification parameters were extracted from ultrasonic testing including velocity and attenuation measurement, and C-Scan imaging. These measurements were further used to extract parameters that quantify the anisotropy, microstructural heterogeneity, and grain scattering. Two laser-DED IN718 samples fabricated with slightly different processing parameters were evaluated to observe the influence of the laser power and scan speed on the qualification parameters. The identified qualification parameters were compared for these two samples, along with a hot-rolled sample that was also used as reference. The results suggest that the anisotropy, attenuation, and heterogeneity were highest in the DED samples compared to the reference sample. The identified qualification parameters seem to capture these changes, suggesting they could be potentially used for qualification of AM parts. Full article

The subject of the current paper is cooling heat sinks using the TPMS structure. An experiment was conducted using water and a mixture of 10% vol. ethylene glycol in water, which was used to cool heat sinks in the presence of the TPMS structure. The gyroid was developed using 3D printing with three different porosities: 0.7, 0.8, and 0.9, respectively. The shell network is a single domain, and fluid is circulated at various flow rates. A comparison with the numerical model, as simulated using COMSOL software (version 6.2), showed good agreement. A uniform temperature distribution is a clear indication of uniform cooling. Then, the TPMS structure is changed from one domain to two unconnected domains, and a different flow rate is applied to each domain entry. This approach is unique in that it investigates the cooling of the heat sink with a two-domain structure, which has not been previously studied. The novelty of this paper lies in utilizing two TPMS structure domains to cool the heat sink. Thus, dual-domain TPMS heat sinks are implemented and optimized with separate inlets. Statistical testing of the model for the Nusselt number and the performance evaluation criterion is performed using Fisher’s statistical test to analyze variance (ANOVA). It was found that the cooling heat sink is more accurate with two-domain systems. The average Nusselt number polynomial is found to vary linearly with the two-inlet velocity, the porosity and the fluid Prandtl number. Similar linearity is found for the performance evaluation criterion. The optimum Nusselt number equals 77, the PEC equals 49 for a porosity of 0.85, and the Prandtl number is 36.9. Full article

In this paper, we propose a multi-mode switchable ultra-wideband terahertz absorber based on patterned graphene and VO₂ by designing a graphene pattern composed of a large rectangle rotated 45° in the center and four identical small rectangles in the periphery, as well as a VO₂ layer pattern composed of four identical rectangular boxes and small rectangles embedded in the dielectric layer. VO₂ can regulate conductivity via temperature, the Fermi level of graphene depends on the external voltage, and the graphene layer and VO₂ layer produce resonance responses at different frequencies, resulting in high absorption. The proposed absorption microdevices have three modes: Mode 1 (2.52–4.52 THz), Mode 2 (3.91–9.66 THz), and Mode 3 (2.14–10 THz), which are low-band absorption, high-band absorption, and ultra-wideband absorption. At 2.96 THz in Mode 1, the absorption rate reaches 99.98%; at 8.04 THz in Mode 2, the absorption rate reaches 99.76%; at 5.04 THz in Mode 3, the absorption rate reaches 99.85%; and at 8.4 THz, the absorption rate reaches 99.76%. We explain the absorption mechanism by analyzing the electric field distribution and local plasma resonance, and reveal the high-performance absorption mechanism by using the relative impedance theory. In addition, absorption microdevices have the advantages of polarization insensitivity, incident angle insensitivity, multi-mode switching, ultra-wideband absorption, large manufacturing tolerance, etc., and have potential research and application value in electromagnetic stealth devices, filters and optical switches. Full article

Innovations in additive manufacturing (AM) methods represent a significant advancement in manufacturing technology, opening new avenues for creating objects in various shapes and sizes. Fused deposition modeling (FDM) is a specialized AM technique in which computers build layers upon each other to form a complete 3D object. The feasibility of producing metal parts using these methods has been thoroughly analyzed, but the design process has yet to catch up with manufacturing capabilities. Biodegradable aliphatic polyester PLA is derived from lactic acid. To enhance its strength, PLA is combined with metal particles, resulting in versatile property improvements and applications. While the aesthetic and functional qualities of PLA–metal composite filaments are intriguing, they also present difficulties related to extrusion, equipment wear, and maintaining consistent print quality. These challenges could be mitigated, to some extent, with careful tuning and specialized hardware. However, the inferior mechanical properties of bioresorbable PLA filaments highlight the need for the development of infilled PLA filaments to improve strength and other characteristics. This review discusses the 3D printing of PLA infilled with metal particles, various materials used, and their properties as a matter of interest in AM technology. Additionally, the applications of PLA–metal composites, along with their implications, limitations, and prospects, are comprehensively examined in this article. This sets the stage for the development of high-strength, sustainable materials for use in a range of engineering and technology fields. Full article

As large-scale additive manufacturing advances, the reliable prediction of the structural behavior of FDM-printed composites is becoming increasingly important. However, existing finite element methods often oversimplify the material anisotropy introduced by the printing path. This study proposes a projection-based method that maps toolpath-defined fiber orientations directly into a finite element model to represent anisotropic mechanical behavior. The mechanical properties of printed carbon fiber-reinforced ABS were experimentally characterized in three directions (UDL, UDT, and UD10). The results confirmed strong anisotropy, with elastic moduli ranging from 3.2 to 9.8 GPa and tensile strengths from 20 to 81 MPa. The shear modulus and strength obtained from the 10° off-axis tensile tests were 1.17 GPa and 10.9 MPa, respectively. This directional data enabled the implementation of the FE model of a 20 m-long printed ship structure. The predicted mid-span deflection (2.19 mm) differed by only 5% from the experimental measurement (2.08 mm). While effective, this method may face challenges with highly irregular geometries. Nevertheless, it offers a scalable approach for the accurate simulation of FDM-printed composites. Full article

The mechanical limitations of gypsum-based composites necessitate reinforcement strategies to enhance their structural performance. This study investigates the feasibility of integrating 3D-printed polylactic acid (PLA) meshes into gypsum composites through a series of preliminary experiments. Various mesh configurations were tested, including different fiber thicknesses, mesh grid sizes, and single- and double-layer applications. The impact of mesh incorporation on bulk density, ultrasonic pulse velocity (UPV), bending strength, and compressive strength was assessed. The results indicate that the inclusion of PLA meshes had a limited effect on bulk density and led to a slight decrease in UPV values, suggesting increased porosity. Although improvements in mechanical properties were anticipated, most specimens exhibited lower bending and compressive strengths compared to the reference specimen. Among the tested configurations, 2 mm thick meshes demonstrated relatively higher performance, particularly in bending strength, with narrow-mesh aperture yielding better results. However, double-layer mesh applications consistently resulted in lower strength values. These findings highlight the challenges associated with integrating 3D-printed PLA meshes into gypsum composites. While the study provides valuable insights into mesh-based reinforcement, further investigations are required to optimize fiber–matrix interactions and enhance mechanical performance. Future research should explore alternative printing parameters, improved adhesion techniques, and hybrid reinforcement approaches to fully exploit the potential of additive manufacturing in gypsum-based composites. Full article

This study demonstrates the conversion of agricultural and industrial waste into construction materials by developing ultra-high-performance concrete using cold-bonded sesame ash and waste glass aggregates. The primary focus of this study was sustainability and waste valorization in self-curing concrete systems. This study focuses on many aspects of producing cementless concrete with superior short- and long-term properties, incorporating an innovative artificial aggregate premanufactured using sesame ash and waste glass. Prepacking technology of casting was used. A self-curing additive is used to reduce the energy required for curing. In cold-bonded aggregates (CBAs), the aggregate content ranged from 10 to 50% of the total sand volume. Polyethylene glycol was used as an internal curing agent to evaluate the mechanical properties of the concrete, including the compressive strength and tensile strength at different ages. The durability characteristics of the concrete were also analyzed in terms of its resistance to sulfates, chloride ion penetration, and performance at elevated temperatures of 300 and 600 °C. Microscopic analyses were conducted by scanning electron microscopy (SEM), thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR), and Differential Scanning Calorimetry (DSC). The results showed a significant improvement in the mechanical and durability performance, especially at 30%, which resulted in the highest compressive strength of 147.2 MPa at 90 days. This is an 11.93% increase compared with that of the reference mix. The tensile strength was also improved by 14.5% at the same replacement ratio. The mix containing 30% manufactured aggregate demonstrated the best thermal resistance, retaining the highest percentage of residual strength at both 300 °C and 600 °C, as well as superior sulfate impact resistance, with a strength reduction factor of 39.5%. When the replacement ratio was increased to 50%, the chloride penetration resistance improved significantly by 41% compared with that of the reference mix. FTIR, TGA, and DSC analyses also demonstrated enhanced silicate polymerization and increased carbonate formation, contributing to the improved chemical stability and density of the concrete matrix. Full article

Additive manufacturing (AM), or 3D printing, enables efficient fabrication of complex and customized components. Despite its growth across industries, users frequently encounter print failures due to design errors, process limitations, and inadequate monitoring. While existing research has explored various aspects of these failures, much of it remains fragmented, with limited consolidated overviews that map common problems, troubleshooting strategies, and guidelines across the AM workflow. This study conducted a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to identify and categorize common 3D printing problems and their solutions. Relevant studies published between 2000 and 2024 were extracted from major databases. A total of 126 peer-reviewed articles were selected and analyzed. Three major categories of recurring challenges were identified: (1) design and pre-processing errors; (2) geometric errors and dimensional deviations; (3) failures in in-process error detection and response. A variety of mitigation strategies have been proposed across the literature, including STL and slicing optimization, thermal management, machine calibration, and sensor-based real-time monitoring. These approaches reflect the multifactorial nature of 3D printing failures, which often arise from the complex interplay of design, material, and process parameters. This review provides a structured summary of failure types and mitigation strategies across the AM workflow. Full article

Tungsten heavy alloys (WHAs) are two-phase composites known for their exceptional density, strength, hardness, and ductility, making them ideal for radiation shielding, kinetic energy penetrators, and aerospace components. Due to their high melting point, WHAs are primarily processed via powder metallurgy, with liquid-phase sintering (LPS). Spark plasma sintering (SPS) and microwave sintering are emerging as advanced consolidation techniques. Recent research has focused on improving WHA performance through microstructural manipulation, alloying with elements like Fe, Co, Mo, and Re; rare earth oxides like Y₂O₃, La₂O₃, and Ce₂O₃; and employing high-entropy alloys (HEAs) as matrix phase. Additionally, additive manufacturing (AM) techniques are increasingly being used to fabricate complex WHA components. Despite their advantages, WHAs still exhibit limitations in penetration performance, primarily due to their tendency to form mushroom-like heads upon impact rather than self-sharpening. Ongoing research seeks to enhance shear localization, refine grain structure, and optimize processing methods to improve the mechanical properties and impact resistance of WHAs. Furthermore, modeling and simulation approaches are being explored to understand the mechanical behavior of WHAs. This review comprehensively overviews the above aspects and presents recent advances in WHA processing. Full article

The Iberian Pyrite Belt (IPB) contains the world’s largest massive sulfide deposit, and, due to extensive mining developed during the last 200 years, large amounts of mining waste have been abandoned in this area, with roasted pyrite ash being the focus of this study. Polymetallic mining is also classified as a NORM (naturally occurring radioactive material) activity, thus the main objective of this work was to develop a radiological and physicochemical characterization of this waste (mineral phases, elemental and radionuclide concentrations) in order to perform a valorization diagnosis of this material. The composition of this waste strongly depends on its origin (mine), and is mainly formed by iron oxides (hematite, Fe₂O₃) and heavy metals and metalloids such as As, Pb, Zn, and Cu, in levels 2–4 orders of magnitude higher than those of undisturbed soils, depending on each particular element. However, the average natural radionuclide levels are similar to those of unperturbed soils (around 30 Bqkg⁻¹ of ²³⁸U-series, 50 Bqkg⁻¹ of ²³²Th, and 70 Bqkg⁻¹ for ⁴⁰K), thus they are below the limits established by European Union regulations to require radiological control during their future valorization. As the main potential applications of roasted pyrite ash, the valorization diagnosis indicates that it can be used as a source of Fe (FeCl₃ or FeSO₄), or an additive in the manufacturing of cements, pigments, etc. Full article

Compliant mechanisms are extensively utilized in precise positioning systems. This work presents a novel compliant fine Z-positioner for directing the indenter in a nanoindentation testing positioning system. Initially, the suggested positioner consists of a novel hybrid symmetric compliant displacement amplifier of four-lever and Scott Russell structures combined with a parallel guiding mechanism. Subsequently, a static–dynamic characteristic of the proposed positioner is modeled by the pseudo-rigid body method and the Lagrange technique. Based on the FEA results, the parasitic motion error of the developed fine Z-positioner was 0.0956%. Thirdly, the analytical result was verified by FEA analysis, and the error between the two methods was 0.5869%. Therefore, the proposed analytical approach was reliable for quickly assessing the output response of the proposed positioner. Finally, to enhance the quality of the proposed structure’s response, the main design variables of the fine Z-positioner are optimized using the Firefly algorithm. The optimal findings indicated that the first natural frequency occurs at around 220.16 Hz. The imprecision between the optimal result and the FEA result was 9.67%. The analytical results are in close agreement with the confirmed FEA result. The prototype was manufactured by the computerized numerical milling method. The inexactness between the FEA outcome and the experimentation outcome was 11.04%. Based on the FEA and experiment results, displacement amplification proportions were 6.8725 and 8, respectively. In addition, the experimental results demonstrated a good linear relationship for guiding mechanisms in nanoindentation testing positioning systems. Full article

Additive manufacturing (AM) presents significant opportunities for advancing sustainability through optimized process control and material utilization. This research investigates the application of machine learning (ML) models to directly associate AM process parameters with sustainability metrics, which is often a challenge by experimental methods alone. Initially, experimental data are generated by systematically varying key AM parameters, layer height, infill density, infill pattern, build orientation, and number of shells. Subsequently, four ML models, Linear Regression, Decision Trees, Random Forest, and Gradient Boosting, are trained and evaluated. Hyperparameter tuning is conducted using the Limited-memory Broyden–Fletcher–Goldfarb–Shanno with Box constraints (L-BFGS-B) algorithm, which demonstrates the superior computational efficiency compared to traditional approaches such as grid and random search. Among the models, Random Forest yields the highest predictive accuracy and lowest mean squared error across all target sustainability indicators: energy consumption, part weight, scrap weight, and production time. The results confirm the efficacy of ML in predicting sustainability outcomes when supported by robust experimental data. This research offers a scalable and computationally efficient approach to enhancing sustainability in AM processes and contributes to data-driven decision-making in sustainable manufacturing. Full article

Metal additive manufacturing is a disruptive technology that is changing how various alloys are processed. Although this technology has several advantages over conventional manufacturing, it is still necessary to standardize its properties, which are dependent on the relative density (RD). In addition, since experimental designs are costly, one solution is using machine learning algorithms that allow the effects of variations in the processing parameters on the resulting density of the additively manufactured components to be anticipated. This work assembled a database based on data from 673 observations and 10 predictors to forecast the relative density of 316L stainless steel and AlSi10Mg components produced by laser powder bed fusion (L-PBF). LazyPredict was employed to select the algorithm that best models the variability of the inherent data. Ensemble boosting regressors offer higher accuracy, providing hyperparameter fitting and optimization advantages. The predictions’ precision for aluminum and stainless steel obtained an R² value greater than 0.86 and 0.83, respectively. The results of the SHAP values indicated that laser power and energy density are the parameters that have the greatest impact on the predictability of the relative density of Al-Si10-Mg and SS 316L materials processed by L-PBF. This study presents a compendium of data for the additive fabrication of stainless steel and aluminum alloys, offering researchers a guide to understanding how processing parameters influence RD. Full article

Decision-making in complex environments, especially sustainable ones, requires flexible methodologies to handle multiple criteria and stakeholder perspectives. This study introduces the SAPEVO-H² method (Simple Aggregation of Preferences Expressed by Ordinal Vectors—Hybrid and Hierarchical), an extensive model from the SAPEVO family, which offers multi-criteria analysis through a hierarchical structure of variables evaluated by groups partitioned into levels concerning their respective responsibilities. The proposal allows flexible analysis, considering inputs through ordinal and cardinal information. The validation of the methodology is demonstrated through a case study involving an automobile manufacturing company, which focuses on prioritizing sustainability projects based on multiple objectives aimed at minimizing polluting gas emissions. Within a hierarchical structure of five levels, the individual level results are presented. In addition, a sensitivity analysis is applied, exposing the most sensitive variables to changes concerning the highest levels. Then, we discuss the main contributions and limitations concerning the mathematical proposal and the conclusions and proposals for future work. Full article