Hasil untuk "Production capacity. Manufacturing capacity"

Jeong Woo Park, Huiyang Kim

The continuous growth in global air traffic demand highlights the need to accurately estimate airspace capacity for efficiently using limited resources in air traffic management (ATM) systems. Although previous studies focused on either sector capacity based on air traffic controllers (ATCo) workload or runway throughput, studies on the unique structural and functional characteristics of terminal control area (TMA) remain lacking. In this study, capacity is defined as the maximum occupancy count. Further, a TMA capacity estimation model grounded in structural space conceptually defined as the space formed by instrument flight procedures and traffic characteristics is developed. Capacity is estimated from the temporal flight distance, which represents the physical length of arrival paths converted to flight time, and the average time separation at the runway threshold considering traffic proportions and aircraft mix. The proposed model is applied to the Jeju International Airport TMA (RWY 07/25) using one year of ADS-B trajectory data. The estimated capacities are 9.3 (RWY 07) and 6.9 (RWY 25) aircraft, and the differences are attributed to the temporal flight distance. Sensitivity analysis shows that capacity is shaped by aircraft speed and air traffic control (ATC) separations, which implies that operational measures such as speed restrictions or adjusted separations effectively enhance capacity even within physically constrained TMA. The model offers a practical, transparent, and quantitative framework for TMA capacity assessment and operational design.

João V. N. Sousa, João M. P. Q. Delgado, Ricardo S. Gomez et al.

Resin Transfer Molding (RTM) is a widely used composite manufacturing process where liquid resin is injected into a closed mold filled with a fibrous preform. By applying this process, large pieces with complex shapes can be produced on an industrial scale, presenting excellent properties and quality. A true physical phenomenon occurring in the RTM process, especially when using vegetable fibers, is related to the absorption of resin by the fiber during the infiltration process. The real effect is related to the slowdown in the advance of the fluid flow front, increasing the mold filling time. This phenomenon is little explored in the literature, especially for non-isothermal conditions. In this sense, this paper does a numerical study of the liquid injection process in a closed and heated mold. The proposed mathematical modeling considers the radial, three-dimensional, and transient flow, variable injection pressure, and fluid viscosity, including the effect of liquid fluid absorption by the reinforcement (fiber). Simulations were carried out using Computational Fluid Dynamic tools. The numerical results of the filling time were compared with experimental results, and a good approximation was obtained. Further, the pressure, temperature, velocity, and volumetric fraction fields, as well as the transient history of the fluid front position and injection fluid volumetric flow rate, are presented and analyzed.

Alireza Soleymanipoor, Tomoyoshi Maeno

An environmentally friendly alternative to phosphate-based lubrication was studied through the lateral cold extrusion forging of internal gears using pulsating motion. A die set with a removable punch enabled a detailed observation of relubrication, forming load, material flow, and gear geometry. Pulsating motion with liquid lubricant significantly reduced the forming load during punch penetration, while no such effect was observed under dry conditions. Even when the number of pulses (<i>n</i>) was set to 1, relubrication occurred, and a comparable load reduction to that of <i>n</i> = 3 was achieved, shortening the forming time. When <i>n</i> = 3, pulsating motion contributed to increased gear height and reduced separated burr formation; however, it also caused slightly incomplete tooth filling, which may be undesirable for precision applications. Varying the pulse start position from 5.50 mm to 13.30 mm influenced forming load and material flow, further affecting gear geometry. During punch extraction, the presence of liquid lubricant reduced the load and suppressed material displacement, while dry conditions led to higher extraction loads and more deformation.

Jiahao Wu, Yimin Hua, Yanjiang Zheng et al.

Skeletal muscle, constituting ~40% of body mass, serves as a primary effector for movement and a key metabolic regulator through myokine secretion. Hereditary myopathies, including dystrophinopathies (DMD/BMD), limb–girdle muscular dystrophies (LGMD), and metabolic disorders like Pompe disease, arise from pathogenic mutations in structural, metabolic, or ion channel genes, leading to progressive weakness and multi-organ dysfunction. Gene therapy has emerged as a transformative strategy, leveraging viral and non-viral vectors to deliver therapeutic nucleic acids. Adeno-associated virus (AAV) vectors dominate clinical applications due to their efficient transduction of post-mitotic myofibers and sustained transgene expression. Innovations in AAV engineering, such as capsid modification (chemical conjugation, rational design, directed evolution), self-complementary genomes, and tissue-specific promoters (e.g., MHCK7), enhance muscle tropism while mitigating immunogenicity and off-target effects. Non-viral vectors (liposomes, polymers, exosomes) offer advantages in cargo capacity (delivering full-length dystrophin), biocompatibility, and scalable production but face challenges in transduction efficiency and endosomal escape. Clinically, AAV-based therapies (e.g., Elevidys^® for DMD, Zolgensma^® for SMA) demonstrate functional improvements, though immune responses and hepatotoxicity remain concerns. Future directions focus on AI-driven vector design, hybrid systems (AAV–exosomes), and standardized manufacturing to achieve “single-dose, lifelong cure” paradigms for muscular disorders.

Felix Parker, Fardin Ganjkhanloo, Diego A. Martínez et al.

Effective hospital capacity management is pivotal for enhancing patient care quality, operational efficiency, and healthcare system resilience, notably during demand spikes like those seen in the COVID-19 pandemic. However, devising optimal capacity strategies is complicated by fluctuating demand, conflicting objectives, and multifaceted practical constraints. This study presents a data-driven framework to optimize capacity management decisions within hospital systems during surge events. Two key decisions are optimized over a tactical planning horizon: allocating dedicated capacity to surge patients and transferring incoming patients between emergency departments (EDs) of hospitals to better distribute demand. The optimization models are formulated as robust mixed-integer linear programs, enabling efficient computation of optimal decisions that are robust against demand uncertainty. The models incorporate practical constraints and costs, including setup times and costs for adding surge capacity, restrictions on ED patient transfers, and relative costs of different decisions that reflect impacts on care quality and operational efficiency. The methodology is evaluated retrospectively in a hospital system during the height of the COVID-19 pandemic to demonstrate the potential impact of the recommended decisions. The results show that optimally allocating beds and transferring just 32 patients over a 63 day period around the peak, about one transfer every two days, could have reduced the need for surge capacity in the hospital system by nearly 90%. Overall, this work introduces a practical tool to transform capacity management decision-making, enabling proactive planning and the use of data-driven recommendations to improve outcomes.

Alessio Amaolo, Pengning Chao, Benjamin Strekha et al.

Information transfer through electromagnetic waves is an important problem that touches a variety of technologically relevant applications, including computing and telecommunications. Prior attempts to establish limits on optical information transfer have treated waves propagating through known photonic structures (including vacuum). In this article, we address fundamental questions concerning optimal information transfer in photonic devices. Combining information theory, wave scattering, and optimization theory, we formulate bounds on the maximum Shannon capacity that may be achieved by structuring senders, receivers, and their environment. Allowing for arbitrary structuring leads to a non-convex problem that is significantly more difficult than its fixed structure counterpart, which is convex and satisfies a known "water-filling" solution. We derive a geometry-agnostic convex relaxation of the problem that elucidates fundamental physics and scaling behavior of Shannon capacity with respect to device parameters and the importance of structuring for enhancing capacity. We also show that in regimes where communication is dominated by power insertion requirements, bounding Shannon capacity maps to a biconvex optimization problem in the basis of singular vectors of the Green's function. This problem admits analytical solutions that give physically intuitive interpretations of channel and power allocation and reveals how Shannon capacity varies with signal-to-noise ratio. Proof of concept numerical examples show that bounds are within an order of magnitude of achievable device performance and successfully predict the scaling of performance with channel noise. The presented methodologies have implications for the optimization of antennas, integrated photonic devices, metasurface kernels, MIMO space-division multiplexers, and waveguides to maximize communication efficiency and bit-rates.

Fynn Atzler, Simon Hümbert, Heinz Voggenreiter

Fused granular fabrication (FGF) is used in industrial applications to manufacture complex parts in a short time frame and with reduced costs. Recently, the overprinting of continuous fibre-reinforced laminates has been discussed to produce high-performance, functional structures. A hybrid process combining FGF with Automated Fibre Placement (AFP) was developed to implement this approach, where an additively manufactured structure is bonded in situ onto a thermoplastic laminate. However, this combination places great demands on process control, especially in the first printing layer. When 3D printing onto a laminate, the height of the first printed layer is decisive to the shear strength of the bonding. Manufacturing-induced surface defects of a laminate, like thermal warpage, gaps, and tape overlaps, can result in deviations from the ideal geometry and thus impair the bonding strength when left uncompensated. This study, therefore, proposes a novel process flow that uses a 3D scan of a laminate to adjust the geometry of the additively manufactured structure to achieve a constant layer height in the 3D print and, thus, constant mechanical properties. For the above-listed surface defects, only thermal warpage was found to have a significant effect on the bonding strength.

André F. V. Pedroso, Naiara P. V. Sebbe, Rúben D. F. S. Costa et al.

Machining INCONEL^® presents significant challenges in predicting its behaviour, and a comprehensive experimental assessment of its machinability is costly and unsustainable. Design of Experiments (DOE) can be conducted non-destructively through Finite Element Analysis (FEA). However, it is crucial to ascertain whether numerical and constitutive models can accurately predict INCONEL^® machining. Therefore, a comprehensive review of FEA machining strategies is presented to systematically summarise and analyse the advancements in INCONEL^® milling, turning, and drilling simulations through FEA from 2013 to 2023. Additionally, non-conventional manufacturing simulations are addressed. This review highlights the most recent modelling digital solutions, prospects, and limitations that researchers have proposed when tackling INCONEL^® FEA machining. The genesis of this paper is owed to articles and books from diverse sources. Conducting simulations of INCONEL^® machining through FEA can significantly enhance experimental analyses with the proper choice of damage and failure criteria. This approach not only enables a more precise calibration of parameters but also improves temperature (<i>T</i>) prediction during the machining process, accurate Tool Wear (TW) quantity and typology forecasts, and accurate surface quality assessment by evaluating Surface Roughness (SR) and the surface stress state. Additionally, it aids in making informed choices regarding the potential use of tool coatings.

Yelda Karatepe Mumcu

Assembly line efficiency is one of the most important parameters that determine the overall efficiency of a manufacturing company. The production of a product under optimum conditions is ensured by a balanced assembly. With a balanced assembly line, machinery, material and labour costs are reduced. Within the scope of this research, real data about the daily production capacity and assembly line efficiency of a company producing Emergency Luminaire were taken, the same assembly line was balanced with 4 different Heuristic ALB methods and the results were compared. According to the results obtained, a high line efficiency of 93.955% was achieved using the Hoffman, Comsoal and Moodie&Young (M&Y) methods, and 84.414% was achieved with the Ranked Positional Weight (RPW) method. As a result of this, it was observed that the daily production capacity increased from 250 units to 375 units. As a result of the study, it was revealed that the efficiency of the existing assembly line and accordingly the daily production capacity increased. In addition, the study results of this assembly line were taught to an artificial neural network model for training purposes, and the work station results of the operations of a different assembly line were obtained with 99.940 accuracy. In this context, it has been revealed that the artificial neural networks method can be used in addition to the use of the heuristic method in the solution of ALB problems.

Lev Yohananov, Moshe Schwartz

We derive the coding capacity for duplication-correcting codes capable of correcting any number of duplications. We do so both for reverse-complement duplications, as well as palindromic (reverse) duplications. We show that except for duplication-length $1$, the coding capacity is $0$. When the duplication length is $1$, the coding capacity depends on the alphabet size, and we construct optimal codes.

Qianyi Zhang, Antoine Boniface, Virendra K. Parashar et al.

Light-based additive manufacturing holds great potential in the field of bioprinting due to its exceptional spatial resolution, enabling the reconstruction of intricate tissue structures. However, printing through biological tissues is severely limited due to the strong optical scattering within the tissues. The propagation of light is scrambled to form random speckle patterns, making it impossible to print features at the diffraction-limited size with conventional printing approaches. The poor tissue penetration depth of ultra-violet or blue light, which is commonly used to trigger photopolymerization, further limits the fabrication of high cell-density tissue constructs. Recently, several strategies based on wavefront shaping have been developed to manipulate the light and refocus it inside scattering media to a diffraction-limited spot. In this study, we present a high-resolution additive manufacturing technique using upconversion nanoparticles and a wavefront shaping method that does not require measurement from an invasive detector, i.e., it is a non-invasive technique. Upconversion nanoparticles convert near-infrared light to ultraviolet and visible light. The ultraviolet light serves as a light source for photopolymerization and the visible light as a guide star for digital light shaping. The incident light pattern is manipulated using the feedback information of the guide star to focus light through the tissue. In this way, we experimentally demonstrate that near-infrared light can be non-invasively focused through a strongly scattering medium. By exploiting the optical memory effect, we further demonstrate micro-meter resolution additive manufacturing through highly scattering media such as a 300-μm-thick chicken breast. This study provides a proof of concept of high-resolution additive manufacturing through turbid media with potential application in tissue engineering.

Alberto Murillo-Marrodán, Yury Gamin, Liudmila Kaputkina et al.

The cross-roll piercing and elongation (CPE) is a forming process performed at high temperatures and high strain rates. The final product quality is strongly dependent on its microstructure. In this study, a finite element method (FEM) model was developed to better understand plastic deformation effects on microstructure during CPE and to analyze alternative thermo-mechanical processing routes. Specific models were used to simulate dynamic and meta-dynamic recrystallization (DRX and MDRX) for the processing of superaustenitic stainless steel (SASS). In addition, the CPE of SASS was investigated experimentally. The microstructure, mechanical properties, and chemical changes of the final product were assessed using optical microscopy, hardness testing, X-ray diffraction, and SEM-EDS. The results revealed higher temperatures and strain rates in the exterior area of the shell after piercing, and MDRX occurred in the whole thickness. However, an average grain size reduction of 13.9% occurred only in the shell middle and inner diameters. During elongation, the highest values of the strain rate and DRX were observed in the inner region, exhibiting a grain size reduction of 38%. Spread in terms of grain size and grain shape anisotropy was found to be less accentuated for tube samples as compared to the pierced shells.

Neha Sangwan, Varun Narayanan, Vinod M. Prabhakaran

We study communication with consensus over a broadcast channel - the receivers reliably decode the sender's message when the sender is honest, and their decoder outputs agree even if the sender acts maliciously. We characterize the broadcast channels which permit this byzantine consensus and determine their capacity. We show that communication with consensus is possible only when the broadcast channel has embedded in it a natural ''common channel'' whose output both receivers can unambiguously determine from their own channel outputs. Interestingly, in general, the consensus capacity may be larger than the point-to-point capacity of the common channel, i.e., while decoding, the receivers may make use of parts of their output signals on which they may not have consensus provided there are some parts (namely, the common channel output) on which they can agree.

Adam Scherlis, Kshitij Sachan, Adam S. Jermyn et al.

Individual neurons in neural networks often represent a mixture of unrelated features. This phenomenon, called polysemanticity, can make interpreting neural networks more difficult and so we aim to understand its causes. We propose doing so through the lens of feature \emph{capacity}, which is the fractional dimension each feature consumes in the embedding space. We show that in a toy model the optimal capacity allocation tends to monosemantically represent the most important features, polysemantically represent less important features (in proportion to their impact on the loss), and entirely ignore the least important features. Polysemanticity is more prevalent when the inputs have higher kurtosis or sparsity and more prevalent in some architectures than others. Given an optimal allocation of capacity, we go on to study the geometry of the embedding space. We find a block-semi-orthogonal structure, with differing block sizes in different models, highlighting the impact of model architecture on the interpretability of its neurons.

Marco-Tulio Fonseca Rodrigues

Capacity and coulombic efficiency are often used to assess the performance of Li-ion batteries, under the assumption that these quantities can provide direct insights about the rate of electron consumption due to growth of the solid electrolyte interphase (SEI). Here, we show that electrode properties can actually change the amount of information about aging that can be directly retrieved from capacity measurements. During cycling of full-cells, only portions of the voltage profiles of the cathode and anode are accessible, leaving a reservoir of cyclable Li+ stored at both electrodes. The size and availability of this reservoir depends on the shape of the voltage profiles, and accessing this extra Li+ can offset some of the capacity that is consumed by the SEI. Consequently, capacity and efficiency measurements can, at times, severely underestimate the rate of side reactions experienced by the cell. We show, for example, that a same rate of SEI growth would cause faster capacity fade in LiFePO4 than in NMC cells, and that the perceived effects of aging depend on testing variables such as depth of discharge. Simply measuring capacity may be insufficient to gauge the true extent of aging endured by Li-ion batteries.

Chiao-Hsuan Wang, Fangxin Li, Liang Jiang

High-performance quantum transducers, which faithfully convert quantum information between disparate physical carriers, are essential in quantum science and technology. Different figures of merit, including efficiency, bandwidth, and added noise, are typically used to characterize the transducers' ability to transfer quantum information. Here we utilize quantum capacity, the highest achievable qubit communication rate through a channel, to define a single metric that unifies various criteria of a desirable transducer. Using the continous-time quantum capacities of bosonic pure-loss channels as benchmarks, we investigate the optimal designs of generic quantum transduction schemes implemented by transmitting external signals through a coupled bosonic chain. With physical constraints on the maximal coupling rate $g_{max}$, the highest continuous-time quantum capacity $Q^{max} \approx 5 g_{max}$ is achieved by transducers with a maximally flat conversion frequency response, analogous to Butterworth electric filters. We further investigate the effect of thermal noise on the performance of transducers.

Javaid Butt, Raghunath Bhaskar, Vahaj Mohaghegh

Fused filament fabrication (FFF) is a widely used additive manufacturing process that can produce parts from thermoplastics. Its ease of operation and wide variety of materials make it a popular choice for manufacturing. To leverage such benefits, the commonly used thermoplastics (e.g., PLA and ABS) are impregnated with nanoparticles, short or continuous fibers, and other additives. The addition of graphene nanoplatelets to PLA makes for a high-quality filament possessing enhanced mechanical, electrical, and thermal properties. Even with the advancement in materials, the optimisation of the process parameter remains the most complex aspect for FFF. Therefore, this study investigates the influence of two under-researched and overlooked processing parameters (material extrusion rates and line widths) on commercially available graphene-enhanced PLA (GPLA). Nine different material extrusion rates (70% to 150%) and five different line widths (0.2 mm to 1 mm) were used to manufacture GPLA specimens using a low-cost, desktop-based 3D printer, as per British and international standards. The study analyses the influence of these two processing parameters on mass, dimensional accuracy, surface texture, and mechanical properties of GPLA specimens. A non-destructive test has also been conducted and correlated with three-point flexural test to establish its applicability in evaluating flexural properties of GPLA. The results how that small line widths provide more accuracy with longer print times whereas large line widths offer more strength with shorter printing times. Increase in material extrusion rates adversely affect the surface finish and hardness but positively influence the flexural strength of GPLA specimens. The study shows that the manipulation of material extrusion rates and line widths can help designers in understanding the limitations of the default printing settings (100% material extrusion rate and 0.4 mm line width) on most desktop 3D printers and identifying the optimal combination to achieve desired properties using the FFF process.

Paul Wood, Fathi Boud, Wayne Carter et al.

The paper discusses an instrumented tapping test method using a CNC machine tool to compare the lubricity of MWFs by cutting threads in a Ti-6Al-4V alloy at low speed. The method uses a spiral flute tap size typical of industrial practice. A soft synchronous tap holder and spindle mounted dynamometer were incorporated on the machine to measure torque and thrust force. The tapping test method was demonstrated on three groups of MWFs that were commercially available and classified by ASTM E2523-13:2018. The method developed stable results free of chip clogging in tool flutes which could otherwise mask their comparative lubricity. The fully synthetic (FS) group displayed the best lubricity and within this group the FS from renewables (FS-bio) was the best overall. The method was shown to be effective in mitigating biasing effects on lubricity performance due to the generous tool chamfer angle tolerance and was practical and economical to implement. The significance of the results is discussed enabling an understanding of friction effects in tapping using a soft synchronous tap holder. A life cycle assessment of each MWF group found total Greenhouse Gas emitted from the FS group was 17% of the hydrocarbon group whilst FS-bio emitted just 7%.

Weizhi Zou, Zhen Wang, Zhenchao Qian et al.

Abstract Vat‐photopolymerization‐based 3D printing enables on‐demand construction of customized objects with scalable production capacity and high precision. Herein, the sol‐gel process for aerogels with digital light processing 3D printing to produce advanced functional materials possessing hierarchical pore structures and complex shapes is combined. It has revealed the temporal evolution of the photorheological behavior of acrylate‐modified silica sols in an acid‐base catalytic procedure, and confirmed that silica aerogels can be fabricated with very low acrylate content. The resulting aerogels are thermostable with intrinsic silica contents, skeletal densities, and physical characteristics similar to those of commercial silica aerogels yet distinct mechanical behaviors. More importantly, the printed silica aerogels can be used as a versatile nanoengineering platform to produce high‐performance and multifunctional interpenetrating phase nanocomposites with complex shapes through programmable post‐printing processes. Epoxy‐based nanocomposites possessing excellent mechanical performance, ionogel‐based conductive nanocomposites with decoupled electrical and mechanical properties, and anti‐swelling hydrogel‐based nanocomposites are demonstrated. The results of this study offer new guidelines for the design and fabrication of novel materials by additive manufacturing.

Glen R. Merritt, Malcolm B. Williams, Paul G. Allison et al.

Additive Friction Stir Deposition (AFSD) is a recent innovation in non-beam-based metal additive manufacturing that achieves layer-by-layer deposition while avoiding the solid-to-liquid phase transformation. AFSD presents numerous benefits over other forms of fusion-based additive manufacturing, such as high-strength mechanical bonding, joining of dissimilar alloys, and high deposition rates. To improve, automate, and ensure the quality, uniformity, and consistency of the AFSD process, it is necessary to control the temperature at the interaction zone and the force applied to the consumable feedstock during deposition. In this paper, real-time temperature and force feedback are achieved by embedding thermocouples into the nonconsumable machine tool-shoulder and estimating the applied force from the motor current of the linear actuator driving the feedstock. Subsequently, temperature and force controllers are developed for the AFSD process, ensuring that the temperature at the interaction zone and the force applied to the feedstock track desired command values. The temperature and force controllers were evaluated separately and together on setpoints and time-varying trajectories. For combined temperature and force control with setpoints selected at a temperature of 420 °C and a force of 2669 N, the average temperature and force tracking errors are 5.4 ± 6.5 °C (1.4 ± 1.6%) and 140.1 ± 213.5 N (5.2 ± 8.0%), respectively.