Hasil untuk "Production capacity. Manufacturing capacity"

Hazwan Haini

Abstract This study examines the effect of ICT and human capital on carbon emissions in the ASEAN economies from 1996 to 2019 using panel estimators. The region has implemented several joint-policies on climate change and ICT sector development. On the one hand, ICT can help reduce carbon emissions through innovative clean technology; however, there are concerns on the production and disposal of ICT. Meanwhile, human capital formation can increase carbon emissions as it indirectly affects growth; yet human capital can enhance the absorptive capacity of an economy and enhance the effectiveness of ICT technologies to potentially reduce emissions. Results show that ICT reduce carbon emissions while human capital formation increases it. However, the results vary when examining the impact of ICT and human capital on carbon emissions from manufacturing, residential, transport and other industries: ICT consistently reduces carbon emissions, meanwhile, human capital decreases carbon emissions for manufacturing and other industries. Subsequently, ASEAN policymakers should develop the ICT infrastructure in order to support the reduction of carbon emissions.

Ziheng Zhou, Chaolong Zhang, Shi Chen et al.

Accurate and rapid capacity estimation is essential for efficient battery management in industrial settings particularly for cell grading, pack assembly, and second-life screening where throughput, cost, and energy efficiency are paramount. Conventional approaches require complete discharge cycles, leading to testing times of several hours per cell, which severely limits scalability and increases operational costs. To address this bottleneck, this paper proposes a fast capacity estimation method for battery capacity grading in the production process, which utilizes only the early-stage voltage measurements within the first 300–480 s of the initial discharge cycle during cell grading to accurately predict the cell's nominal capacity, enabling reliable battery capacity grading within minutes instead of hours. Although real-world grading data from production lines are often inaccessible, this first-cycle setup serves as a well-controlled surrogate that replicates key aspects of factory-based capacity labeling. The method exploits early-voltage transients that encode degradation-sensitive electrochemical signatures such as lithium inventory loss and solid-electrolyte interphase (SEI) evolution arising from microscopic changes in charge-transfer resistance and ion transport dynamics. From this short window, we extract physically interpretable health indicators (HIs) that reflect underlying aging mechanisms. A nonlinear feature enhancement strategy is then applied to amplify subtle capacity-related patterns while suppressing manufacturing-induced variability. These engineered features feed into a Multi-Decision Ensemble Learning (MDEL) architecture, which adaptively fuses multiple regression pathways to improve robustness across diverse cell chemistries and aging stages. Evaluated on both in-lab cells, the public CALCE and MIT dataset spanning fresh to end-of-life capacity conditions, the proposed approach achieves a mean absolute error (MAE) of ≤0.039,1 Ah (≤1.63% of nominal capacity), which is comparable to the methods with complete cycle data while reducing testing time by over 80%. This enables reliable capacity assessment in minutes rather than hours, offering a practical, scalable solution for high-throughput battery manufacturing, precise pack matching, and rapid second-life qualification.

C. E. Corcione, F. Gervaso, F. Scalera et al.

Abstract The goal of the work was the manufacturing of hydroxyapatite microsphere/polylactic acid (PLA) scaffolds by means of fused deposition modelling (FDM). Micrometer-sized hydroxyapatite spheres synthesized by spray drying (sdHA), were dispersed in PLA by extrusion compounding. Composite filaments were obtained from extrusion which were used in FDM 3D printing for the production of macroporous scaffolds. The sdHA microspheres were used in the composite in order to improve the biomimicry and the bioactivity of the 3D printed scaffold to increase the bone regeneration capacity. Morphological, thermal, physical and mechanical characterizations were performed on the 3D printed composites. Pure PLA scaffolds were 3D printed and used as a reference. Thermal analyses, TGA and DSC evidenced that the glass transition temperature and the degree of crystallinity of PLA were not influenced by the presence of sdHA. Morphological analysis showed a smooth surface of the printed samples when pure PLA was used. A rough surface was found on the PLA/sdHA composites, confirming, the homogeneous dispersion of the ceramic phase in the polymeric matrix. The higher porosity of the composite samples compared to PLA ones, most likely caused a decrease of the mechanical performances of the PLA/sdHA scaffolds. Composite scaffolds displayed stiffness values compatible with that of bone tissue.

Thomas O. Butler, R. Kapoore, S. Vaidyanathan

A switch from a petroleum-based to a biobased economy requires the capacity to produce both high-value low-volume and low-value high-volume products. Recent evidence supports the development of microalgae-based microbial cell factories with the objective of establishing environmentally sustainable manufacturing solutions. Diatoms display rich diversity and potential in this regard. We focus on Phaeodactylum tricornutum, a pennate diatom that is commonly found in marine ecosystems, and discuss recent trends in developing the diatom chassis for the production of a suite of natural and genetically engineered products. Both upstream and downstream developments are reviewed for the commercial development of P. tricornutum as a cell factory for a spectrum of marketable products.

Zhen Wu, Qi Zhao, Zhihao Ma

The super-additivity of quantum channel capacity is an important feature of quantum information theory different from classical theory, which has been attracting attention. Recently a special channel called ``platypus channel'' exhibits super-additive quantum capacity when combined with qudit erasure channels. Here we consider the ``generalized platypus channel'', prove that it has computable channel capacities, such as both private and classical capacity equal to $1$, and in particular, the generalized platypus channel still displays the super-additivity of quantum capacity when combined with qudit erasure channels and multilevel amplitude damping channels respectively.

Sujeethkumar Prithiviraj, Alejandro Garcia Garcia, Karin Linderfalk et al.

Tissue engineering strategies predominantly rely on the production of living substitutes, whereby implanted cells actively participate in the regenerative process. Beyond cost and delayed graft availability, the patient-specific performance of engineered tissues poses serious concerns on their clinical translation ability. A more exciting paradigm consists in exploiting cell-laid, engineered extracellular matrices (eECMs), which can be used as off-the-shelf materials. Here, the regenerative capacity solely relies on the preservation of the eECM structure and embedded signals to instruct an endogenous repair. We recently described the possibility to exploit custom human stem cell lines for eECM manufacturing. In addition to the conferred standardization, the availability of such cell lines opened avenues for the design of tailored eECMs by applying dedicated genetic tools. In this study, we demonstrated the exploitation of CRISPR/Cas9 as a high precision system for editing the composition and function of eECMs. Human mesenchymal stromal/stem cell (hMSC) lines were modified to knock out vascular endothelial growth factor (VEGF) and Runt-related transcription factor 2 (RUNX2) and assessed for their capacity to generate osteoinductive cartilage matrices. We report the successful editing of hMSCs, subsequently leading to targeted VEGF and RUNX2-knockout cartilage eECMs. Despite the absence of VEGF, eECMs retained full capacity to instruct ectopic endochondral ossification. Conversely, RUNX2-edited eECMs exhibited impaired hypertrophy, reduced ectopic ossification, and superior cartilage repair in a rat osteochondral defect. In summary, our approach can be harnessed to identify the necessary eECM factors driving endogenous repair. Our work paves the road toward the compositional eECMs editing and their exploitation in broad regenerative contexts.

Xuemei Xie, Qiwei Zhu, Ruoying Wang

Green process innovation has been seen as a key strategy for manufacturing firms to pursue sustainable development. Yet, how to help manufacturing firms eliminate bottlenecks when implementing green process innovation remains poorly understood. To address this issue, the current study, which is anchored in the government incentive perspective, examines the drivers, contingent conditions, and consequences of green process innovation by using the panel data of manufacturing‐listed firms in China from 2013 to 2017. The results present valuable findings: (a) green subsidies are positively related to two dimensions of green process innovation, namely, cleaner production technology and end‐of‐pipe technology; (b) both cleaner production technology and end‐of‐pipe technology are positively related to firms' green image; (c) a firm's cleaner production technology mediates the relationship between green subsidies and its green image; and (d) higher absorptive capacity strengthens the indirect effect of green subsidies on a firm's green image via cleaner production technology. Our results provide meaningful theoretical and practical implications by revealing the benefits of green subsidies through green process innovation by leveraging levels of absorptive capacity.

David Tremblet, Simon Thevenin, A. Dolgui

ABSTRACT A production plan gives the quantity of products to release on the shop floor in each period, where a period may represent a week or a month. The plan is the basis for negotiating order acceptance and delivery dates with customers and suppliers. The production plan must respect the available capacity on the shop floor, as underloading the shop floor leads to a loss of opportunity, and thus, a loss of competitiveness for the company. To properly manage the production capacity while negotiating with suppliers and customers, the production planners need a tool to accurately estimate the capacity consumption in each period. The computation of capacity consumption requires creating a detailed production schedule which is a complex task. Algorithms that find close to optimal schedules in a complex manufacturing environment are often time-consuming, which is impractical in a negotiation context. We investigate machine learning models to predict capacity consumption. We consider a flexible job-shop as commonly encountered in practice, and proposed several machine learning models. Namely, several variants of linear regression, decision trees, and artificial neural networks. Numerical experiments showed that our models outperform those found by both an exact approach and dispatching rules when computation time is short.

Towfique Rahman, F. Taghikhah, S. Paul et al.

The current COVID-19 pandemic has hugely disrupted supply chains (SCs) in different sectors globally. The global demand for many essential items (e.g., facemasks, food products) has been phenomenal, resulting in supply failure. SCs could not keep up with the shortage of raw materials, and manufacturing firms could not ramp up their production capacity to meet these unparalleled demand levels. This study aimed to examine a set of congruent strategies and recovery plans to minimize the cost and maximize the availability of essential items to respond to global SC disruptions. We used facemask SCs as an example and simulated the current state of its supply and demand using the agent-based modeling method. We proposed two main recovery strategies relevant to building emergency supply and extra manufacturing capacity to mitigate SC disruptions. Our findings revealed that minimizing the risk response time and maximizing the production capacity helped essential item manufacturers meet consumers’ skyrocketing demands and timely supply to consumers, reducing financial shocks to firms. Our study suggested that delayed implementation of the proposed recovery strategies could lead to supply, demand, and financial shocks for essential item manufacturers. This study scrutinized strategies to mitigate the demand–supply crisis of essential items. It further proposed congruent strategies and recovery plans to alleviate the problem in the exceptional disruptive event caused by COVID-19.

Kristian Moring, Christoph Scheven

We consider different notions of capacity related to the parabolic $p$-Laplace equation. Our focus is on a variational notion, which is consistent in the full range $1<p<\infty$. For such a notion we show some basic properties as well as its connection to other notions of capacity presented in the literature, and to a certain parabolic version of the Hausdorff measure. As applications, we use the introduced variational notion of capacity to study polar sets and removability results for supersolutions.

Raj Bridgelall

Solid-state batteries (SSBs) offer significant improvements in safety, energy density, and cycle life over conventional lithium-ion batteries, with promising applications in electric vehicles and grid storage due to their non-flammable electrolytes and high-capacity lithium metal anodes. However, challenges such as interfacial resistance, low ionic conductivity, and manufacturing scalability hinder their commercial viability. This study conducts a comprehensive scientometric analysis, examining 131 peer-reviewed SSB research articles from IEEE Xplore and Web of Science databases to identify key thematic areas and bibliometric patterns driving SSB advancements. Through a detailed analysis of thematic keywords and publication trends, this study uniquely identifies innovations in high-ionic-conductivity solid electrolytes and advanced cathode materials, providing actionable insights into the persistent challenges of interfacial engineering and scalable production, which are critical to SSB commercialization. The findings offer a roadmap for targeted research and strategic investments by researchers and industry stakeholders, addressing gaps in long-term stability, scalable production, and high-performance interface optimization that are currently hindering widespread SSB adoption. The study reveals key advances in electrolyte interface stability and ion transport mechanisms, identifying how solid-state electrolyte modifications and cathode coating methods improve charge cycling and reduce dendrite formation, particularly for high-energy-density applications. By mapping publication growth and clustering research themes, this study highlights high-impact areas such as cycling stability and ionic conductivity. The insights from this analysis guide researchers toward impactful areas, such as electrolyte optimization and scalable production, and provide industry leaders with strategies for accelerating SSB commercialization to extend electric vehicle range, enhance grid storage, and improve overall energy efficiency.

Dominik Pietsch, Marvin Matthes, Uwe Wieland et al.

The manufacturing industry must maintain high-quality standards while meeting customer demands for customization, reduced carbon footprint, and competitive pricing. To address these challenges, companies are constantly improving their production processes using quality management tools. A crucial aspect of this improvement is the root cause analysis of manufacturing defects. In recent years, there has been a shift from traditional knowledge-driven approaches to data-driven approaches. However, there is a gap in the literature regarding a systematic overview of both methodological types, their overlaps, and the challenges they pose. To fill this gap, this study conducts a scoping literature review of root cause analysis in manufacturing, focusing on both data-driven and knowledge-driven approaches. For this, articles from IEEE Xplore, Scopus, and Web of Science are examined. This review finds that data-driven approaches have become dominant in recent years, with explainable artificial intelligence emerging as a particularly strong approach. Additionally, hybrid variants of root cause analysis, which combine expert knowledge and data-driven approaches, are also prevalent, leveraging the strengths of both worlds. Major challenges identified include dependence on expert knowledge, data availability, and management issues, as well as methodological difficulties. This article also evaluates the potential of artificial intelligence and hybrid approaches for the future, highlighting their promises in advancing root cause analysis in manufacturing.

Clarissa Maria Rodrigues de Oliveira, Paula Cristina de Amorim Andrade, Maria Socorro Ferreira dos Santos

O intenso aumento da geração de resíduos sólido associado as práticas inadequadas de descartes corroboram com a necessidade do desenvolvimento de alternativas para o reaproveitamento de materiais de maneira a mitigar, sobretudo, os danos ao meio ambiente. Dentre esses materiais, destaca-se o óleo vegetal residual (OVR), o qual possui um elevado potencial poluidor e é amplamente utilizado em estabelecimentos comerciais e usuários domésticos. Nesse sentido, o presente estudo apresenta a avaliação da cadeia reversa do OVR destinado à fabricação de biodiesel mediante a realização de uma revisão sistemática na literatura. Dessa maneira, foi possível investigar e levantar informações acerca dos fatores relativos à articulação e instituição dessa cadeia, bem como contribuir para mitigar as lacunas nas discussões científicas presente na literatura sobre a temática estudada.

Andreas Hofmann, Tim Grotz, Nico Köstler et al.

Achieving a homogeneous and uniform powder bed surface as well as a defined, uniform layer thickness is crucial for achieving reproducible component properties that meet requirements when powder bed fusion of metals with a laser beam. The existing recoating processes cause wear of the recoater blade due to protruded, melted obstacles, which affects the powder bed surface quality locally. Impairments to the powder bed surface quality have a negative effect on the resulting component properties such as surface quality and relative density. This can lead either to scrapped components or to additional work steps such as surface reworking. In this work, an electric smoother is presented with which a wear-free and contactless smoothing of the powder bed can be realized. The achievable powder bed surface quality was analyzed using optical profilometry. It was found that the electric smoother can compensate for impairments in the powder bed surface and achieve a reproducible surface quality of the powder bed regardless of the initial extent of the impairments. Consequently, the electric smoother offers a promising opportunity to reduce the scrap rate in PBF-LB/M and to increase component quality.

Junaid K. Ahmed, Nihat Atmaca, Ganjeena J. Khoshnaw

In light of the growing global issue of construction waste management, disposal, and environmental impact, this study uniquely focuses on investigating the viability of recycled brick waste powder (RBWP) as a replacement for conventional industrial wastes like ground granulated blast furnace slag (GGBS) and fly ash (FA) for manufacturing engineered geopolymer composites (EGC). The EGC mixes was prepared by utilizing polyvinyl alcohol (PVA) fiber with a 12 mm length, and 40 micrometers were used. The classes F of FA and GGBS replaced by the RBWP in EGC by 0%, 20%, 40%, 50%, 60%, 80%, and 100%. A total of 14 different EGC mixtures were prepared. The flowability, initial and final setting times, density, compressive strength, direct tensile strength, and tensile stress-strain diagrams of EGC were examined. A microstructural characterization was carried out, involving the use of X-ray diffraction (XRD) and scanning electron microscopy (SEM) techniques. The results indicated that the inclusion of RBWP in the FA-based EGC mixtures resulted in decreasing the flowability in terms of the extra usage of superplasticizer from 0 to 6% and the initial and final set by about 90% and 91%, respectively. Admittedly, there was a significant enhancement observed in the compressive strength, tensile strength, and tensile strain capacity, with improvements of 25%, 29%, and 172%, respectively, when RBWP completely replaced FA. However, the flowability, setting time, density, compressive strength, and tensile strength of the EGC decreased when GGBS was completely replaced by RBWP. However, there was a remarkable improvement in the ultimate tensile strain, which increased by a factor of 11 compared to fully GGBS-EGC. Moreover, the microstructural characterization analysis revealed that the RBWP exhibited effective geopolymerization within the EGC mixtures. These results demonstrate the interesting prospective application of RBWP as an effective replacement for industrial wastes in the production of EGC.

Reza Sallakhniknezhad, Hossein Ahmadian, Tianfeng Zhou et al.

Recently, there has been much scholarly research on the applications of CNTs in various fields which can be attributed to their outstanding properties. For that matter, machining processes as the backbone of manufacturing technologies have also benefited greatly from the introduction of CNTs. However, there is a lack of papers that provide a holistic overview on potential applications, which impedes focused and robust research in their application. In this work, after providing an outline of the methods used in increasing the productivity of machining processes, we will review the ways in which CNTs, known for their remarkable mechanical, chemical, electrical, and thermal characteristics, enhance the productivity of machining processes. We emphasize fit-for-purpose applications to determine the fate of CNTs use in machining processes. We examine the applications of CNTs in enhancing the mechanical characteristics of cutting tools, which include increased wear resistance, strength, and thermal conductivity, thereby extending tool life and performance. Additionally, this work highlights the application of nanofluids in MQL systems, where CNTs play a crucial role in reducing friction and enhancing thermal management, leading to reduced lubricant usage while maintaining cooling and lubrication effectiveness.

Hao Wu, Zisong Lu, Steven Hill et al.

This study evaluates the effectiveness of these conventional heating methods, commonly adopted in the industry with long durations (typically one hour), in comparison to newer, potentially more efficient approaches such as induction coil heating, infrared module heating, and infrared furnaces that can perform solution heat treatment in significantly shorter times (5 to 20 min). The properties of the edge and centre regions of the solution-treated billets, including the state of precipitates, grain structures, and Vickers hardness, are investigated and compared. Results have shown that the 7075 billets heated by conventional heating methods sufficiently dissolved the stable precipitates, achieving hardness ranging from 137 to 141 HV, in contrast to the benchmark unheated, as-received sample of approximately 70 HV. In the meantime, the induction coil and infrared furnace demonstrate notable effectiveness, achieving hardness between 126 and 135 HV. The average grain sizes in the centre and edge regions for all samples are measured as 3 and 8 µm, respectively. However, the impact of the grain size on the hardness is negligible compared to the impact of the precipitates. Finite element (FE) modelling comparing the slowest heating method—the electric furnace—and the fastest heating method—induction coil heating—reveals the latter could heat the billet up to 450 °C at a rate ten times faster than the electric furnace. This study highlights the potential of novel heating techniques in promoting the efficiency of heat treatment processes for 7075 aluminium alloys.

Gabriel Ballot, Vadim Malvone, Jean Leneutre et al.

Model checking strategic abilities was successfully developed and applied since the early 2000s to ensure properties in Multi-Agent System. In this paper, we introduce the notion of capacities giving different abilities to an agent. This applies naturally to systems where multiple entities can play the same role in the game, such as different client versions in protocol analysis, different robots in heterogeneous fleets, different personality traits in social structure modeling, or different attacker profiles in a cybersecurity setting. With the capacity of other agents being unknown at the beginning of the game, the longstanding problems of imperfect information arise. Our contribution is the following: (i) we define a new class of concurrent game structures where the agents have different capacities that modify their action list and (ii) we introduce a logic extending Alternating-time Temporal Logic to reason about these games.

Ali Zardoshtian, Reza Esmaeilizadeh, Mazyar Ansari et al.

Laser-directed energy deposition (LDED) is a promising technology for coating, repairing, and building near-net-shape 3D structures. However, the processing of copper alloys, specifically, has presented a significant challenge due to their low laser absorptivity at the 1060 nm laser wavelength and high thermal conductivity. This study undertook a methodical examination by employing a 2 kW disk laser, operating at a wavelength of 1064 nm, and a coaxial nozzle head to comprehensively examine the processability of the highly conductive CuCrZr alloy for expanding the range of materials that can be successfully processed using LDED. The investigation focuses not only on optimizing the input process parameters that are the laser power, scanning speed, powder feed rate, and overlap ratio, but also on planning the toolpath trajectory, as these factors were found to exert a substantial influence on processability, geometrical accuracy, and the occurrence of defects such as lack of fusion. The optimal toolpath trajectory discovered involved implementing a zigzag strategy combined with a 90° rotation of the scanning direction. Additionally, a start point rotation was considered between each layer to even out the deposition of the layers. Moreover, a contour with a radial path at the corners was introduced to enhance the overall trajectory. Based on the hierarchal experimental study, the appropriate ranges for the key process parameters that leads to 99.99% relative density have been identified. They were found to be from 1100 up to 2000 W for the laser power (P), and from 0.003 up to 0.016 g/mm for the amount of powder that is fed to the melt pool distance (F/V). Regarding the influence of process parameters on the microstructure of the samples with equal deposition height, it was observed that varying combinations of process parameters within the optimal processing window resulted in variations in grain size ranging from 105 to 215 µm.

Florian Graf, Sebastian Zeng, Bastian Rieck et al.

We study the excess capacity of deep networks in the context of supervised classification. That is, given a capacity measure of the underlying hypothesis class - in our case, empirical Rademacher complexity - to what extent can we (a priori) constrain this class while retaining an empirical error on a par with the unconstrained regime? To assess excess capacity in modern architectures (such as residual networks), we extend and unify prior Rademacher complexity bounds to accommodate function composition and addition, as well as the structure of convolutions. The capacity-driving terms in our bounds are the Lipschitz constants of the layers and an (2, 1) group norm distance to the initializations of the convolution weights. Experiments on benchmark datasets of varying task difficulty indicate that (1) there is a substantial amount of excess capacity per task, and (2) capacity can be kept at a surprisingly similar level across tasks. Overall, this suggests a notion of compressibility with respect to weight norms, complementary to classic compression via weight pruning. Source code is available at https://github.com/rkwitt/excess_capacity.