Search Results (369)

In nanometer integrated-circuit (IC) manufacturing, advanced technology scaling has intensified the effects of process variations on circuit reliability and performance. Random fluctuations in parameters such as threshold voltage, channel length, and oxide thickness further degrade design margins and increase the likelihood of functional failures. These variations often lead to rare circuit failure events, underscoring the importance of accurate yield estimation and robust design methodologies. Conventional Monte Carlo yield estimation is computationally infeasible as millions of simulations are required to capture failure events with extremely low probability. This paper presents a novel reliability-based circuit design optimization framework that leverages deep transfer learning to improve the efficiency of repeated yield analysis in optimization iterations. Based on pre-trained neural network models from prior design knowledge, we utilize model fine-tuning to accelerate importance sampling (IS) for yield estimation. To improve estimation accuracy, adversarial perturbations are introduced to calibrate uncertainty near the model decision boundary. Moreover, we propose a cascaded batch Bayesian optimization (CBBO) framework that incorporates a smart initialization strategy and a localized penalty mechanism, guiding the search process toward high-yield regions while satisfying nominal performance constraints. Experimental validation on SRAM circuits and amplifiers reveals that CBBO achieves a computational speedup of 2.02×–4.63× over state-of-the-art (SOTA) methods, without compromising accuracy and robustness. Full article

This study establishes a systematic and reproducible workflow for topology optimization (TO) of indexable face milling cutter bodies with integrated internal coolant channels, designed for Additive Manufacturing (AM) of metallic parts. Grounded in Design for Additive Manufacturing (DfAM) principles, the workflow combines displacement-based TO and computational fluid dynamics analysis to generate simulation-driven tool geometries tailored to the constraints of AM. By leveraging iterative design knowledge, the proposed methodology enhances the scalability and repeatability of the design process, reducing development time and supporting rapid adaptation across various tool geometries. AM is explicitly exploited to integrate support-free internal coolant channels directed toward the insert cutting edge, thereby achieving a 20% mass reduction relative to the initial milling tool designs, and improving material usage efficiency at the design stage. The workflow yields numerically optimized geometries that maintain simulated global stiffness under the considered loading conditions and exhibit coolant flow distributions that effectively target the exposed cutting edges. These simulation results demonstrate the feasibility of an AM oriented, workflow-based approach for the numerical design of milling tools with internal cooling, mass reduction and provide a focused basis for subsequent experimental validation and comparison with conventionally manufactured counterparts. Full article

Polymer composites have opened a novel innovation phase in additive manufacturing (AM), and now lightweight, high-strength, and geometrical advanced components with tailored functionalities can be produced. The present study introduces advances in polymer composite materials and their integration into AM processes, particularly in rapidly growing industries such as aerospace, automotive, biomedical, and electronics. The embedding of cutting-edge reinforcement materials, such as nanoparticles, carbon fibers, and natural fibers, into polymer matrices enhances mechanical, thermal, electrical, and multifunctional properties. These material developments are combined with advanced fabrication techniques, including multi-material printing, in situ curing, and functionally graded manufacturing, which achieves accurate regulation of microstructures and properties. Furthermore, high-impact innovations such as smart polymer composites with self-healing or stimuli-responsive behaviors, the growing shift toward sustainable, bio-based composite alternatives, are driving progress. Despite significant advances, challenges remain in interfacial bonding, printability, process repeatability, and long-term durability. This review offers a comprehensive overview of current advancements and outlines future directions in polymer composite–based AM. Full article

The manufacturing process contributes significantly to the proliferation, metabolic state, and functional persistence of chimeric antigen receptor (CAR)-T cells. However, how different culture systems regulate CAR-T cell metabolism and thereby influence their long-term antitumor activity remains poorly understood. In this study, we compared dynamic cultivation using a wave bioreactor with static expansion systems (gas-permeable and conventional T-flasks) for the production of CD99-specific CAR-T cells. CAR-T cells expanded by the wave bioreactor exhibited faster proliferation and stronger cytotoxicity during culture. Upon repeated antigen stimulation, they retained these enhanced functional properties and showed the reduced expression of immune checkpoint molecules, preferentially preserved memory-like subsets, and displayed transcriptional features consistent with memory maintenance and exhaustion resistance. Targeted metabolomic profiling revealed enhanced Tricarboxylic Acid (TCA) cycle activity and features consistent with sustained oxidative phosphorylation, supporting mitochondrial-centered metabolic reprogramming. In a Ewing sarcoma xenograft model, wave bioreactor-cultured CAR-T cells showed a greater percentage of memory-like tumor-infiltrating lymphocytes. Collectively, these results indicate that wave bioreactor-based dynamic cultivation promotes mitochondrial metabolic reprogramming, which is characterized by an enhanced TCA cycle and sustained oxidative phosphorylation, thereby sustaining CAR-T cell functionality and providing a robust platform for the manufacturing of potent and durable cellular therapeutics. Full article

This study evaluated the properties of two commercial filaments intended for medical and sterile applications: PLACTIVE (Copper 3D, Santiago, Chile) and CPE ANTIBAC (Fiberlogy, Brzezie, Poland). The aim of the research was to compare the dimensional accuracy, repeatability of the fused deposition modeling (FDM) 3D printing process, and the antibacterial properties of the samples using standardized procedures. Four types of samples were manufactured: geometrically differentiated specimens for metrological measurements (S1); cylinders with a diameter of 15 mm and a height of 40 mm for assessing process repeatability (S2); rectangular specimens measuring 40 × 40 × 2 mm for surface topography analysis (S3); and rectangular samples measuring 20 × 20 × 2 mm for biocidal property evaluation (S4). The results demonstrated that PLACTIVE samples exhibited higher dimensional conformity with nominal values and lower variability of diameters than CPE ANTIBAC samples, which may be associated with greater process stability. For both materials, the PSm parameter was correlated with layer height only in the 90° printing orientation. Surface topography analysis showed that increasing the layer height from 0.08 mm to 0.20 mm led to a significant rise in Rsm, Ra, and Sa values, indicating deterioration in the reproduction of micro-irregularities and increased spatial differentiation of the surface. For PLACTIVE samples, a tendency toward more convex structures with positive Rsk values and moderate kurtosis (Rku) was observed, suggesting uniform plasticization and stable interlayer bonding, particularly at the 0° orientation. In contrast, CPE ANTIBAC samples (especially those printed at 90°) were characterized by higher Ra and Sa values and negative skewness (Rsk), indicating valley-dominated, sharper surface morphology resulting from different rheological behavior and faster solidification of the material. PLACTIVE samples did not exhibit antibacterial properties against Escherichia coli (E. coli), while for Staphylococcus aureus (S. aureus), the activity was independent of printing direction and layer height. The CPE ANTIBAC material showed antibacterial effects against both tested strains in approximately 50% of the samples. The findings provide insights into the relationships between material type, printing orientation, and process parameters in shaping the dimensional and biocidal properties of FDM filaments. Full article

Liquid Crystal Elastomers combine the elasticity of polymer networks with the anisotropic ordering of liquid crystals, thus enabling reversible shape modifications and stimulus responsive actuation. Unfortunately, manual LCE fabrication remains limited by operator-dependent variability, which can lead to inconsistent film thickness and manufacturing times inadequate for a mass production. This work presents a low-cost, automated manufacturing framework that redesigns the mechanical assembly steps of the traditional one-step LCE fabrication process. The design includes rubbing, slide alignment, spacer placement, and infiltration cell assembly to ensure consistent film quality and scalability. A customized Cartesian robot, built by adapting a modified X–Y core 3D printer, integrates specially designed manipulator systems, redesigned magnetic slide holders, automated rubbing tools, and supporting fixtures to assemble infiltration devices in an automated way. Validation tests demonstrate reproducible infiltration, improved mesogen alignment confirmed via polarized optical microscopy, and high geometric repeatability, although glass-slide thickness variability remains a significant contributor to deviations in final film thickness. By enabling parallelizable low-cost production, the designed hardware demonstrates its effectiveness in devising the scalable manufacturing of LCE films suited for advanced therapeutic and engineering applications. Full article

Laser powder bed fusion (LPBF) is the best suited technology to manufacture temperature-resistant Ti-6Al-2Sn-4Zr-6Mo parts with complex geometrical features for high-end applications. Improving printing accuracy by reducing the layer thickness (t) generally requires repeating a tedious and time-consuming process optimization routine. To simplify this endeavour, the present work proposes three process equivalence criteria allowing to transfer optimized process conditions from one printing parameter set to another. This approach recommends keeping the volumetric laser energy density (VED) and hatching space-to-layer thickness ratio (h/t) constant, while adjusting the scanning speed (v) and hatching space (h) accordingly. To validate this approach, Ti6246 parts were printed with 50 µm and 25 µm layer thicknesses, while keeping VED = 100 J/mm³ and h/t = 3 constant for both cases. The printed samples were analyzed in terms of their density, microstructure and mechanical properties, as well as the geometric compliance of wall-, gap- and channel-containing artefacts. Highly dense samples exhibiting comparable microstructures and mechanical properties were obtained with both parameters sets investigated. However, they induced markedly differing geometric characteristics. Notably, using 25 µm layers allowed printing walls as thin as 0.2 mm as compared to 1.0 mm for 50 µm layers. Full article

The wire Laser-based Directed Energy Deposition (DED-LB) metal additive manufacturing (AM) process is time- and cost-effective, providing high-quality, dense parts while supporting multi-scale manufacturing, repair, and repurposing services. However, its ability to consistently produce parts of uniform quality depends on process stability, which can be achieved through monitoring and controlling key process phenomena, such as heat accumulation and variations in the distance between the deposition head and the working surface (standoff distance). Part quality is closely linked to achieving predictable melt pool dimensions and stable thermal conditions, which in turn influence the end-part’s cross-sectional stability, overall dimensions, and mechanical properties. This work presents a workflow that correlates process and metrology data, enabling the determination of tunable process parameters and their operating process window. The process data are acquired using a vision-based monitoring system and a load-cell embedded in the deposition head, which together detect variations in melt pool area and standoff distance during the process, while metrology devices assess the part quality. Finally, this monitoring setup and its ability to capture the complete process history are fundamental for developing in-line control strategies, enabling optimized, supervision-free, and repeatable processes. Full article

Triply periodic minimal surfaces (TPMS) are mathematically defined minimal surfaces that exhibit zero mean curvature and repeat periodically along all three Cartesian axes. They integrate mathematically defined geometry with extensive functional adjustability. Their smooth, non-self-intersecting topology enables systematic control over relative density and improves load transfer efficiency within the lattice. Their large surface area-to-volume ratios further enhance specific energy absorption (SEA) and enable diverse functional uses. Recent developments in additive manufacturing (AM) have made it easier to create TPMS structures. As a result, they are now considered as the architected materials that combine biological, thermal, and mechanical functions within a single framework. This study presents a comprehensive overview of the major TPMS structures. It further highlights several AM techniques used for their fabrication and provides a critical evaluation of how geometric design, relative density, and post-processing influence their mechanical and thermal performances. This work also discusses recent developments in graded and hybrid TPMS structures. It further identifies the main challenges and future research directions related to multi-material additive manufacturing and data-driven topology optimization. Full article

Digital maturity is increasingly recognised as a determinant of competitiveness for small and medium-sized enterprises (SMEs), yet empirical evidence from advanced economies remains limited. Here, we evaluate a sample of Luxembourgish manufacturing SMEs across six dimensions of the Digital Maturity Assessment Tool (DMAT)—Digital Business Strategy (DBS), Digital Readiness (DR), Human-Centric Digitalisation (HCD), Data Governance/Connectedness (DG), Automation and AI (AAI), and Green Digitalisation (GD)—to quantify their overall maturity. To avoid compositional artefacts, given that we rely on the EU’s DMAT, we introduce leave-one-out correlation (LOOC) to assess the association between DMA score and each focal dimension; within-firm disparities were tested via repeated-measures ANOVA; sample profiles were examined using Principal Component Analysis (PCA) followed by hierarchical clustering (HCPC). Respectively, the results converged across methods: HCD (r = 0.717) and DBS (r = 0.652) exhibited the strongest links to maturity, DG/AAI/GD were moderate contributors (r ≈ 0.50–0.58), and DR was weak (r = 0.298). The ANOVA analysis indicated substantial between-dimension differences (partial η² ≈ 0.41), with DG and DBS leading and AAI and GD lagging. PCA–HCPC revealed two coherent cluster profiles—Leaders and Laggards—arrayed along a general maturity axis, with the most significant gaps in DBS and HCD. Practically, firms that prioritise DBS and HCD exhibit a higher DMA score, which creates a foundation for industrialising and automatising manufacturing processes. Given the small, single-country, cross-sectional design, longitudinal and adequately powered studies with objective performance outcomes are warranted to validate and generalise these findings. Full article

This work reports the design, fabrication, and validation of a modular ergonomic saddle for rehabilitation cycling, developed through a combined additive manufacturing approach. The saddle consists of a metallic support produced by Laser Powder Bed Fusion (LPBF) in AISI 316L stainless steel and a polymeric ergonomic covering fabricated via Selective Laser Sintering (SLS) using thermoplastic polyurethane (TPU). A preliminary material screening between TPU and polypropylene (PP) was conducted, with TPU selected for its superior elastic response, energy dissipation, and more favourable SLS processability, as confirmed by thermal analyses. A series of gyroid lattice configurations with varying cell sizes and wall thicknesses were designed and mechanically tested. Cyclic testing under both stress- and displacement-controlled conditions demonstrated that the configuration with 8 mm cell size and 0.3 mm wall thickness provided the best balance between compliance and stability, showing minimal permanent deformation after 10,000 cycles and stable force response under repeated displacements. Finite Element Method (FEM) simulations, parameterized using experimentally derived elastic and density data, correlated well with the mechanical results, correlated with the mechanical results, supporting comparative stiffness evaluation. Moreover, a cost model focused on the customizable TPU component confirmed the economic viability of the modular approach, where the metallic base remains a reusable standard. Finally, the modular saddle was fabricated and successfully mounted on a cycle ergometer, demonstrating functional feasibility. Full article

In extremely harsh high-temperature environments in aerospace, industrial manufacturing and other fields, traditional ultrasonic temperature measurement technology has certain limitations. This paper proposes a differential single crystal sapphire ultrasonic temperature measurement method based on the magnetostrictive effect. This method abandons the traditional sensitive flexural structure and uses two single-crystal sapphire waveguides of the same material, same diameter, and slightly different lengths as sensing elements. By measuring the time delay difference between their end-face echoes, the sound velocity is inverted and the temperature is measured. COMSOL multi-physics v6.1 simulation was used to optimize the bias magnetic field design of the magnetostrictive transducer, which improved the system’s energy conversion efficiency and high-temperature stability. Experimental results show that in the range of 300–1200 °C, the sensor delay increases monotonically with increasing temperature, the sound speed shows a downward trend, and the repeatability error is less than 5%; the differential processing method effectively suppresses common mode noise in the range of 300–700 °C, and still shows high sensitivity above 800 °C. This research offers a technical solution with high reliability and accuracy for temperature monitoring in extreme environments such as those characterized by high temperatures and high pressures. Full article

This study investigates the effectiveness of a virtual reality (VR) simulation for teaching the hand lay-up process in composite manufacturing within mechanical engineering education. A within-subjects experiment involving 17 undergraduate mechanical engineering students compared the VR-based training with conventional physical laboratory instruction. Task performance, cognitive load, and learner perceptions were measured using procedural accuracy scores, completion times, NASA-TLX workload ratings, and post-task interviews. Results indicated that while participants required more time to complete the task in VR, procedural accuracy was comparable between VR and physical labs. VR significantly reduced mental, physical, and effort-related demands but elicited higher frustration levels, primarily due to navigation challenges and motion discomfort. Qualitative feedback showed strong learner preference for VR, citing its hazard-free environment, repeatability, and step-by-step guidance. These findings suggest that VR offers a viable and pedagogically effective alternative or complement to traditional composite-manufacturing training, particularly in contexts where access to physical facilities is limited. Future work should examine long-term skill retention, incorporate haptic feedback for tactile realism, and explore hybrid models combining VR and physical practice to optimise learning outcomes. Full article

Metal binder jetting (MBJ) is an additive manufacturing technology of increasing interest due to its potential competitiveness in medium- and large-scale production, especially from a sustainability perspective. However, challenges in controlling the product accuracy and precision significantly limit the widespread adoption of this technology. This work investigates the achievable accuracy, precision, and spatial repeatability of parts produced using the MBJ process. Additionally, the paper aims to identify the causes of inaccuracy and suggest countermeasures to improve the product quality. The study was conducted experimentally by designing a benchmark geometry with various basic features. This geometry was scaled to three sizes—10–20 mm (small), 20–30 mm (intermediate), and 30–50 mm (large)—and produced using two different stainless-steel powders: AISI 316L and 17-4PH. In the green state, the dimensional tolerances ranged from IT8 to IT12 for features parallel to the build direction (heights) and from IT9 to IT13 for features parallel to the build plane (lengths). In the sintered state, the tolerances ranged from IT10 to IT16. This study reveals the challenges in scaling geometries to compensate for accuracy loss originating from the printing and sintering stages. In the green state, accuracy issues are likely due to non-uniform binder application and drying operations. In the sintered state, the accuracy loss is related to variable shrinkage based on the feature size, anisotropic shrinkage depending on the print direction, and differing densification mechanisms influenced by the material type. This study offers novel insights for improving MBJ process precision, supporting wider adoption in the manufacturing industry. Full article

Research on big data analytics (BDA) and supply chains often inventories “capabilities” but rarely explains how firms progress through adoption—or how governance over data and related resources shapes resilience outcomes. Drawing on 16 semi-structured interviews with senior managers in the manufacturing sector, we analyze organizational practices around data, analytics, and decision-making and synthesize a governed-adoption process framework. The framework specifies how five governance levers—ownership, standards, stewardship, access, lineage—operate differentially across four adoption gates (data plumbing—descriptive monitoring—predictive alerting—prescriptive decisioning). To move beyond staged descriptions, we make the underlying generative mechanisms explicit—Comparability, Explainability, Authorization, Fidelity, Executability—and link them to dynamic-capability micro foundations (sensing, seizing, reconfiguring) via decision-latency outcomes (“resilience timers”: Time-to-Detect, Time-to-Decide, Time-to-Reconfigure, Time-to-Recover). Brief deviant-case contrasts (e.g., notification without action; dashboards without owners) clarify boundary conditions under which governance enables or impedes resilient action. We also state concise, testable propositions (e.g., standards+lineage as a necessary condition for improving Time-to-Detect; ownership+access as necessary for improving Time-to-Decide) and provide gate exit-criteria to support evaluation and future comparative tests. Claims are bounded to analytic generalization from a single-country, manufacturing-sector qualitative sample; we make no assertion of statistical validation. Practically, the framework prioritizes governance work ahead of tool spend, helping organizations convert dashboards into repeatable decisions at speed. Full article