Search Results (74)

Sanicro 25 (X7NiCrWCuCoNb25-23-3-3-2) steel is specifically designed for use in superheater components within the latest generation of conventional power plants. These power plants operate under conditions often referred to as super-ultra-supercritical, with steam parameters that can reach up to 30 MPa and temperatures of 653 °C for fresh steam and 672 °C for reheated steam. While last-generation supercritical power plants still rely on fossil fuels, they represent a significant step forward in more sustainable energy production. The most sophisticated facilities of this kind can achieve thermodynamic efficiencies exceeding 47%. This study aimed to conduct a detailed analysis of the initial precipitation processes occurring in Sanicro 25 steel within the temperature range of 700–900 °C. The temperature of 700 °C corresponds to the operational conditions of this material, particularly in secondary steam superheaters in thermal power plants that operate under ultra-supercritical parameters. Understanding precipitation processes is crucial for optimizing mechanical performance, particularly in terms of long-term strength and creep resistance. To accurately assess the microstructural changes that occur during the early stages of service, a digital twin approach was employed, which included CALPHAD simulations and experimental heat treatments. Experimental annealing tests were conducted in air within the temperature range of 700–900 °C. Precipitation behavior was simulated using the Thermo-Calc 2025a with Dictra software package. The results from Prisma simulations correlated well with the experimental data related to the kinetics of phase transformations; however, it was noted that the predicted sizes of the precipitates were generally smaller than those observed in experiments. Additionally, computational limitations were encountered during some simulations due to the complexity arising from the numerous alloying elements present in Sanicro 25 steel. The microstructural evolution was investigated using various methods, including light microscopy (LM), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Full article

Additive Manufacturing (AM) techniques, such as Fused Deposition Modeling (FDM) and Stereolithography (SLA), are increasingly adopted in various high-demand sectors, including the aerospace, biomedical engineering, and automotive industries, due to their design flexibility and material adaptability. However, the tribological performance and surface integrity of parts manufactured by AM are the biggest functional deployment challenges, especially in wear susceptibility or load-carrying applications. The current review provides a comprehensive overview of the tribological challenges and surface engineering solutions inherent in FDM and SLA processes. The overview begins with a comparative overview of material systems, process mechanics, and failure modes, highlighting prevalent wear mechanisms, such as abrasion, adhesion, fatigue, and delamination. The effect of influential factors (layer thickness, raster direction, infill density, resin curing) on wear behavior and surface integrity is critically evaluated. Novel post-processing techniques, such as vapor smoothing, thermal annealing, laser polishing, and thin-film coating, are discussed for their potential to endow surface durability and reduce friction coefficients. Hybrid manufacturing potential, where subtractive operations (e.g., rolling, peening) are integrated with AM, is highlighted as a path to functionally graded, high-performance surfaces. Further, the review highlights the growing use of finite element modeling, digital twins, and machine learning algorithms for predictive control of tribological performance at AM parts. Through material-level innovations, process optimization, and surface treatment techniques integration, the article provides actionable guidelines for researchers and engineers aiming at performance improvement of FDM and SLA-manufactured parts. Future directions, such as smart tribological, sustainable materials, and AI-based process design, are highlighted to drive the transition of AM from prototyping to end-use applications in high-demand industries. Full article

Accurate measurement uncertainty quantification and its propagation are critical for dimensional compliance in precision manufacturing. This study presents a novel framework that examines the evolution of measurement error along the axial length of CNC-turned components, focusing on spatial and material-specific factors. A systematic experimental comparison was conducted between a manual Digital Vernier Caliper (DVC) and an automated Coordinate Measuring Machine (CMM) using five engineering materials: Aluminum Alloy 6061, Brass C26000, Bronze C51000, Carbon Steel 1020 Annealed, and Stainless Steel 304 Annealed. Dimensional measurements were taken from five consecutive machining zones to capture localized metrological behaviors. The results indicated that the CMM consistently achieved lower expanded uncertainty (as low as 0.00166 mm) and minimal propagated uncertainties (≤0.0038 mm), regardless of material hardness or cutting position. In contrast, the DVC demonstrated significantly higher uncertainty (up to 0.03333 mm) and propagated errors exceeding 0.035 mm, particularly in harder materials and unsupported zones affected by surface degradation and fixture variability. Root-sum-square (RSS) modeling confirmed that manual measurements are more prone to operator-induced error amplification. While the DVC sometimes recorded lower absolute errors, its substantial uncertainty margins hampered measurement reliability. To statistically validate these findings, a two-way ANOVA was performed, confirming that both the measurement system and machining zone significantly impacted uncertainty, as well as their interaction. These results emphasize the importance of material-informed and zone-sensitive metrology, highlighting the advantages of automated systems in sustaining measurement repeatability and dimensional stability in high-precision applications. Full article

Green data centers (GDCs) are increasingly deployed worldwide to power digital infrastructure sustainably. These centers integrate renewable energy sources, such as solar and wind, to reduce dependence on grid electricity and lower operational costs. When distributed geographically, GDCs face considerable challenges due to spatial variations in renewable energy availability, electricity pricing, and bandwidth costs. This paper addresses the joint optimization of operational cost and service quality for delay-sensitive applications scheduled across distributed green data centers (GDDCs). We formulate a multi-objective optimization problem that minimizes total operational costs while reducing the Average Task Loss Probability (ATLP), a key Quality of Service (QoS) metric. To solve this, we propose an Adaptive Firefly-Based Bi-Objective Optimization (AFBO) algorithm that introduces multiple adaptive mechanisms to improve convergence and diversity. The minimum Manhattan distance method is adopted to select a representative knee solution from each algorithm’s Pareto front, determining optimal task service rates and ISP task splits into each time slot. AFBO is evaluated using real-world trace-driven simulations and compared against benchmark multi-objective algorithms, including multi-objective particle swarm optimization (MOPSO), simulated annealing-based bi-objective differential evolution (SBDE), and the baseline Multi-Objective Firefly Algorithm (MOFA). The results show that AFBO achieves up to 64-fold reductions in operational cost and produces an extremely low ATLP value ($1.875\times {10}^{-7}$) that is nearly two orders of magnitude lower than SBDE and MOFA and several orders better than MOPSO. These findings confirm AFBO’s superior capability to balance energy cost savings and Quality of Service (QoS), outperforming existing methods in both solution quality and convergence speed. Full article

The development of holography has facilitated significant advancements across a wide range of disciplines. A phase-only spatial light modulator (SLM) plays a crucial role in realizing digital holography, typically requiring a phase mask as its input. Non-iterative (NI) algorithms are widely used for phase mask generation, yet they often fall short in delivering precise solutions and lack adaptability in complex scenarios. In contrast, the Simulated Annealing (SA) algorithm provides a global optimization approach capable of addressing these limitations. This study investigates the integration of NI algorithms with the SA algorithm to enhance the optimization of phase mask generation in digital holography. Furthermore, we examine how adjusting annealing parameters, especially the cooling strategy, can significantly improve system optimization performance and symmetry. Notably, we observe a considerable improvement in the efficiency of the SA algorithm when non-iterative methods are employed to generate the initial phase mask. Our method achieves a perfect representation of the symmetry in desired light fields. The efficacy of the optimized phase masks is evaluated through optical tomographic measurements using two-dimensional mutually unbiased bases (MUBs), with the resulting average similarity reaching 0.99. These findings validate the effectiveness of our methodin optimizing phase mask generation and underscore its potential for high-precision optical mode recognition and analysis. Full article

Tourism refers to the activity of traveling for pleasure, recreation, or leisure purposes. It encompasses a wide range of activities and experiences, from sightseeing to cultural exploration. In today’s digital age, tourists often organize their excursions independently by utilizing information available on websites. However, due to constraints in designing customized tour routes such as travel time and budget, many still require assistance with vacation planning to optimize their experiences. Therefore, this paper proposes an algorithm for personalized tourism planning that considers tourists’ preferences. For instance, the algorithm can recommend places to visit and suggest activities based on tourist requirements. The proposed algorithm utilizes an extended model of the team orienteering problem with time windows (TOPTW) to account for mandatory locations and activities at each site. It offers trip planning that includes a set of locations and activities designed to maximize the overall score accumulated from visiting these locations. To solve the proposed model, the Adaptive Neighborhood Simulated Annealing (ANSA) algorithm is applied. ANSA is an enhanced version of the well-known Simulated Annealing algorithm (SA), providing an adaptive mechanism to manage the probability of selecting neighborhood moves during the SA search process. The computational results demonstrate that ANSA performs well in solving benchmark problems. Furthermore, a real-world attractive location in Tak Province, Thailand, is used as the case study in this paper to illustrate the effectiveness of the proposed model. Full article

Foodborne viruses are significant contributors to global food safety incidents, posing a serious burden on human health and food safety. In this study, a multiplex reverse transcription–droplet digital PCR (RT-ddPCR) assay based on the MS2 phage as a process control virus (PCV) was developed to achieve the simultaneous detection of hepatitis A virus (HAV) and hepatitis E virus (HEV) in bivalve shellfish. By optimizing the reaction system and procedures, the best reaction conditions were selected, and the specificity, sensitivity, and reproducibility of the method were assessed. Additionally, the MS2 phage’s recovery rate was utilized as an indicator to evaluate the optimal sample nucleic acid enrichment method. The results indicated that the RT-ddPCR assay exhibited optimal amplification efficiency with primer concentrations of 900 nmol/L, probe concentrations of 350 nmol/L for HAV and HEV, and 500 nmol/L for MS2, an annealing temperature of 53.1 °C, an extension time of 90 s, and 45 cycles. Additionally, the developed multiplex RT-ddPCR assay demonstrated high specificity, with quantitation limits of 12.6, 8.9, and 7.8 copies/reaction being observed for HAV, HEV, and the MS2 phage, respectively. A total of 240 bivalve samples were analyzed, of which 4 were positive for HAV and 12 for HEV. The viral loads for HAV ranged from 3048 to 6528 copies/2 g, while those for HEV ranged from 3312 to 20,350 copies/2 g. This assay provides a vital tool for enhancing food safety monitoring. Full article

The present study focuses on advancing one of the most popular AM techniques, namely, laser powder bed fusion (LPBF) technology, which has the ability to produce complex geometry parts with minimum material waste but continues to face challenges in minimizing the surface roughness. For this purpose, a novel hybrid manufacturing technology, which applies in a single setup (in-envelope) both LPBF technology and high-speed machining, was examined in this research for the fabrication of tensile specimens with three different surface finish conditions: as-built, hybrid (in-envelope machining) and post-machining (out-of-envelope) on Inconel^® alloy 718, hereafter referred to as IN718. As the application of the IN718 alloy in service is typically specified in the precipitation-hardened condition, three different heat treatments were applied to the tensile specimens based on the most promising thermal cycles identified previously for room-temperature tensile properties by the authors. The as-built (AB) specimens had the highest average surface roughness (R_a) of 5.1 μm ± 1.6 μm, which was a significant improvement (five-fold) on the hybrid (1.0 μm ± 0.2 μm) and post-machined (0.8 μm ± 0.5 μm) surfaces. The influence of this surface roughness on the mechanical properties was studied both at ambient temperature and at 650 °C, which is close to the maximum service temperature of this alloy. Regardless of the surface conditions, the room-temperature mechanical properties of the as-fabricated IN718 specimens were within the range of properties reported for standard wrought IN718 in the annealed condition. Nonetheless, detailed examination of the strain localization behavior during tensile testing using digital image correlation showed that the IN718 specimens with AB surfaces exhibited lower ductility (global and local) relative to the hybrid and post-machined ones, most likely due to the higher surface roughness and near-surface porosity in the former. At 650 °C, even though the mechanical properties of all the heat-treated IN718 specimens surpassed the minimum specifications for the wrought precipitation-hardened IN718, the AB surface condition showed up to 4% lower strength and 33–50% lower ductility compared with the hybrid and PM surface conditions. Microfocus X-ray computed tomography (µXCT) of the fractured specimens revealed the presence of numerous open cracks on the AB surface and a predisposition for the near-surface pores to accelerate rupture, leading to premature failure at lower strains. Full article

Heterostructured materials consist of heterogeneous zones with dramatic variations in mechanical properties, and have attracted extensive attention due to their superior performance. Various heterostructured materials have been widely investigated in recent years. In the present study, a combination of two different types of heterogeneous structures, a surface bimodal structure and gradient structure, was designed using the traditional surface mechanical attrition treatment (SMAT) method in pure copper, and the mechanical properties and microstructural evolution of dual-heterostructure Cu were studied in depth. In total, 100 stainless steel balls with a diameter of 6 mm were utilized to impact the specimen surface at room temperature for a short period of time. In this work, the sample surface was divided into hard areas and soft areas, along with a roughly 90 μm gradient structure in the cross-sectional direction after 30 s of SMAT processing. After the partial SMAT processing, lasting 30 s, the strength increased to 158.0 MPa and a considerable ductility of 25.7% was sustained, which overcomes the strength–ductility trade-off. The loading–unloading–reloading (LUR) test was utilized to measure the HDI stress, and the result showed that the HDI stress of the partial SMAT sample was much higher than the annealed one, especially for the Cu-SMAT-30S specimen, the strength of which increased from 80.4 MPa to 153.8 MPa during the tensile test. An in situ digital image correlation (DIC) investigation demonstrated that the strain developed stably in the Cu-SMAT-10S specimen. Furthermore, electron backscatter diffraction (EBSD) was carried out to study the microstructural evolution after partial SMAT processing; the KAM value increased to 0.34 for the Cu-SMAT-10S specimen. This research provides insights for the effective combination of superior strength and good ductility in dual-heterostructure materials. Full article

This paper is focused on improving material formability in hybrid wire-arc additive manufacturing comprising metal forming stages to produce small-to-medium batches of customized parts. The methodology involves fabricating wire arc additive manufactured AISI 316L stainless steel parts subjected to mechanical and thermal processing (MTP), followed by microhardness measurements, tensile testing with digital image correlation, as well as microstructure and microscopic observations. Results show that mechanical processing by pre-straining followed by thermal processing by annealing can reduce material hardness and strength, increase ductility, and eliminate anisotropy by recrystallizing the as-built dendritic-based columnar grain microstructure into an equiaxed grain microstructure. Full article

In the present study, microstructure and cyclic tensile tests were used to measure the superelastic responses of Fe_40.95Ni₂₈Co₁₇Al_11.5Ta_2.5B_0.05 (at.%) shape memory alloys after 97% cold rolling. Cold-rolled samples underwent annealing heat treatment (1250 °C/1 h) followed by quenching in water or aging heat treatment (700 °C/6 h and 700 °C/12 h) followed by quenching in water. The microstructure results showed that the average grain size increased from 210 μm to 1570 μm as annealing times increased from 0.5 h to 1 h. X-ray diffraction (XRD) spectra for FeNiCoAlTaB (NCATB) showed that in cold-rolled alloys after solution, the strong peak was in the face-centered cubic (γ, FCC) <111> structure. In aged samples, a new peak (γ’, FCC) emerged, the intensity of which increased as aging times rose from 6 to 12 h. Transmission electron microscope (TEM) images showed that the average precipitate size was around 10 nm in 700 °C/6 h specimens and 18 nm in 700 °C/12 h specimens. The precipitate was enriched in Ni, Al, and Ta elements and exhibited an L1₂ crystal structure. Tensile samples aged at 700 °C for 6 and 12 h exhibited recoverable strains of 1% and 2.6%, respectively, at room temperature. Digital image correlation (DIC) results for the sample aged at 700 °C for 12 h showed that two martensite variants were activated during the superelastic test. Such variants can form corresponding variant pairs (CVPs), which promote tensile deformation. The tensile sample exhibited a gradual cyclic degradation, and a large irrecoverable strain was observed after the test. This irrecoverable strain was the result of residual martensite, which was pinned by dislocations. Full article

The relationship between estimating traffic flow and evaluating pollutant emissions lies in understanding how vehicular traffic patterns affect air quality. Traffic flow estimation is a complex field that involves a variety of analytical techniques to understand, predict, and manage the flow of vehicles on road networks. Different types of analyses commonly employed in this area are statistical analysis (e.g., descriptive statistics, inferential statistics, time series analysis), mathematical modeling (macroscopic models, microscopic models, mesoscopic models), computational methods (e.g., simulation modeling, machine learning, and AI techniques), geospatial analysis (e.g., geographic information systems (GISs), spatial data analysis), network analysis (e.g., graph theory and network flow models). In sensor network setups, the strategic placement of sensors is crucial, primarily due to the challenges posed by limited energy supplies, restricted storage capabilities, and the demands on processing and communication, all of which significantly impact maintenance costs and hardware limitations. To mitigate the burden on processing and communication, it is essential to deploy a limited number of sensors strategically. In practical applications, achieving an optimal layout of physical sensors (i.e., placing sensors within the network in such a way as to meet a specific optimality criterion, such as identifying the minimum number of sensors required to ensure the ability to design reliable state observers capable of reconstructing the network’s state based on the available data) is essential for the accurate monitoring of large-scale systems, including traffic flow or the distribution networks of water and gas. In the context of traffic systems, addressing the challenge of full link flow observability, that is, the ability to accurately monitor and assess the flow of entities (i.e., vehicles) across all the links or pathways within a network, entails selecting the smallest number of traffic sensors from a larger set to install. The goal is to choose a subset of p sensors, which may include redundancies, from a pool of $n>>p$ potential sensors. This is conducted to maintain the structural observability of the entire traffic network. This concept pertains to deducing the complete internal state (traffic volume on each road link in the network) from external outputs and inputs (measurements from sensors). The traditional concept of system observability serves as a criterion for sensor placement. This article presents the development of a simulated annealing heuristic to address the selection problem. The selected sensors are then applied to construct a Luenberger observer, a mathematical construct used in control theory to accurately estimate the internal state of a dynamic system based on its inputs and outputs. Numerical simulations are carried out to demonstrate the effectiveness of this method, and a performance analysis using a digital twin of a transport network, designed using the Aimsun Next software, are also carried out to assess traffic flow and associated pollutant emissions. In particular, we examine a traffic network comprising 21 roads. We address the sensor selection problem by identifying an optimal set of six sensors, which facilitates the design of a Luenberger observer. This observer enables the reconstruction of traffic flow across the network with minimal estimation error. Furthermore, by integrating this observer with data from the Aimsun Next software, we assess the pollutant emissions related to traffic flow. The results indicate a high accuracy in estimating pollutant levels. Full article

In this study, the fabrication of microfluidic chips through the bonding of poly (methyl methacrylate) (PMMA) boards featuring designed patterns to create a three-dimensional sandwich structure with embedded microchannels was explored. A key focus was optimization of the interface quality of bonded PMMA pairs by adjusting the solvent, such as such as acetone, alcohol, and their mixture. Annealing was conducted below 50 °C to leverage the advantages of low-temperature bonding. Because of the differences in the chemical reactivity of PMMA toward acetone, alcohol, and their combinations, the resulting defect densities at the bonding interfaces differed significantly under low-temperature annealing conditions. To achieve the optimal sealing integrity, bonding pressures of 30 N, 40 N, and 50 N were evaluated. The interface was analyzed through microstructural examination via optical microscopy and stress measurements were determined using digital photoelasticity, while the bonding strength was assessed through tensile testing. Full article

Aiming at the problems of low detection efficiency and complexity of aircraft gear tooth surfaces, a path optimization algorithm based on an improved genetic algorithm is proposed. The detection area of the tooth surface is planned, the sampling points of the tooth surface are determined based on the digital technology of the tooth surface, and the sampling mesh is obtained by the truncated plane method to reduce the sampling distortion of the shape and improve the sampling efficiency. Adaptive crossover and mutation probability are used to improve the convergence speed and accuracy of the genetic algorithm. The selected individuals of the binary tournament are used to guide the global optimal search by a simulated annealing algorithm, and the local optimal is avoided by the Metropolis criterion. In the simulation experiment, the proposed method and other algorithms are used to optimize the detection path. The optimized tooth-surface-detection path has the shortest distance and the shortest time, that is, the tooth-surface-detection path efficiency is improved, verifying the practicability of the algorithm. Full article

Digital twins have emerged as a powerful concept for real-time monitoring and analysis, facilitating Quality by Design integration into biopharmaceutical manufacturing. Traditionally, lyophilization processes are developed through trial-and-error, incorporating high security margins and inflexible process set points. Digital twins enable the integration of adaptable operating conditions and implementation of automation through Advanced Process Control (APC) with Process Analytical Technology (PAT) and validated physicochemical models that rely on heat and mass transfer principles, allowing us to overcome the challenges imposed by the lyophilization process. In this study, a digital twin for freeze-drying processes is developed and experimentally validated. Using the digital twin, primary drying conditions were optimized for controlled nucleation and annealing methods by carrying out a few laboratory tests beforehand. By incorporating PAT and modeling, the digital twin accurately predicts the product’s temperature and drying endpoint, showing smaller errors than the experiments. The digital twin significantly increases productivity by up to 300% while reducing the costs by 74% and the Global Warming Potential by 64%. Full article