Hasil untuk "Production capacity. Manufacturing capacity"

Johannes Jakob Meyer, Jacopo Rizzo, Asad Raza et al.

Quantum channel capacities are fundamental to quantum information theory. Their definition, however, does not limit the computational resources of sender and receiver. In this work, we initiate the study of computational quantum capacities. These quantify how much information can be reliably transmitted when imposing the natural requirement that en- and decoding have to be computationally efficient. We focus on the computational two-way quantum capacity and showcase that it is closely related to the computational distillable entanglement of the Choi state of the channel. This connection allows us to show a stark computational capacity separation. Under standard cryptographic assumptions, there exists a quantum channel of polynomial complexity whose computational two-way quantum capacity vanishes while its unbounded counterpart is nearly maximal. More so, we show that there exists a sharp transition in computational quantum capacity from nearly maximal to zero when the channel complexity leaves the polynomial realm. Our results demonstrate that the natural requirement of computational efficiency can radically alter the limits of quantum communication.

Bálint Leon Seregi, Péter Ficzere

Fused deposition modeling (FDM) is a widely used additive manufacturing (AM) technology, favored for its design flexibility and suitability for low-volume production. However, precise cost estimation remains a critical challenge, particularly in industrial environments where decision-making depends on accurate financial assessments. This study proposes a comprehensive, parameter-based cost calculation model for FDM processes, with a special focus on the wear of machine tooling. Unlike conventional methods, the model separates tooling costs from general machine operation costs and introduces a novel approach to nozzle wear estimation based on extruded material volume rather than printing time. The framework incorporates key cost components—including material usage, support removal, machine operation, tooling degradation, and labor—and links them to quantifiable parameters such as part volume, build time, and energy consumption. The methodology was tested across multiple scenarios with different geometries and production volumes, revealing significant differences between time- and volume-based wear calculations. The results demonstrate that the proposed model provides more accurate and adaptable cost predictions, especially in varied production settings. This approach enhances the financial transparency of FDM workflows and supports better-informed decisions in both prototyping and small-batch manufacturing contexts.

Piyao Liu, Linsen Song, Zhenhui Li et al.

Traditional manufacturing methods of wind tunnel nozzles are often cumbersome, time-consuming, and costly. The study of spinning forming technology for wind tunnel nozzles provides a pathway to improve manufacturing efficiency while reducing both cost and production cycle. However, when processing alloy steel (20MnMo), challenges arise due to large deformation, high-temperature loading, and complex wall-thickness control. To address these issues, this work proposes a die-less multi-pass hot spinning process. A three-dimensional dynamic explicit finite element model was developed to simulate the stress–strain evolution during multi-pass spinning. In the first pass, an L9 orthogonal experimental design was applied to analyze the influence of spinning parameters on forming stress and plastic deformation capacity, thereby determining the optimal combination of workpiece rotation speed, axial feed, and radial feed rates. The optimized design strategy was subsequently extended to ten passes. Based on simulation results, hot spinning experiments were conducted, followed by precision machining of the nozzle’s inner and outer surfaces. Inspection results indicated that the deviations in contour and wall thickness between simulation predictions and actual specimens were both less than 0.5%. This study establishes an integrated process route combining numerical simulation, hot spinning, and finishing, providing both theoretical support and practical guidance for the high-precision and high-stability manufacturing of complex thin-walled nozzle structures.

Loucas Papadakis, Stelios Avraam, Muhammad Zulhilmi Mohd Izhar et al.

Additive manufacturing has increasingly become a transformative approach in the design and fabrication of personalized medical devices, offering improved adaptability, reduced production time, and enhanced patient-specific functionality. Within this framework, simulation-driven design plays a critical role in ensuring the structural reliability and performance of orthopedic supports before fabrication. This research study delineates the novel development of a wrist hybrid splint (WHS) which has a simulation-based design and was additively manufactured using fused deposition modeling (FDM). The primary material selected for this purpose was polylactic acid (PLA), recognized for its biocompatibility and structural integrity in medical applications. Prior to the commencement of the actual FDM process, an extensive pre-analysis was imperative, involving the application of nonlinear numerical models aiming at replicating the mechanical response of the WHS in respect to different deposition configurations. The methodology encompassed the evaluation of a sophisticated material model incorporating a damage mechanism which was grounded in experimental data derived from meticulous tensile and three-point bending testing of samples with varying FDM process parameters, namely nozzle diameter, layer thickness, and deposition orientation. The integration of custom subroutines with utility routines was coded with a particular emphasis on maximum stress thresholds to ensure the fidelity and reliability of the simulation outputs on small scale samples in terms of their elasticity and strength. After the formulation and validation of these computational models, a comprehensive simulation of a full-scale, finite element (FE) model of two WHS design variations was conducted, the results of which were aligned with the stringent requirements set forth by the product specifications, ensuring comfortable and safe usage. Based on the results of this study, the final force comparison between the numerical simulation and experimental measurements demonstrated a discrepancy of less than 2%. This high level of agreement highlights the accuracy of the employed methodologies and validates the effectiveness of the WHS simulation and fabrication approach. The research also concludes with a strong affirmation of the material model with a damage mechanism, substantiating its applicability and effectiveness in future manufacturing of the WHS, as well as other orthopedic support devices through an appropriate selection of FDM parameters.

Mirza Manjgo, Srečko Glodež, Gorazd Lojen et al.

Parameters of EPFM are used as relevant parameters in structural integrity assessments. In this research, the fracture toughness of armoured steel was determined. The resulting resistance curves and <i>K</i>_JIC obtained according to the ASTM E1820 standard with normalization, compliance and multi-specimen methods were compared. Also, the <i>K</i>_IC was verified according to the ASTM E399 standard as the most precise method for obtaining the <i>K</i>_IC, which also requires a lot of knowledge. For the experiment, the multi-specimen method was used, which is the most expensive and most accurate method, where the least assumption and crack size is measured on the specimen. A fractographic analysis was also presented, and this heat-treated high-strength steel, which is used for anti-ballistic protection, was fully characterized.

Mariem Abdennadher, Beatriz Garrido, Vicente Albaladejo-Fuentes et al.

Thermal spray techniques allow coatings to be deposited from a wide range of materials with controlled thicknesses, from micrometres to millimetres. For this reason, thermal spraying can optimize performance for diverse applications across industries, ensuring strong adhesion and the durability of coated surfaces. In this work, composite ethylene chlorotrifluoroethylene/ceramic (ECTFE/Al₂O₃) coatings with different ceramic ratios were deposited by plasma spraying. Four coatings were produced by spraying blended powders consisting of pure ECTFE and ECTFE with 5%, 10%, and 15 wt.% Al₂O₃. The effect of varying the ceramic ratio on the coatings’ microstructure and properties was investigated. Morphology and particle size distributions were determined for the raw powders. The microstructural examination of the coatings showed proportional increases in Al₂O₃ content. An improvement in adhesion was achieved with ceramic in the coatings from 5 wt.% Al₂O₃. Enhanced friction coefficients were obtained with ceramic, except for 15 wt.% Al₂O₃. Taber abrasion tests showed a minimal influence on ceramic content.

Dequan Shi, Youen Xu, Xuhui Wang et al.

Robotic grinding serves as a pivotal embodiment and key technological support of Industry 4.0. Elucidating the influence of robotic grinding parameters on the material removal depth (MRD) of 42CrMo steel and optimizing these parameters are critical to enhancing grinding efficiency and quality. In this study, the influences of revolution speed, feed speed, grinding force, and grit designation on MRD and surface Vickers hardness of 42CrMo steel were investigated by using an adaptive electro-hydraulic-actuated triangular abrasive belt in robot grinding. A predictive model for MRD of 42CrMo steel has been established using the orthogonal central composite design method. The results indicated that as the revolution speed or grinding increases, both MRD and surface hardness increase. However, as the revolution speed surpasses 4000 RPM or the grinding force exceeds 60 N, the increase of MRD becomes slower due to the increase in surface hardness. Both the MRD and surface hardness decrease continuously as the feed speed increases, and once it exceeds 15 mm·s⁻¹, the decrease of the MRD becomes slow. The rise in grit designation of the abrasive belt makes the MRD reduce gradually while the surface hardness rises slightly. The correlation coefficient of the predictive model is 0.9387, and the relative error between the predicted and experimental MRD is within 10%, indicating a relatively high accuracy. At the optimal grinding parameters (grinding force of 81 N, revolution speed of 4739 RPM, and feed speed of 7.6 mm·s⁻¹), the maximum MRD of 42CrMo steel achieved by an abrasive belt of 60 grit designation is 0.934 mm. This work provides a basis for high-precision robot abrasive belt grinding of 42CrMo steel.

Garima Thakur, Saxena Nikita, Vinesh Balakrishnan Yezhuvath et al.

The continuous manufacturing of biologics offers significant advantages in terms of reducing manufacturing costs and increasing capacity, but it is not yet widely implemented by the industry due to major challenges in the automation, scheduling, process monitoring, continued process verification, and real-time control of multiple interconnected processing steps, which must be tightly controlled to produce a safe and efficacious product. The process produces a large amount of data from different sensors, analytical instruments, and offline analyses, requiring organization, storage, and analyses for process monitoring and control without compromising accuracy. We present a case study of a cyber–physical production system (CPPS) for the continuous manufacturing of mAbs that provides an automation infrastructure for data collection and storage in a data historian, along with data management tools that enable real-time analysis of the ongoing process using multivariate algorithms. The CPPS also facilitates process control and provides support in handling deviations at the process level by allowing the continuous train to re-adjust itself via a series of interconnected surge tanks and by recommending corrective actions to the operator. Successful steady-state operation is demonstrated for 55 h with end-to-end process automation and data collection via a range of in-line and at-line sensors. Following this, a series of deviations in the downstream unit operations, including affinity capture chromatography, cation exchange chromatography, and ultrafiltration, are monitored and tracked using multivariate approaches and in-process controls. The system is in line with Industry 4.0 and smart manufacturing concepts and is the first end-to-end CPPS for the continuous manufacturing of mAbs.

Juha Lehrbäck, Kaushik Mohanta, Antti V. Vähäkangas

We study non-local or fractional capacities in metric measure spaces. Our main goal is to clarify the relations between relative Hajlasz-Triebel-Lizorkin capacities, potentional Triebel-Lizorkin capacities, and metric space variants of Riesz capacities. As an application of our results, we obtain a characterization of a Hajlasz-Triebel-Lizorkin capacity density condition, which is based on an earlier characterization of a Riesz capacity density condition in terms of Hausdorff contents.

Aqsa Kashaf, Aidan Walsh, Maria Apostolaki et al.

There is anecdotal evidence that attackers use reconnaissance to learn the capacity of their victims before DDoS attacks to maximize their impact. The first step to mitigate capacity reconnaissance attacks is to understand their feasibility. However, the feasibility of capacity reconnaissance in network functions (NFs) (e.g., firewalls, NATs) is unknown. To this end, we formulate the problem of network function capacity reconnaissance (NFCR) and explore the feasibility of inferring the processing capacity of an NF while avoiding detection. We identify key factors that make NFCR challenging and analyze how these factors affect accuracy (measured as a divergence from ground truth) and stealthiness (measured in packets sent). We propose a flexible tool, NFTY, that performs NFCR and we evaluate two practical NFTY configurations to showcase the stealthiness vs. accuracy tradeoffs. We evaluate these strategies in controlled, Internet and/or cloud settings with commercial NFs. NFTY can accurately estimate the capacity of different NF deployments within 10% error in the controlled experiments and the Internet, and within 7% error for a commercial NF deployed in the cloud (AWS). Moreover, NFTY outperforms link-bandwidth estimation baselines by up to 30x.

M. Reza Mohammadi Mozaffar

We study various aspects of capacity of entanglement in the squeezed states of a scalar field theory. This quantity is a quantum informational counterpart of heat capacity and characterizes the width of the eigenvalue spectrum of the reduced density matrix. In particular, we carefully examine the dependence of capacity of entanglement and its universal terms on the squeezing parameter in the specific regimes of the parameter space. Remarkably, we find that the capacity of entanglement obeys a volume law in the large squeezing limit. We discuss how these results are consistent with the behavior of other entanglement measures including entanglement and Renyi entropies. We also comment on the existence of consistent holographic duals for a family of Gaussian states with generic squeezing parameter based on the ratio of entanglement entropy and the capacity of entanglement.

Daniel Butron, Manuel J. Valcárcel-Muñoz, M. Valme García-Moreno et al.

Brandy is a spirit obtained from distilled wine that has an alcohol content equal to or greater than 36 % ABV (Alcohol by Volume). It undergoes an ageing process in oak wood casks with a capacity of up to 1000 L for a minimum of six months. During this process, a series of physicochemical and sensory changes take place that confer the initial wine distillate with a series of improvements to its sensory profile. Such changes are mainly determined by the intrinsic characteristics of the wood and by those associated with the manufacturing process of the casks. The previous use of the casks, ageing time and the alcoholic strength of the wine distillate are also important factors, among others. The casks, which will have previously contained some type of Sherry wine (such as Fino, Amontillado, Oloroso and Pedro Ximénez), are known as Sherry Casks® and they must be used in the production of Brandy de Jerez. During the ageing of Brandy de Jerez, Sherry Casks® contribute to the final brandy via the compounds that are both inherent to the wood they are made of and from the wine that they initially contained and that were retained in the wood pores. The alcohol content of the wine distillate to be aged significantly affects not only the quality of the brandy, but also the financial cost of the process. This study aimed to determine the influence on brandy of the alcoholic strength of wine distillates aged in static ageing systems using Sherry Casks®. Specifically, we assessed the physicochemical composition and sensory profile of Brandy de Jerez made from wine distillates with three different alcoholic strengths (40 %, 55 % and 68 % ABV) and aged for 24 months. The Brandy de Jerez with lower alcoholic strengths (40 % - 55 % ABV) were found to contain a higher concentration of polyphenolic compounds deriving from the wood as well as from the constituents of the cask-seasoning Sherry wine. The brandies with higher alcoholic strengths exhibited a marked colour change, while the 40 % and 55 % ABV brandies were perceived to have the best sensory characteristics.

Miguel Luiz Ribeiro Ferreira, Bruno Sobral Macedo

Neste artigo busca-se avaliar o desempenho dos modelos de curva potencial e exponencial de aprendizagem de um grupo de soldadores, na soldagem de tubulações de aço carbono com o processo TIG. No estudo utilizou-se dois tipos de indicadores: produtividade média e produtividade ideal. A amostra agrupou os dados de produtividade, em faixas de diâmetros, de acordo com os graus de dificuldade estabelecidos pelo código ASME Seção IX. Realizou-se o ajuste dos dados aos modelos potencial e exponencial com o auxílio da ferramenta “Solver” do software Excel. Os resultados revelaram que o modelo exponencial é o que melhor representa o aprendizado. Os dados da produtividade média geraram ajustes de melhor qualidade em comparação com a produtividade ideal.

Peiqi Sun, Michel Grabisch, Christophe Labreuche

Capacity is an important tool in decision-making under risk and uncertainty and multi-criteria decision-making. When learning a capacity-based model, it is important to be able to generate uniformly a capacity. Due to the monotonicity constraints of a capacity, this task reveals to be very difficult. The classical Random Node Generator (RNG) algorithm is a fast-running speed capacity generator, however with poor performance. In this paper, we firstly present an exact algorithm for generating a $n$ elements' general capacity, usable when $n < 5$. Then, we present an improvement of the classical RNG by studying the distribution of the value of each element of a capacity. Furthermore, we divide it into two cases, the first one is the case without any conditions, and the second one is the case when some elements have been generated. Experimental results show that the performance of this improved algorithm is much better than the classical RNG while keeping a very reasonable computation time.

F. Aggogeri, Nicola Pellegrini, F. L. Tagliani

This study aims to present an overall review of the recent research status regarding Machine Learning (ML) applications in machining processes. In the current industrial systems, processes require the capacity to adapt to manufacturing conditions continuously, guaranteeing high performance in terms of production quality and equipment availability. Artificial Intelligence (AI) offers new opportunities to develop and integrate innovative solutions in conventional machine tools to reduce undesirable effects during operational activities. In particular, the significant increase of the computational capacity may permit the application of complex algorithms to big data volumes in a short time, expanding the potentialities of ML techniques. ML applications are present in several contexts of machining processes, from roughness quality prediction to tool condition monitoring. This review focuses on recent applications and implications, classifying the main problems that may be solved using ML related to the machining quality, energy consumption and conditional monitoring. Finally, a discussion on the advantages and limits of ML algorithms is summarized for future investigations.

Maximilian Neitmann, Tom Rothe, Erik Kappel et al.

Composite structures in aeroplanes are often thin-walled and lightweight, resulting in significant compliance, which presents a handling and assembly challenge due to the associated part deformations. In order to counteract these deformations, the parts are held in their specified geometry using stiff and correspondingly heavy fixtures or jigs. Mobile industrial robots are very versatile and widely used in industrial volume production, but they are limited in their payload capacity. High-rate production of large aerospace modules requires highly automated flexible assembly processes. The approach presented in this paper is to combine mobile units with lightweight assembly jigs that have the capability of deformation compensation. The subject of the study is a high-rate assembly process for flap modules using an Autonomous Industrial Mobile Manipulator (AIMM) and a lightweight end effector. The end effector has a shape compensation function, implemented by an integrated Stewart platform, which enables the compensation of manufacturing tolerances as well as gravity effects. The compensation function is used in a closed loop and counteracts shape deviations by appropriate fixture shape adjustments. The paper reports on the conceptual design of the assembly scenario, the design of the end effector, its realization and the successful experimental demonstration at 1:1 scale.

Rishabh Shotri, Koen Faes, Guillaume Racineux et al.

Magnetic pulse welding of overlapping dissimilar metallic sheets is an emerging technique and usually employs flat electromagnetic coils with rectangular-, H-, I-, and E-shaped cross-sections. The asymmetric cross-section of these coils results in a non-uniform electromagnetic field and in a non-uniform connection in the interface between the overlapping sheets. In this article, the use of a novel O-shaped flat coil is proposed to join an aluminium flyer sheet with a target steel sheet. A finite element-based numerical model is developed to calculate the electromagnetic field, flyer velocity, and its gradual impact onto the target, and the deformations of the sheet assembly. The calculated results with the O-shaped coil show a high-intensity electromagnetic field, the concentration of which decreases radially outwards in a uniform manner. The numerically computed and experimentally measured flyer velocity are found to be in fair agreement. The calculated results show a regularly decreasing impact behaviour between the flyer and target and their resulting deformation. The measured results show the formation of an annular ring-shaped joint profile that is generally found to be stronger compared to that obtained with flat coils with a rectangular cross-section.

Federico Bobbio, Margarida Carvalho, Andrea Lodi et al.

The many-to-one stable matching problem provides the fundamental abstraction of several real-world matching markets such as school choice and hospital-resident allocation. The agents on both sides are often referred to as residents and hospitals. The classical setup assumes that the agents rank the opposite side and that the capacities of the hospitals are fixed. It is known that increasing the capacity of a single hospital improves the residents' final allocation. On the other hand, reducing the capacity of a single hospital deteriorates the residents' allocation. In this work, we study the computational complexity of finding the optimal variation of hospitals' capacities that leads to the best outcome for the residents, subject to stability and a capacity variation constraint. First, we show that the decision problem of finding the optimal capacity expansion is NP-complete and the corresponding optimization problem is inapproximable within a certain factor. This result holds under strict and complete preferences, and even if we allocate extra capacities to disjoint sets of hospitals. Second, we obtain analogous computational complexity results for the problem of capacity reduction. Finally, we study the variants of these problems when the goal is to maximize the size of the final matching under incomplete preference lists.

D. -S. Wang

Capacities of quantum channels are fundamental quantities in the theory of quantum information. A desirable property is the additivity for a capacity. However, this cannot be achieved for a few quantities that have been established as capacity measures. Asymptotic regularization is generically necessary making the study of capacities notoriously hard. In this work, by a proper refinement of the physical settings of quantum communication, we prove additive quantities for quantum channel capacities that can be employed for quantum Shannon theorems. This refinement, only a tiny step away from the standard settings, is consistent with the principle of quantum theory, and it further demonstrates von Neumann entropy as the cornerstone of quantum information.

Hanif Putera, Harun Al Rosyid

Kemajuan pesat teknologi berbanding lurus dengan kebutuhan akan energi listrik namun tidak sejalan dengan ketersedian sumberdaya penghasil energi listrik tersebut, untuk itu diperlukan konservasi energi untuk meningkatkan efisiensi dan optimalisasi dari sebuah pembangkit listrik sehingga dapat menunjang peningkatan kebutuhan akan energi listrik. jurnal ini memberikan analisa kelayakan pemanfaatan energi panas sisa proses dari pembangkit listrik geothermal yang menjadi pembangkit utama, Dengan menggunakan System Organic Rankine Cycle (ORC) dengan fluida kerja Iso-Butene, dengan cara menghitung heat balance dari siklus ORC, hingga didapatkan nilai enthalpi dan entropy dari system tersebut sehingga dapat dihitung energi yang dapat dihasilkan oleh turbin ekspansi yang menggerakan Generator, dengan hasil akhir ini tambahan energi yang dihasilkan oleh system ORC