Search Results (1,937)

In this study, we investigate the mechanical energy absorption performance of reusable 3D-printed honeycomb structures fabricated using fused deposition modeling with three thermoplastic polyurethane variants: TPU 70A, TPU 85A, and TPU 95A. Prior to manufacturing, the mechanical properties of the TPU filaments were analyzed as a function of printing temperature to optimize tensile strength and layer adhesion. Four honeycomb configurations, including hexagonal and circular cell geometries, both with and without a 30° twist, were subjected to out-of-plane compression testing to evaluate energy absorption efficiency, specific energy absorption, and crushing load efficiency. The highest energy absorption efficiency, 47%, was achieved by the hexagonal honeycomb structure fabricated from TPU 95A, surpassing the expected values for expanded polystyrene and approaching the performance reported for high-cost advanced lattice structures. Additionally, twisted honeycomb configurations exhibited improved crushing load efficiency values (up to 73.5%), indicating better stress distribution and enhanced reusability. Despite variations in absorbed energy, TPU 95A demonstrated the best balance of elasticity, structural integrity, and reusability across multiple compression cycles. These findings suggest that TPU-based honeycomb structures could provide a viable, cost-effective alternative for energy-absorbing applications in impact protection systems, automotive safety, and sports equipment. Full article

Femtosecond laser has been widely utilized in functional microstructural surfaces for applications such as anti-reflection, radiative cooling, and self-cleaning. However, achieving high-efficiency manufacturing of high-consistency functional microstructures (with feature sizes ~1 μm) over large areas remains a challenge. Here, we report a femtosecond laser temporal and spatial modulation technique for fabricating large-area anti-reflective microholes on magnesium fluoride (MgF₂) windows. The beam was transformed into a Bessel beam to extend the Rayleigh length, enabling the fabrication of microhole arrays with sub-micron precision and surface roughness variations within 10 nm over a 6 μm focal position shift range (5–11 μm). By modulating MHz burst pulses, the aspect ratio of the microholes was increased from 0.3 to 0.7 without compromising a processing speed of 10,000 holes per second. As a proof of concept, large-area anti-reflective microholes were fabricated on a 20 mm × 20 mm surface of the MgF₂ window, forming a nanoscale refractive index gradient layer and achieving a transmittance increase to over 98%. This method provides a feasible solution for the efficient and high-consistency manufacturing of functional microstructures over large areas. Full article

In this study, a novel approach was proposed for predicting the interfacial gap in copper overlap joints by using deep learning and multi-sensor fusion. In this method, an image sensor, a spectrometer, and optical sensors tomography (OCT) sensors were used to develop and validate deep learning models under various gap conditions. The results revealed that the variation in melt pool dimensions, changes in keyhole behavior, intensity variations at specific wavelengths, and keyhole depth derived from the OCT data could be used to accurately predict the interfacial gap. Among the proposed models, a binary gap classification model achieved the highest accuracy of 98.8%. The spectrometer was the most effective sensor in this study, whereas the image and OCT sensors provided complementary data. The best performance was achieved by fusing all three sensors, which emphasizes the importance of sensor fusion for precise gap prediction. This study provides valuable insights into improving weld quality assessment and optimizing manufacturing processes. Full article

Additive manufacturing (AM) of ceramics has rapidly evolved over the past decade, enabling the production of complex, high-precision components with tailored porosity and geometry. Among AM techniques, stereolithography (SLA) and digital light processing (DLP) are particularly promising for fabricating dense and functional oxide ceramics. However, the final properties of printed ceramics are strongly affected by sintering conditions, layer geometry, and microstructural uniformity. This study presents a two-stage experimental approach to evaluate the influence of sample geometry, layer thickness, and sintering schedule on the mechanical and microstructural performance of SLA-printed ceramic parts. In Stage I, the relationships between elastic modulus (E_c) and compressive strength (σ_c) were examined as a function of sample height, layer thickness (0.05 and 0.10 mm), and firing program. In Stage II, the effects of sintering temperature (1250, 1271, and 1300 °C) and holding time (2–20 min) were analyzed for the reference geometry. Microstructural characterization, including pore size distribution and quantitative porosity analysis, was conducted to establish correlations with the mechanical results (Stage III). The findings reveal that optimized sintering and geometry parameters can significantly enhance mechanical performance and reduce porosity variations. The study provides both scientific insights and engineering guidelines for improving the structural reliability of SLA-fabricated ceramic components. Full article

Static mixers are widely used devices for efficient fluid mixing, homogenization, and enhancement of heat transfer, with applications ranging from chemical processing and pharmaceutical manufacturing to wastewater treatment. This review provides a structured overview of mixing processes and the key metrics used to assess mixing quality in static mixers. Conceptual models such as dispersive versus distributive mixing and the classification into macro-, meso-, and micromixing are introduced as a basis for understanding mixing phenomena. Subsequently, a comprehensive set of quantitative measures, including G-value, residence time distribution, intensity of segregation, coefficient of variation, striation-based descriptors, Lyapunov exponent, extensional efficiency, and shear rate, is discussed in detail. Correlations and relationships among these measures are highlighted to facilitate their application in characterizing mixing quality in static mixers. By systematically summarizing the theoretical background, definitions, and interconnections of mixing quality measures, this review aims to provide researchers and engineers with a clear framework for evaluating and comparing mixing quality in static mixers, thereby supporting both academic studies and practical design considerations. Full article

Objectives: Transcatheter aortic valve implantation (TAVI) has become the standard of care for severe aortic stenosis across all surgical risk categories. Continuous innovation in prosthesis technology necessitates a comprehensive and clinically oriented analysis of contemporary TAVI systems to guide device selection and understand evolving trends. This review aims to provide a practical, device-specific decision-making framework for TAVI prosthesis selection, synthesizing the latest evidence (2023–2025) to address the challenge of individualized choice in an era of device proliferation. We conducted a detailed review of current TAVI models from leading manufacturers (Medtronic, Abbott, Boston Scientific, Biotronik, etc.), examining their technical specifications, design innovations, and data from recent international clinical trials and registries. A comparative analysis was performed based on key parameters: delivery profile, resheathability/repositionability, sealing mechanisms, hemodynamic performance, and complication rates. Modern TAVI prostheses demonstrate significant advancements. Self-expanding nitinol frames offer superior adaptability and lower profiles (as low as 14 Fr). Innovations in sealing technology have drastically reduced the incidence of moderate-to-severe paravalvular leak (PVL) to below 2–3%. Supra-annular leaflet designs provide superior hemodynamics. Clinical outcomes show excellent 30-day mortality rates (1.1–2.0%) and durability estimates of 10–15 years. Variation exists between devices in rates of permanent pacemaker implantation and coronary access. The current generation of TAVI prostheses represents a mature technology offering high safety and efficacy. The key development vectors are focused on further device miniaturization, enhancing long-term durability, and expanding indications. This analysis provides a novel, clinically oriented comparison that moves beyond technical specifications to guide optimal device selection based on specific patient anatomy and clinical characteristics. Full article

Economic development and its sustainability are influenced not only by material, human, financial, and intellectual factors, but also by psychological factors. In particular, the levels of business expectations, trust, and confidence significantly affect the resilience of the economy, especially in crucial sectors such as industry and, more specifically, industrial production. Based on political, economic, social, and legal stability, businesses are likely to assess their opportunities more optimistically and realistically. This, in turn, enables them to look confidently toward the future and provides a foundation for investing in further development. Conversely, a decline in business expectations and confidence can slow socio-economic development, potentially leading to recession or depression. The purpose of the article is to identify the association between business confidence (Impact of the Business Confidence Indicator, IBCI) and the level of industrial production (Industrial Production Index, IPI), as a crucial aspect of ensuring sustainable economic development. A correlation–regression analysis conducted using Ukraine as a case study—a country candidate for EU accession—and statistical data from the State Statistics Service of Ukraine (SSSU) for the period from 1 February 2022 to 1 September 2024 demonstrated that there is a stable, positive, and strong relationship between IBCI and IPI levels (r = 0.7; D = 0.49). The constructed linear correlation model indicates that, with other factors held constant, a one-percentage-point increase in positive business expectations may lead to a 2.23-point rise in the industrial production activity of enterprises in Ukraine’s manufacturing sector. Furthermore, approximately 49.0% of the variation in industrial production levels is likely explained by changes in business expectations. Verification of the constructed regression equation and assessment of its parameters indicate that it is statistically reliable and consistent with real economic processes. Specifically, the Fisher coefficient (F = 5.30) exceeds the critical (tabular) value (Ft = 2.04), with Se = 0.45 and C_95% = 1.96; the causality test based on the Granger methodology revealed the presence of a causal relationship, indicating that the IBCI influences the IPI. The obtained statistical results for the applied models and tests are as follows: MDF (p < 0.05), KPSS (p > 0.10), Durbin–Watson ≈ 2.0, Breusch–Godfrey (p = 0.32), White (p = 0.41), ARCH (p = 0.27), and SER (p = 0.36). The constructed correlation–regression equation also allowed forecasting based on trend line modeling—how IPI levels will change depending on business confidence. According to the forecast, the IPI in Ukraine at the beginning of 2030 is expected to increase by 63.48 percentage points compared to the beginning of 2024, reaching 153.6%. Full article

Silicon-Based Physical Unclonable Functions (PUFs) exploit inherent manufacturing variations to produce a unique, random, and ideally unclonable secret key. As electronic devices are decommissioned and sent for End of Life (EOL) recycling, the encrypted critical program information remains within the device. However, conventional PUFs remain vulnerable to invasive attacks and reverse engineering that with sufficient time, resources, and effort can enable an adversary to bypass the security enclave of the system and extract this secret data. Recent research has started to explore techniques to respond to tamper attempts using electromigration (EM) and time-dependent dielectric breakdown (TDDB) to the PUF entropy source, preventing future authentication attempts with well-known semiconductor reliability failure mechanisms. This work presents a Pre-Amplifier Physical Unclonable Function (Pre-Amp PUF) with a self-corruption function designed and manufactured in a 3 nm FinFET technology. This PUF can perform a destructive read operation as an EOL anti-counterfeit measure against recycled and reused electronics. The destructive read utilizes an accelerated aging technique that exploits both Hot Carrier Injection (HCI) and Bias Temperature Instability (BTI) degradations directly at the PUF entropy source bitcell data. This work demonstrates a silicon proven ability to irreversibly corrupt the encryption key, invalidating the PUF key, and blocking future authentication attempts. By utilizing HCI and BTI aging effects rather than physical damage a PUF that can self-corrupt its own key without being detectable with imaging techniques is demonstrated for the first time. A feedback loop enables corruption of up to ~30% of the PUF entropy source, which is approximately 3× more data corruption than the prior state of the art self-corrupting PUF. Our technique reuses on-chip stable (repeatable) PUF bitcells identifying circuitry and thereby minimizes the area overhead to support this differentiated feature. Full article

Virtual lifetime estimation is growing in importance, as replacing physical tests by simulations leads to cost reductions in the development of microelectronics assemblies. However, the predictions made by fatigue models often differ significantly from the lifetimes recorded in physical tests. Tuning these models is not straightforward, and results are often accurate only in specific test cases. Deviations may arise from manufacturing tolerances in the soldering process which can lead to deviations in the solder joint geometry. These include variations in the size of the copper pad area or in the volume of solder material. These factors, which have impacts on estimated lifetimes, are not fully understood. This paper assesses the impact of solder geometry in parallel with that of thermal cycling properties on estimated lifetimes. It is demonstrated that the shape and thermocycling properties of the solder joint significantly affect the thermomechanical lifetimes of surface-mounted resistors. Full article

Conventional innovation management methodologies (IMMs) often struggle to respond to the complexity, uncertainty, and cognitive diversity that characterise contemporary innovation projects. This study introduces Innovation Flow (IF), a human-centred and adaptive framework grounded in Flow Theory and enhanced by Generative Artificial Intelligence (GenAI). At its core, IF operationalises Personalised Innovation Techniques (PInnTs)—adaptive variations of established methods tailored to project genetics and team profiles, generated dynamically through a GenAI-based system. Unlike traditional IMMs that rely on static toolkits and expert facilitation, Innovation Flow (IF) introduces a dynamic, GenAI-enhanced system capable of tailoring techniques in real time to each project’s characteristics and team profile. This adaptive model achieved a 60% reduction in ideation and prototyping time while maintaining high creative performance and autonomy. IF thus bridges the gap between human-centred design and AI augmentation, providing a scalable, personalised, and more inclusive pathway for managing innovation. Using a mixed-methods design that combines grounded theory with quasi-experimental validation, the framework was tested in 28 innovation projects across healthcare, manufacturing, and education. Findings show that personalisation improves application fidelity, engagement, and resilience, with 87% of cases achieving high efficacy. GenAI integration accelerated ideation and prototyping by more than 60%, reduced dependence on expert facilitators, and broadened participation by lowering the expertise barrier. Qualitative analyses emphasised the continuing centrality of human agency, as the most effective teams critically adapted rather than passively adopted AI outputs. The research establishes IF as a scalable methodology that augments, rather than replaces, human creativity, accelerating innovation cycles while reinforcing motivation and autonomy. Full article

Flattened bamboo (FB) exhibits pronounced structural and chemical heterogeneity between outer and inner layers and between nodes and internodes. These variations critically influence its interfacial performance with waterborne acrylic coatings. This study aimed to clarify how primer layer configuration and substrate heterogeneity jointly affect the coating adhesion, hardness, and abrasion resistance of FB. Three coating schemes—one primer and one topcoat (1P1T), two primers and one topcoat (2P1T), and three primers and one topcoat (3P1T)—were applied to four types of FB substrates defined by layer and structural position. Adhesion, pencil hardness, and abrasion resistance were measured according to GB/T standards, complemented by surface roughness, contact angle, XPS, and SEM analyses. Results showed that substrate heterogeneity dominated coating behavior. The parenchyma-rich inner-layer internodes, characterized by higher polarity (O/C = 0.296) and rougher texture, exhibited stronger adhesion and superior abrasion stability, whereas the fiber-dense outer layer nodes, with lower polarity (O/C = 0.262), showed weaker bonding. Increasing the number of primer layers improved film continuity only when the substrate microstructure allowed sufficient primer penetration. The combined findings indicate that coating adhesion and wear stability are primarily governed by substrate composition and surface polarity rather than by coating thickness. These results provide scientific and practical guidance for optimizing primer application and surface preparation in the industrial finishing of bamboo-based decorative panels, while also highlighting the environmental and economic advantages of waterborne coating optimization for sustainable bamboo manufacturing. Full article

The thermal response of Carbon Fiber Reinforced Thermoplastic (CFRTP) tapes under short-term localized heating is critical for automated manufacturing processes. Conventional homogenized models often overlook microstructural heterogeneities that can promote non-uniform heating and affect the quality of the consolidated part. In this work, we combine insights from infrared thermography with finite element simulations at the fiber scale built on micrographs extracted from real tapes to quantify the effect of individual heterogeneities—including surface roughness, thickness variation, fiber agglomeration, and porosity—on thermal propagation. Three modeling configurations were compared under identical conditions: a full microstructure model; a simplified geometry-aware model (where the real geometry is taken into the account, including the surface roughness and thickness variability, but the properties of the domain are considered as a homogeneous-equivalent material); and a homogeneous-equivalent baseline with flat borders and uniform thickness. Results show that porosity effects depend strongly on location and orientation: large, horizontally aligned pores near the heated surface produce the highest gradients. Surface roughness, on the other hand, exerts dominant effects on surface temperature non-uniformity with respect to thickness variation and fiber distribution. These findings demonstrate that accounting for microscale heterogeneities is essential to achieve more accurate, optimized, and application-tailored analyses of CFRTP tapes in advanced manufacturing. Full article

Enhancing RAW images acquired under low-light conditions remains a fundamental yet challenging problem in computational photography and image signal processing. Recent deep learning-based approaches have shifted from real paired datasets toward synthetic data generation, where sensor noise is typically simulated through physical modeling. However, most existing methods primarily account for additive noise, neglect multiplicative noise components, and rely on global calibration procedures that fail to capture pixel-level manufacturing variability. Consequently, these methods struggle to faithfully reproduce the complex statistics of real sensor noise. To overcome these limitations, this paper introduces a physically grounded composite noise model that jointly incorporates additive and multiplicative noise components. We further propose a per-pixel noise simulation and calibration strategy, which estimates and synthesizes noise individually for each pixel. This physics-based calibration not only circumvents the constraints of global noise modeling but also captures spatial noise variations arising from microscopic CMOS sensor fabrication differences. Inspired by the recent success of rectified-flow methods in image generation, we integrate our physics-based noise synthesis into a rectified-flow generative framework and present PGRF (Physics-Guided Rectified Flow): a physics-guided rectified-flow framework for low-light RAW image enhancement. PGRF leverages the expressive capacity of rectified flows to model complex data distributions, while physical guidance constrains the generation process toward the desired clean image manifold. To evaluate our method, we constructed the LLID, a dedicated indoor low-light RAW benchmark captured using the Sony A7S II camera. Extensive experiments demonstrate that the proposed framework achieves substantial improvements over state-of-the-art methods in low-light RAW image enhancement. Full article

This study implemented a Lean Six Sigma (LSS) methodology to enhance the productivity of the mechanical and industrial engineering technology workshops of an international higher education institution. The efficiency and effectiveness of the engineering workshops were often compromised by poor housekeeping and operational practices, which resulted in incomplete tasks, long operational and activity times, disorganized tools, cluttered workspaces, and a lack of systematic processes for managing materials. These issues led to waste in the form of lost time, unnecessary movement, and safety risks. This eventually affected the overall productivity of the workshops. Following the combination of the Define, Measure, Analyze, Improve, and Control (DMAIC) methodology of Six Sigma with Lean manufacturing, the investigation was conducted in two parts. The first part of this research mainly consisted of measuring the existing state of the three workshops to map the process and frame issues and origins of variations. During the second part of this study, the focus shifted towards Lean thinking while applying the chosen Lean Six Sigma (LSS) tools. Implementation revealed several benefits in the workshops during each phase of DMAIC. A Plan–Do–Check–Act (PDCA) continuous improvement board was installed in the main workshop to promote continuous improvement and sustainability. The process capability increased for the main workshop and welding laboratory, which shows an increase in service and performance standards after LSS implementation. For the main workshop, the process capability ‘Cp’ increased from 0.33 to 1.24 and the process capability index (Cpk) increased from 0.26 to 0.99. The process capability index (Cpk) for the main workshop increased; however, it did not reach the value of 1.33 due to the computer workstation installation not being completed during the study. The welding laboratory showed an increased ‘Cp’ from 0.67 to 2.13, and the process capability index (Cpk) increased from 0.18 to 1.34. The layout of the workshop office was improved to support efficient workflow by providing easy access to frequently used resources while keeping movement paths clear, thereby minimizing interruptions and promoting productivity. As a result, machines and tools were used more productively and operation times decreased. The mechanical workshops can continue increasing their process capability by following the outcomes and findings of the current study, leading to sustainable quality, efficiency, and operational reliability improvements. Full article

Reverse engineering (RE) is increasingly recognized as a vital methodology for reconstructing mechanical components, particularly in high-value sectors such as aerospace, transportation, and energy, where technical documentation is often missing or outdated. This study presents a systematic review that investigates the application, challenges, and future directions of RE in mechanical component reconstruction. Adopting the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, 68 peer-reviewed studies were identified, screened, and synthesized. The review highlights RE applications in restoration, redesign, internal geometry modeling, and simulation-driven performance assessment, leveraging technologies such as 3D scanning, CAD modeling, and finite element analysis. However, persistent challenges remain across five domains: product complexity, tolerance and dimensional variations, scanning limitations, integration barriers, and human-material-process dependencies, which hinder automation, accuracy, and manufacturability. Future research opportunities include the automated conversion of point cloud data into editable boundary representation (B-rep) models and AI-driven approaches for feature recognition, geometry reconstruction, and the generation of simulation-ready models. Additionally, advancements in scanning techniques to capture hidden or internal features more effectively are crucial. Overall, this review provides a comprehensive synthesis of current practices and challenges while proposing pathways to advance RE in industrial applications, fostering greater automation, accuracy, and integration in digital manufacturing workflows. Full article