Hasil untuk "Production capacity. Manufacturing capacity"

M. Kabeyi, O. Olanrewaju

Biogas is competitive, viable, and generally a sustainable energy resource due to abundant supply of cheap feedstocks and availability of a wide range of biogas applications in heating, power generation, fuel, and raw materials for further processing and production of sustainable chemicals including hydrogen, and carbon dioxide and biofuels. The capacity of biogas based power has been growing rapidly for the past decade with global biogas based electricity generation capacity increasing from 65 GW in 2010 to 120 GW in 2019 representing a 90% growth. This study presents the pathways for use of biogas in the energy transition by application in power generation and production of fuels. Diesel engines, petrol or gasoline engines, turbines, microturbines, and Stirling engines offer feasible options for biogas to electricity production as prme movers. Biogas fuel can be used in both spark ignition (petrol) and compression ignition engines (diesel) with varying degrees of modifications on conventional internal combustion engines. In internal combustion engines, the dual-fuel mode can be used with little or no modification compared to full engine conversion to gas engines which may require major modifications. Biogas can also be used in fuel cells for direct conversion to electricity and raw material for hydrogen and transport fuel production which is a significant pathway to sustainable energy development. Enriched biogas or biomethane can be containerized or injected to gas supply mains for use as renewable natural gas. Biogas can be used directly for cooking and lighting as well as for power generation and for production of Fischer-Tropsch (FT) fuels. Upgraded biogas/biomethane which can also be used to process methanol fuel. Compressed biogas (CBG) and liquid biogas (LBG) can be reversibly made from biomethane for various direct and indirect applications as fuels for transport and power generation. Biogas can be used in processes like combined heat and power generation from biogas (CHP), trigeneration, and compression to Bio-CNG and bio-LPG for cleaned biogas/biomethane. Fuels are manufactured from biogas by cleaning, and purification before reforming to syngas, and partial oxidation to produce methanol which can be used to make gasoline. Syngas is used in production of alcohols, jet fuels, diesel, and gasoline through the Fischer-Tropsch process.

Guanghui Zhou, Chao Zhang, Zhi Li et al.

Rapid advances in new generation information technologies, such as big data analytics, internet of things (IoT), edge computing and artificial intelligence, have nowadays driven traditional manufacturing all the way to intelligent manufacturing. Intelligent manufacturing is characterised by autonomy and self-optimisation, which proposes new demands such as learning and cognitive capacities for manufacturing cell, known as the minimum implementation unit for intelligent manufacturing. Consequently, this paper proposes a general framework for knowledge-driven digital twin manufacturing cell (KDTMC) towards intelligent manufacturing, which could support autonomous manufacturing by an intelligent perceiving, simulating, understanding, predicting, optimising and controlling strategy. Three key enabling technologies including digital twin model, dynamic knowledge bases and knowledge-based intelligent skills for supporting the above strategy are analysed, which equip KDTMC with the capacities of self-thinking, self-decision-making, self-execution and self-improving. The implementing methods of KDTMC are also introduced by a thus constructed test bed. Three application examples about intelligent process planning, intelligent production scheduling and production process analysis and dynamic regulation demonstrate the feasibility of KDTMC, which provides a practical insight into the intelligent manufacturing paradigm.

Anthony M. Polloreno

In noisy physical reservoirs, the classical information-processing capacity $C_{\mathrm{ip}}$ quantifies how well a linear readout can realize tasks measurable from the input history, yet $C_{\mathrm{ip}}$ can be far smaller than the observed rank of the readout covariance. We explain this ``missing capacity'' by introducing the innovation capacity $C_{\mathrm{i}}$, the total capacity allocated to readout components orthogonal to the input filtration (Doob innovations, including input-noise mixing). Using a basis-free Hilbert-space formulation of the predictable/innovation decomposition, we prove the conservation law $C_{\mathrm{ip}}+C_{\mathrm{i}}=\mathrm{rank}(Σ_{XX})\le d$, so predictable and innovation capacities exactly partition the rank of the observable readout dimension covariance $Σ_{XX}\in \mathbb{R}^{\rm d\times d}$. In linear-Gaussian Johnson-Nyquist regimes, $Σ_{XX}(T)=S+T N_0$, the split becomes a generalized-eigenvalue shrinkage rule and gives an explicit monotone tradeoff between temperature and predictable capacity. Geometrically, in whitened coordinates the predictable and innovation components correspond to complementary covariance ellipsoids, making $C_{\mathrm{i}}$ a trace-controlled innovation budget. A large $C_{\mathrm{i}}$ forces a high-dimensional innovation subspace with a variance floor and under mild mixing and anti-concentration assumptions this yields extensive innovation-block differential entropy and exponentially many distinguishable histories. Finally, we give an information-theoretic lower bound showing that learning the induced innovation-block law in total variation requires a number of samples that scales with the effective innovation dimension, supporting the generative utility of noisy physical reservoirs.

Ziang Lei, Jianhua Shi, Ziren Luo et al.

While Industry 4.0 improves human productivity, it also raises sustainability and social challenges. Industry 5.0, as a supplement and logical continuation of the Industry 4.0 paradigm, focuses on the development of a human-centric, sustainable, and resilient manufacturing system. This paper reviews the existing literature. First, it discusses the definition and implementation framework for Industry 5.0. Then, it expounds the application status of Industry 5.0 in the field of intelligent manufacturing from four perspectives: digital manufacturing and intelligence, human-centric intelligent manufacturing and production process management, decentralized and resilient production, and sustainable production. It summarizes the role of Industry 5.0 technology in various intelligent manufacturing scenarios, as well as the challenges it faces, and concludes by analyzing Industry 5.0’s potential development direction and future technologies. This paper believes that the application of Industry 5.0 technology will effectively improve the production capacity of intelligent manufacturing systems and promote the development of intelligent manufacturing systems in a safe, efficient, sustainable and resilient direction; the development of Industry 5.0 will focus on giving full play to human creativity, avoiding repetitive labor through human-robot collaboration, and thereby realizing human value.

Luis M. López-López, Geovanny Maldonado, Cesar Paltán-Zhingre et al.

Traditional manufacturing processes yield plastic containers in large batches, even for minimal production runs, resulting in elevated production costs. Three-dimensional printing has emerged as a viable alternative for very low production volumes, offering properties comparable to traditional methods at significantly reduced costs. To assess the tensile strength, specimens printed with identical geometric parameters to the 3D-printed containers were tested according to ASTM D638 standards, enabling the determination of the stress–strain curve behavior. A compression test was conducted on containers obtained from both manufacturing processes to establish their respective resistance and deformation characteristics. The results revealed a 67% difference in resistance, indicating greater rigidity in the 3D-printed container, and a higher deformation in the blow-molded container, reaching up to 4 mm in height without fracture. Similarly, impact resistance was analyzed using finite element analysis with Ls-Dyna software, showing deformation differences of 0.91% and stress differences of 2.15%. Therefore, 3D printing presents itself as a compelling alternative for the fabrication of plastic containers in small production runs.

Ali Dinc, Suleiman Obeidat, Ali Mamedov et al.

This study evaluates the surface roughness of micromilled 316L stainless steel parts fabricated via fused filament fabrication (FFF) and sintering, establishing statistical links between additive manufacturing and post-machining parameters. The surface roughness of the final part is affected by both 3D printing and micromachining parameters. The presented work has direct practical relevance because micromilled 316L stainless steel components are frequently used in applications such as lab-on-a-chip (LOC) devices and micro-electro-mechanical systems (MEMS), where fatigue behavior and the rheological behavior of fluid flow play critical roles. Both fluid flow and fatigue performance of micromilled components are highly dependent on surface integrity, including surface roughness, residual stresses, and microstructure. Specimens were produced using a 3D printer, under controlled layer thicknesses, raster angles, and fabrication directions, followed by a sintering process for the 3D-printed parts. The sintered parts are then micromilled at varying cutting directions (Angle Cut). Surface roughness (Ra) was measured with a profilometer, generating 34 experimental datasets analyzed through correlation and regression modeling. Cutting direction (Angle Cut) exhibited the strongest positive correlation with Ra (r = 0.486, <i>p</i> = 0.004), followed by layer thickness (r = 0.326, <i>p</i> = 0.060), whereas raster angle and fabrication direction had minimal influence. The multiple linear regression model accounted for 33.5% of Ra variance (R² = 0.335, <i>p</i> = 0.0158), highlighting that fine-layer deposition and alignment of tool paths with filament orientation significantly improve post-machined surface quality. Results confirm that additive-induced anisotropy persists after sintering, affecting chip formation and surface morphology during micromilling. The novelty of this work lies in its integrated hybrid framework, linking metal FFF process parameters, fabrication direction, and machining outcomes through a unified statistical approach. This foundation supports machine-learning-based prediction and hybrid process optimization in metal FFF systems, providing guidance for high-quality additive–subtractive manufacturing.

Martina De Giovanni, Mariangela Lazoi, Romeo Bandinelli et al.

In the shift toward sustainable and resource-efficient manufacturing, Artificial Intelligence (AI) is playing a transformative role in overcoming the limitations of traditional production scheduling methods. This study, based on a Systematic Literature Review (SLR), explores how AI techniques enhance Advanced Planning and Scheduling (APS) systems, particularly under finite-capacity constraints. Traditional scheduling models often overlook real-time resource limitations, leading to inefficiencies in complex and dynamic production environments. AI, with its capabilities in data fusion, pattern recognition, and adaptive learning, enables the development of intelligent, flexible scheduling solutions. The integration of metaheuristic algorithms—especially Ant Colony Optimization (ACO) and hybrid models like GA-ACO—further improves optimization performance by offering high-quality, near-optimal solutions without requiring extensive structural modeling. These AI-powered APS systems enhance scheduling accuracy, reduce lead times, improve resource utilization, and enable the proactive identification of production bottlenecks. Especially relevant in high-variability sectors like fashion, these approaches support Industry 5.0 goals by enabling agile, sustainable, and human-centered manufacturing systems. The findings have been highlighted in a structured framework for AI-based APS systems supported by metaheuristics that compares the Industry 4.0 and Industry 5.0 perspectives. The study offers valuable implications for both academia and industry: academics can gain a synthesized understanding of emerging trends, while practitioners are provided with actionable insights for deploying intelligent planning systems that align with sustainability goals and operational efficiency in modern supply chains.

Daniel Campbell

In this paper we give an elementary proof that sets of zero $p,1$-Sobolev-Lorentz capacity are $\mathcal{H}^{n-p}$-null sets independently of non-linear potential theory. We further show that there exists a set of Sobolev-Lorentz-$(p,1)$ capacity equal zero with Hausdorff dimension equal $n-p$.

Jie Cheng, Nai-ru Xu, Noor Ullah Khan et al.

In the contemporary digital landscape, the focus of manufacturing companies on green innovation has garnered attention in the business and academic realms. Nonetheless, the existing research system for manufacturers lacks a systematic study on how artificial intelligence literacy may bolster green innovation efforts. This study endeavors to construct a theoretical framework for artificial intelligence literacy, green information system, green absorptive capacity, and green innovation with respect to the dynamic capability theory and conducting empirical analysis utilizing survey data obtained from 288 ISO14001 manufacturing firms in Malaysia. The findings revealed that artificial intelligence literacy is a significant determinator of green absorptive capacity, the positive outcome of green absorptive capacity is green innovation, and the positive link between artificial intelligence literacy and green absorptive capacity is moderated by green information system. However, artificial intelligence literacy didn't exhibit a direct relationship with green innovation, even when considering green absorptive capacity as a mediator. These results not only offer compelling insights into the link between artificial intelligence literacy and green innovation, but also hold significant implications for academic research and policymaking concerning sustainable development and cleaner manufacturing production.

Liping Zhou, Zhibin Jiang, Na Geng et al.

In the context of Industry 4.0, the manufacturing sector is moving from automation towards intelligence. The application of new generation information and communication technologies (ICTs) improves the interconnection and transparency of intelligent manufacturing (IM) systems, which will change how information interacts and work is done, thus changing how work should be managed. These changes require the following characteristics for IM production and operations management (POM): integration, flexibility and networking, autonomous and collaborative decision-making, learning-based operations management, self-optimisation and adaptability, and proactive decision-making. This paper presents the state of the art, current challenges, and future directions of IM-related POM research from the perspectives of these characteristics through a systematic literature review. Descriptive and thematic analyses of 208 research articles published between 2005 and 2020 are provided. The review and discussions focus on five research themes, i.e. value creation mechanisms, resource configuration and capacity planning, production planning, scheduling, and logistics.

Ege Duran, Cemalettin Ozturk, Barry O'Sullivan

The economic and social crises of the past two decades have prompted a shift from traditional, isolated manufacturing to shared manufacturing. The COVID-19 pandemic has accelerated this trend, with more enterprises adopting the shared manufacturing (ShardMfg) approach. This technique aims to reduce risks and to strengthen the resilience of manufacturing supply chains. The SharedMfg paradigm is a service-oriented manufacturing system that delivers collaborative production capacities on demand, relying on three pillars: Technology, Society, and Economy. SharedMfg involves extensively sharing manufacturing capacities through peer-to-peer cooperation, improving resource utilisation, reducing costs through economies of scale, supporting SMEs, and making the industry more competitive. While cloud software platforms are used for auctioning manufacturing capacities, there is ambiguity about the concept, confusion over terminology, and a lack of clear understanding of its potential and challenges regarding its implementation. Efficient planning and scheduling of shared manufacturing resources in a holistic way is a significant gap in these platforms. This paper aims to conduct an in-depth investigation, including a comparative analysis of different service-oriented production systems and terminology, while emphasising the critical importance of the shared factory business model using a systematic literature review method (SLR).

Carolina Soto Carrión, Wilber Jiménez Mendoza, Iris Perez-Almeida et al.

Grasslands are characterized by native non-forest vegetation, occupying a large land area worldwide. The study was conducted in the Andean National Sanctuary of Ampay, Peru, 3,500-4,500 masl. The purpose has been to evaluate the relationship between biomass, forage productive potential, degree of soil erosion, presence of valuable species for grazing. The three-step (Parker) method was used for natural pastures and the linear intercept for vegetation. From thirteen zones, distributed in three study areas (Yanacocha, Uspacocha, Faccha), herbaceous vegetation frequency results were obtained. The grazing carrying capacity was 1.2 animal units/ha/year. The most productive area with species desirable for livestock was Uspacocha, which had better soil conditions without overgrazing, while the least productive was Yanacocha. The predominant families in herbaceous plant associations were Rosaceae, Poaceae, Asteraceae; the study area was in regular conservation condition with few eroded areas. At present, the inhabitants of the communities surrounding the study area use many of the plant species for feeding, medicinal, fuel, fodder, and manufacturing purposes; being necessary to promote self-sustainable development by strengthening production units.

Hamed Attariani, Shayna Renay Petitjean, Aaron Michael Niekamp

One of the technological hurdles in the widespread application of additive manufacturing is the formation of undesired microstructure and defects, e.g., the formation of columnar grains in Ti-6Al-4V—the columnar microstructure results in anisotropic mechanical properties, a reduction in ductility, and a decrease in the endurance limit. Here, we present the potential implementation of a hexagonal array of synchronized lasers to alter the microstructure of Ti–6Al–4V toward the formation of preferable equiaxed grains. An anisotropic heat transfer model is employed to obtain the temporal/spatial temperature distributions and construct the solidification map for various process parameters, i.e., laser power, scanning speed, and the internal distance among lasers in the array. Approximately 55% of the volume fraction of equiaxed grains is obtained using a laser power of <i>P</i> = 500 W and a scanning speed of <i>v</i> = 100 mm/s. The volume fraction of the equiaxed grains decreases with increasing scanning velocity; it drops to 38% for <i>v</i> = 1000 mm/s. This reduction is attributed to the decrease in absorbed heat and thermal crosstalk among lasers, i.e., the absorbed heat is higher at low scanning speeds, promoting thermal crosstalk between melt pools and subsequently forming a large volume fraction of equiaxed grains. Additionally, a degree of overlap between lasers in the array is required for high scanning speeds (<i>v</i> = 1000 mm/s) to form a coherent melt pool, although this is unnecessary for low scanning speeds (<i>v</i> = 100 mm/s).

Jean Paul Sinumvayo, Pierre Celestin Munezero, Adegboyega Taofeek Tope et al.

Africa, home to the world’s second-largest population of approximately 1.3 billion, grapples with significant challenges in meeting its medical needs, particularly in accessing quality healthcare services and products. The continent faces a continuous onslaught of emerging infectious diseases, exacerbating the strain on its already fragile public health infrastructure. The COVID-19 crisis highlighted the urgency to build local vaccine production capacity and strengthen the health infrastructure in general. The risks associated with a heavy reliance on imported vaccines were exposed during the COVID-19 pandemic, necessitating the need to nurture and strengthen the local manufacturing of vaccines and therapeutic biologics. Various initiatives addressing training, manufacturing, and regulatory affairs are underway, and these require increasing dedicated and purposeful financial investment. Building vaccine manufacturing capacity requires substantial investment in training and infrastructure. This manuscript examines the current state of education in vaccinology and related sciences in Africa. It also provides an overview of the continent’s efforts to address educational needs in vaccine development and manufacturing. Additionally, it evaluates the initiatives aimed at strengthening vaccine education and literacy, highlighting successful approaches and ongoing challenges. By assessing the progress made and identifying the remaining obstacles, this review offers insights into how Africa can enhance its vaccine manufacturing capacity to respond to vaccine-preventable disease challenges.

Mikhailo Buhaievskyi, Yuri Petrenko

The focus of the current research is the multi-criteria task of decision-making support for the effective management of ready-mix concrete production and its delivery to construction sites, taking all possible risk factors into account. The development of a simulation model for the network of production facilities and the distribution chain of ready-made concrete mixtures is a key element of the project to create a digital twin in the production and logistics of a concrete plant. The relevance of this study is supported by the fact that post-war restoration of the destroyed housing stock, reconstruction of damaged infrastructure and industrial buildings, and the resumption of work at all construction sites in the country will lead to a sharp increase in the demand for concrete, which will obviously exceed the existing production capacity. Therefore, one of the top priorities for Ukrainian concrete plants today should be the implementation of a strategy and relevant development projects aimed at increasing productivity without losing quality. This research aims to create a simulation model of the production and delivery of ready-mixed concrete in a network of manufacturing plants and construction sites, as part of a project to create a digital double for making effective risk management decisions in real-time for the early detection of suboptimal activity in the production of high-quality concrete mix and its effective logistics. Thus, the objectives of the study are as follows:  to analyze the problems and features of creating digital duplicates in the production and logistics of concrete plants; to develop a simulation model of analyzing production processes and logistics of ready-mixed concrete mixtures; to provide an illustrated example of modeling production and logistics processes in a network of concrete factories and construction sites; to conduct optimization experiments to determine the modes of system operation. After all necessary work had been done, the following results have been obtained. A simulation model of the analyzing production processes and logistics of ready-mixed concrete mixtures has been developed, with the help of which it is possible to solve several tasks, including the evaluation of the rationality and efficiency in the organization of production and delivery of ready-mixed concrete, the identification of bottlenecks in production and logistics processes, forecasting of indicators activities of concrete plants, taking into account changes in production conditions, and forming data for decision-making on reducing plant and customer downtime, among others. Conclusions. The academic novelty of the study is related to the solution of the actual problem related to the preparation and planning of logistical actions for the delivery of ready-mixed concrete in the network of plants and construction sites bycreating a complex of optimization and simulation models, that contributes to the effectiveness of decision-making on risk management for the early detection of suboptimal activities in the production of commercial concrete mixtures and logistics. The effectiveness of the proposed approach is illustrated by an example of concrete delivery in a network of concrete factories in the Kharkiv region.

Juan De Anton, Juan J Senovilla, Jose M Gonzalez-Varona et al.

Production planning in 3D printing factories brings new challenges among which the scheduling of parts to be produced stands out. A main issue is to increase the efficiency of the plant and 3D printers productivity. Planning, scheduling, and nesting in 3D printing are recurrent problems in the search for new techniques to promote the development of this technology. In this work, we address the problem for the suppliers that have to schedule their daily production. This problem is part of the LONJA3D model, a managed 3D printing market where the parts ordered by the customers are reorganized into new batches so that suppliers can optimize their production capacity. In this paper, we propose a method derived from the design of combinatorial auctions to solve the nesting problem in 3D printing. First, we propose the use of a heuristic to create potential manufacturing batches. Then, we compute the expected return for each batch. The selected batch should generate the highest income. Several experiments have been tested to validate the process. This method is a first approach to the planning problem in 3D printing and further research is proposed to improve the procedure

Sanjana Mukherjee, Kanika Kalra, A. Phelan

The COVID-19 pandemic highlighted significant gaps in equitable access to essential medical countermeasures such as vaccines. Manufacturing capacity for pandemic vaccines, therapeutics, and diagnostics is concentrated in too few countries. One of the major hurdles to equitable vaccine distribution was “vaccine nationalism”, countries hoarded vaccines to vaccinate their own populations first which significantly reduced global vaccine supply, leaving significant parts of the world vulnerable to the virus. As part of equitably building global capacity, one proposal to potentially counter vaccine nationalism is to identify small population countries with vaccine manufacturing capacity, as these countries could fulfill their domestic obligations quickly, and then contribute to global vaccine supplies. This cross-sectional study is the first to assesses global vaccine manufacturing capacity and identifies countries with small populations, in each WHO region, with the capacity and capability to manufacture vaccines using various manufacturing platforms. Twelve countries were identified to have both small populations and vaccine manufacturing capacity. 75% of these countries were in the European region; none were identified in the African Region and South-East Asia Region. Six countries have facilities producing subunit vaccines, a platform where existing facilities can be repurposed for COVID-19 vaccine production, while three countries have facilities to produce COVID-19 mRNA vaccines. Although this study identified candidate countries to serve as key vaccine manufacturing hubs for future health emergencies, regional representation is severely limited. Current negotiations to draft a Pandemic Treaty present a unique opportunity to address vaccine nationalism by building regional capacities in small population countries for vaccine research, development, and manufacturing.

Hongfei Guo, Yingxin Zhu, Yu Zhang et al.

Daniel C. H. Tan, Manu Suvarna, Y. Tan et al.

Kawaljeet Kapoor, A. Bigdeli, Yogesh Kumar Dwivedi et al.

Disruption from the COVID-19 pandemic has caused major upheavals for manufacturing, and has severe implications for production networks, and the demand and supply chains underpinning manufacturing operations. This paper is the first of its kind to pull together research on both—the pandemic-related challenges and the management interventions in a manufacturing context. This systematic literature review reveals the frailty of supply chains and production networks in withstanding the pressures of lockdowns and other safety protocols, including product and workforce shortages. These, altogether, have led to closed facilities, reduced capacities, increased costs, and severe economic uncertainty for manufacturing businesses. In managing these challenges and stabilising their operations, manufacturers are urgently intervening by—investing in digital technologies, undertaking resource redistribution and repurposing, regionalizing and localizing, servitizing, and targeting policies that can help them survive in this altered economy. Based on holistic analysis of these challenges and interventions, this review proposes an extensive research agenda for future studies to pursue.