Hasil untuk "Production capacity. Manufacturing capacity"

Fabian Duffner, Niklas Kronemeyer, J. Tübke et al.

Jinchuan Wang, Yubo Zuo, Cheng Lin et al.

This paper examines the combined effects of intensive melt shearing and magnetic fields on the flow characteristics of molten aluminum alloy during the direct chill (DC) casting process. Using computational fluid dynamics (CFD), we analyze flow fields across varying rotor speeds, diameters, blade counts, stator configurations, and electromagnetic field strengths. The results demonstrate that increasing rotor speed and diameter significantly enhances melt flow velocity and vortex intensity. Conversely, a greater number of rotor blades results in reduced flow velocity due to higher resistance. Moreover, the shape and diameter of the stator opening impact flow patterns, with circular openings producing higher velocities. The interaction of electromagnetic fields and intensive shearing generates three distinct vortices, achieving the highest overall flow velocity and a uniform distribution. Additionally, adjustments in excitation coil current and rotor speed enable further control of melt flow, offering valuable insights for optimizing casting processes and enhancing ingot quality.

Morgane Mokhtari, Chirag Khandivar, Yannick Balcaen et al.

Fused Filament Fabrication (FFF) is a low-cost additive manufacturing process that produces metallic parts from printing with a metal-polymer filament, followed by a debinding–sintering process. It presents an opportunity for the tooling sector to improve performance by geometrical optimization while keeping costs low. This study investigates the possibility of producing a molding core for plastic injection by FFF technology. This research aimed to characterize 17-4 PH stainless steel and H13 hot work tool steels produced through this process. Their heat treatment behavior was investigated using dilatometry, which led to the obtention of a Continuous Cooling Transformation (CCT) diagram. Results show that for as-sintered materials, martensitic steel with some residual austenite is present in 17-4 PH, and a pearlitic microstructure is observed in H13. Porosity (around 4%) falls within the reported range in the literature and can be removed by hot isostatic pressing. CCT diagrams do not show significant differences with conventional materials. The low hardness of as-sintered H13 (around 175 HV1) is improved (>500 HV1) by suitable heat treatment. Finally, both materials meet the requirements for this specific industrial application, and demonstrators were produced.

Guy Tchuente

Decentralized autonomous organizations (DAOs) are designed to disperse control, yet recent evidence shows that effective governance is often concentrated in a small number of participants. This note studies one simple mechanism behind that pattern. Because decentralized governance is monitor-intensive, rising proposal flow may eventually outpace the capacity of broad-based participation. Using a DAO--quarter panel, I estimate a fixed-effects kink model with DAO and quarter fixed effects and find a statistically significant decline in the marginal responsiveness of active voters once proposal activity crosses an interior threshold. I then study realized voting concentration using kink specifications with data-driven cutoffs. Across specifications, decentralization gains do not persist indefinitely once governance workload becomes sufficiently high, and load-based measures show especially clear evidence of a transition toward more concentrated realized control. The results provide reduced-form evidence consistent with a ``too big to monitor'' mechanism in DAO governance: when proposal flow grows faster than broad participation can keep up, effective control may drift toward a smaller set of highly active participants.

Maryam Aftab, Sania Ikram, Muneeb Ullah et al.

The transition from three-dimensional (3D) to four-dimensional (4D)-bioprinting marks a significant advancement in tissue engineering and drug delivery. 4D-bioprinting offers the potential to more accurately mimic the adaptive qualities of living tissues due to its dynamic flexibility. Structures created with 4D-bioprinting can change shape in response to internal and external stimuli. This article reviews the background, key concepts, techniques, and applications of 4D-bioprinting, focusing on its role in tissue scaffolding and drug delivery. We discuss the limitations of traditional 3D-bioprinting in providing customized and sustained medication release. Shape memory polymers and hydrogels are examples of new responsive materials enabled by 4D-bioprinting that can enhance drug administration. Additionally, we provide a thorough analysis of various biopolymers used in drug delivery systems, including cellulose, collagen, alginate, and chitosan. The use of biopolymers in 4D-printing significantly increases material responsiveness, allowing them to react to stimuli such as temperature, light, and humidity. This capability enables complex designs with programmable shape and function changes. The expansion and contraction of hydrogels in response to temperature changes offer a practical method for controlled drug release. 4D-bioprinting has the potential to address significant challenges in tissue regeneration and medication administration, spurring ongoing research in this technology. By providing precise control over cell positioning and biomaterial integration, traditional 3D-bioprinting has evolved into 4D-bioprinting, enhancing the development of tissue constructs. 4D-bioprinting represents a paradigm shift in tissue engineering and biomaterials, offering enhanced possibilities for creating responsive, adaptive structures that address clinical needs. Researchers can leverage the unique properties of biopolymers within the 4D-printing framework to develop innovative approaches for tissue regeneration and drug delivery, leading to advanced treatments in regenerative medicine. One potential future application is in vivo tissue regeneration using bioprinted structures that can enhance the body’s natural healing capabilities.

Yegnaneh Anley, Yeniewa Kerie Anagaw, Fasika Mekete Alemu et al.

Local pharmaceutical production reduces dependency on imports. It also strengthens country-based medicine supply. In Ethiopia, there are government policies in place that aim to support local manufacturers. However, most of them operate below capacity, supplying only 15-20% of national pharmaceutical needs. This study assessed internal, external, and production-related factors that influence the productivity and competitive performance of local pharmaceutical manufacturing companies. A mixed concurrent triangulation design was conducted from October 2021 to February 2022. Quantitative data were collected from 40 employees across ten local manufacturers, while qualitative data were obtained through 29 key informant interviews with representatives from manufacturers, the Ethiopian Food and Drug Authority (EFDA), the Food, Beverage, Pharmaceuticals Industry Development Institute (FBPIDI), and the Ethiopian Pharmaceuticals and Medical Supplies Manufacturing Association (EPMSMA). Quantitative data were analyzed using SPSS version 25, and qualitative data were analyzed thematically. Internal factors, including material handling (β = 0.57, P < 0.036) and production planning and machine maintenance (β = 0.16, P < 0.047), were significantly associated with manufacturing performance. Among external factors, policy and economic conditions (β = -0.07, P < 0.042) significantly affected performance. Only three manufacturers fully complied with cGMP standards. Most government policies designed to support local production were not yet implemented, resulting in underutilization of capacity (<50%). The major challenges are limited foreign currency, technology transfer hurdles, shortage of qualified personnel, raw materials inadequacy and tax policies favoring imports. Both internal and external factors significantly influence the performance of local pharmaceutical manufacturers. Although government policy directions exist to support them, incomplete implementation limits their impact.

Eric Bruce-Amartey, Sumaila Mohammed Sumaila, Ronald Osei Mensah

Abstract The Ghanaian textile industry, once a thriving sector, has experienced a prolonged decline over the past four decades, marked by significant reductions in production capacity, employment, and competitiveness. This study analyse the output of the textile industry and subsequently examine the causes of the decline of Ghana’s textile industry. Guided by the theory of comparative advantage, the research investigates how globalization, trade liberalization, and inefficient domestic policies have contributed to the industry’s challenges, including competition from cheaper imports, counterfeit products, and rising production costs. Using a qualitative approach and historical case study methodology, data were collected through interviews, observations, and document analysis. Key stakeholders, including textile manufacturers, government officials, labor unions, and retailers, provided insights into the operational and policy-related barriers to industry revival. The study reveals a significant decline in Ghana’s textile industry output, dropping from 130 million yards in 1977 to just 15 million yards in 2017, highlighting the impact of foreign competition, smuggling, high production costs, and outdated manufacturing technology on the industry’s performance. Againg, findings highlight the critical impact of trade policies, smuggling, and consumer behavior on local production. This research underscores the urgent need for strategic interventions to enhance competitiveness, improve branding, and enforce intellectual property rights. Recommendations include fostering innovation, improving market access, and revisiting trade policies. These findings offer a foundation for policymakers and industry stakeholders to implement targeted measures aimed at revitalizing the Ghanaian textile sector and restoring its contribution to economic development.

Sunandita Patra, Mehtab Pathan, Mahmoud Mahfouz et al.

Organizations around the world schedule jobs (programs) regularly to perform various tasks dictated by their end users. With the major movement towards using a cloud computing infrastructure, our organization follows a hybrid approach with both cloud and on-prem servers. The objective of this work is to perform capacity planning, i.e., estimate resource requirements, and job scheduling for on-prem grid computing environments. A key contribution of our approach is handling uncertainty in both resource usage and duration of the jobs, a critical aspect in the finance industry where stochastic market conditions significantly influence job characteristics. For capacity planning and scheduling, we simultaneously balance two conflicting objectives: (a) minimize resource usage, and (b) provide high quality-of-service to the end users by completing jobs by their requested deadlines. We propose approximate approaches using deterministic estimators and pair sampling-based constraint programming. Our best approach (pair sampling-based) achieves much lower peak resource usage compared to manual scheduling without compromising on the quality-of-service.

Till Böttjer, Daniella Tola, Fatemeh Kakavandi et al.

In recent years, the hype around Digital Twins (DTs) has been exponentially increasing in both industry and academia. DTs are a potential solution to increase automation and advance towards Smart Manufacturing. Manufacturing DTs have been implemented at different hierarchical levels, ranging from system of systems to unit level. Increasing computational capacity and data exchange rates can enable DT implementations for real-time applications. Several literature reviews on manufacturing DTs have been published. However, no previous paper focuses on manufacturing DTs at the unit level for which real-time control is most applicable. Simultaneously, the challenges to engineer DTs with real-time capabilities are enormous, both from a scientific and technological perspective. Therefore, we focus on DTs of single production units such as traditional machine tools, additive manufacturing machines and advanced robotic applications. In this systematic literature review, 96 papers about practical unit level DT applications found in the Scopus database using a combination of the keywords “Digital Twin”, “Production” and “Manufacturing” are reviewed. We summarize how DTs are currently implemented and operated, and what potential benefits DTs offer at the unit process level in four categories: generic reference models, services, DT content (models and data) and DT deployment (hardware and software). Following the thematic analysis, an overall discussion, summary of key contributions and identified research gaps, and outlook into future research avenues is given. Key findings of this review can be summarized as: focus on DT components versus being holistic; need to share data and models across multiple stakeholders; lack of physical fidelity of the models; stark contrast of lab scale developments and real world testing, e.g., historical data and storage related challenges; lack of clear definition of DT in industry, and missing semantic interoperability between a wide variety of domains.

Magdi Elsallab, M. Maus

Alexandre Dolgui, Oleg Gusikhin, Dmitry A. Ivanov et al.

Abstract During a crisis, manufacturing processes in supply chains of different industries may network with each other as an adaptation response. We propose and examine a “network-of-networks” mechanism of such a cross-industry adaptation to learn about the value of reducing uncertainty through collaborative crisis preparedness and response during the COVID-19 pandemic. Our study allows revelation of the underlying trade-offs between the manufacturing capacity conversion time and effort required to adapt and the gains from collaborative preparedness to uncertainty. Through a real-life data-based analysis with the help of mathematical optimization, we connect the networks-of-networks coordination design and the outcomes of scenario modeling demonstrating a superiority of the coordinated capacity repurposing when compared to ad-hoc adaptation. We conclude that an appropriate collaboration of governmental agencies, healthcare, and industry is crucial for a prompt capacity conversion to healthcare production in a pandemic. Concrete implementation ways are visibility, healthcare inventory monitoring, technology backup plans, and repurposing contingency plans at the preparedness stage. At the response stage, a correct adaptation start time determines success. The results obtained can be instructive to develop technological and managerial plans for a cross-industry adaptation. The proposed “network-of-networks” perspective contributes to theory of supply chain viability and adaptation under disruptions using intertwined supply networks.

Alireza Vahedi Nemani, Mahya Ghaffari, Kazem Sabet Bokati et al.

Copper-based materials have long been used for their outstanding thermal and electrical conductivities in various applications, such as heat exchangers, induction heat coils, cooling channels, radiators, and electronic connectors. The development of advanced copper alloys has broadened their utilization to include structural applications in harsh service conditions found in industries like oil and gas, marine, power plants, and water treatment, where good corrosion resistance and a combination of high strength, wear, and fatigue tolerance are critical. These advanced multi-component structures often have complex designs and intricate geometries, requiring extensive metallurgical processing routes and the joining of the individual components into a final structure. Additive manufacturing (AM) has revolutionized the way complex structures are designed and manufactured. It has reduced the processing steps, assemblies, and tooling while also eliminating the need for joining processes. However, the high thermal conductivity of copper and its high reflectivity to near-infrared radiation present challenges in the production of copper alloys using fusion-based AM processes, especially with Yb-fiber laser-based techniques. To overcome these difficulties, various solutions have been proposed, such as the use of high-power, low-wavelength laser sources, preheating the build chamber, employing low thermal conductivity building platforms, and adding alloying elements or composite particles to the feedstock material. This article systematically reviews different aspects of AM processing of common industrial copper alloys and composites, including copper-chrome, copper-nickel, tin-bronze, nickel-aluminum bronze, copper-carbon composites, copper-ceramic composites, and copper-metal composites. It focuses on the state-of-the-art AM techniques employed for processing different copper-based materials and the associated technological and metallurgical challenges, optimized processing variables, the impact of post-printing heat treatments, the resulting microstructural features, physical properties, mechanical performance, and corrosion response of the AM-fabricated parts. Where applicable, a comprehensive comparison of the results with those of their conventionally fabricated counterparts is provided.

Rodrigo Silva Sotolani, Isabella de Araújo Cionini Menezes, Napoleão Verardi Galegale et al.

O progresso da Indústria 4.0 tem relevância cada vez maior, considerando o aumento das vulnerabilidades de segurança da informação e da complexidade em priorizá-las na tomada de decisões. Observou-se uma lacuna de pesquisa neste tema. O objetivo deste artigo é identificar critérios na literatura científica que possam ser utilizados em um método de análise multicritério, visando a priorização de tratamento de vulnerabilidades de segurança na Indústria 4.0. Um método como o Analytic Hierarchy Process (AHP) é uma proposta de solução. A metodologia utilizada foi a revisão exploratória da literatura encontrada nas bases SCOPUS e Web of Science. O resultado identificou oito critérios e 34 subcritérios relacionados ao tratamento das vulnerabilidades de segurança na Indústria 4.0. A contribuição teórica vai ao encontro do preenchimento da lacuna em relação a este tema. A contribuição prática permite que organizações da Industria 4.0 apliquem os critérios identificados na análise multicritério para o tratamento das suas vulnerabilidades de segurança e assim alcancem melhores decisões para a entrega de produtos e serviços contribuindo para sociedade. Pesquisas futuras podem ser conduzidas por meio de entrevistas ou questionários para validação com profissionais da área dos critérios encontrados, como também a aplicação prática do método AHP.

Huy Hoan Nguyen, Henri Champliaud, Van Ngan Le

The metal spinning process has been observed in recent major investigations carried out using finite element analysis. One interesting idea has proposed simulating a rotating disc for the simulation of the metal spinning process to reduce computational time. The development of this concept is presented in this paper, including the formal mathematical transformation from the inertial frame to the rotating reference frame, specific FEM configurations with mesh sizes based on a minimized aspect ratio, a mesh convergence study, and the application of a feed rate scale. Furthermore, in the context of the rotating reference frame, the flange geometry after wrinkle initiation is investigated, including the number of peaks and their amplitudes. Using this new approach, it was found that the number of peaks gradually increases from two to eight peaks while their amplitude decreases. In the case of severe wrinkles, the number of peaks stays at four while the amplitude increases dramatically. The intermediate path proves capable of increasing the number of peaks while maintaining a low amplitude. These results will make it possible to design new paths, facilitating the production of defect-free spun parts.

Mao Lin, Kyungjoo Noh

Determining the quantum capacity of a noisy quantum channel is an important problem in the field of quantum communication theory. In this work, we consider the Gaussian random displacement channel $N_σ$, a type of bosonic Gaussian channels relevant in various bosonic quantum information processing systems. In particular, we attempt to make progress on the problem of determining the quantum capacity of a Gaussian random displacement channel by analyzing the error-correction performance of several families of multi-mode Gottesman-Kitaev-Preskill (GKP) codes. In doing so we analyze the surface-square GKP codes using an efficient and exact maximum likelihood decoder (MLD) up to a large code distance of $d=39$. We find that the error threshold of the surface-square GKP code is remarkably close to $σ=1/\sqrt{e}\simeq 0.6065$ at which the best-known lower bound of the quantum capacity of $N_σ$ vanishes. We also analyze the performance of color-hexagonal GKP codes up to a code distance of $d=13$ using a tensor-network decoder serving as an approximate MLD. By focusing on multi-mode GKP codes that encode just one logical qubit over multiple bosonic modes, we show that GKP codes can achieve non-zero quantum state transmission rates for a Gaussian random displacement channel $N_σ$ at larger values of $σ$ than previously demonstrated. Thus our work reduces the gap between the quantum communication theoretic bounds and the performance of explicit bosonic quantum error-correcting codes in regards to the quantum capacity of a Gaussian random displacement channel.

Pacana Andrzej, Czerwińska Karolina

A turbulent manufacturing market, especially in the metal industry, determines the quality of products and the level of production efficiency, which contributes to a company's market position and competitiveness. The aim of the study was to develop a model to define a manufacturing company's current market position using KPIs in relation to a key product - gearbox casting. The company's position was defined in terms of the relationship occurring between technological capabilities and market position. An additional aim of the study was to identify critical determinants and, ultimately, to identify conditions for strengthening market position. As a test of the proposed model, the position of the analysed company (in terms of technological capabilities and market position) was defined as "Search for occasions" - box 9 within the 3×3 matrix. Technological determinants that weaken the company's position (low level of maintenance capacity and long production cycle time) and determinants with a strong negative impact on market position (low level of human resource development) were identified. An element of novelty is the use of KPIs as variables determining the position of the company within the 3×3 matrix, which is indicative of a specific technological position in the market. Further lines of research will concern the determination of appropriate KPIs in relation to the identified critical areas of the company. Subsequent steps will concern the implications of the model in relation to the company's other key aluminium alloy castings.

Archit Shrivastava, Ravi Kumar Digavalli

Warm forming is widely used to enhance the formability of aluminum alloy sheets. In warm deep drawing, the process variables significantly affect frictional characteristics at the tool–blank interface. It has been a conventional approach to use a constant value of friction coefficients in the finite element (FE) simulations. However, this can occasionally result in suboptimal accuracy of the predictions. In the present work, strip drawing tests were carried out on AA5182 aluminum alloy sheets to investigate the effect of important process variables, namely, temperature, contact pressure, and drawing speed, on the friction coefficient in the warm forming temperature range (100–250 °C) under lubricated condition. The results obtained from the strip drawing tests were used for defining the friction conditions in the simulation of warm deep drawing of cylindrical cups incorporating the variation of the friction coefficient with contact pressure and speed at different temperatures. The Barlat89 yield criterion was used to define the effect of anisotropy in the material. The Voce hardening law and Cowper–Symonds model were used to incorporate the effect of strain hardening and strain rate, respectively, in the simulation. Drawability and peak force were compared with the predictions when a constant friction coefficient was assumed. Warm deep drawing experiments were conducted to validate the predicted drawability and load–displacement curves. It is clearly observed that the accuracy of prediction of the limiting drawing ratio and peak load through simulations is improved by incorporating the effect of pressure and speed on friction coefficient as it captures the local variations of friction during warm deep drawing precisely, rather than assuming a constant average friction coefficient at all the tool–blank contact areas.

Dana Marsetiya Utama, Selvia Rubiyanti, Rahmat Wisnu Wardana

Inventory issues are often a major concern as they significantly impact operating costs. Lot sizing is one of the key decisions in managing inventory. However, in a real context, the demand for each item is often uncertain. It is supplied from the same supplier, requiring product orders to be placed in the same period. In addition, limited vehicle capacity and transportation costs are important factors to consider in making multi-item lot sizing decisions. The purpose of this study is to propose a new inventory model multi-item lot sizing model involving transportation cost and capacity constraints under stochastic demand. The decision variables involved in the model are each item's ordering cycle and safety factor with the objective function of minimizing the total inventory cost. To optimize the inventory model, this study also offers the advanced procedure of the Aquila Algorithm. This study also presents sensitivity analysis to the appropriate policy for optimizing the multi-item lot-sizing inventory problem involving transportation cost and capacity constraint under stochastic demand.

Vito Basile, Francesco Modica, Lara Rebaioli et al.

As the complexity of micro-products increases, the micro-manufacturing processes, tool setups, and measurement processes have to be more precise and efficient. Combining them in a multi-stage process chain can effectively improve production accuracy and performance and reduce limitations and production costs. This paper focuses on the process chains for the manufacturing of micro-products and presents the state of the art, highlighting the specific characteristics of the existing models of process chains for micro-manufacturing. Based on the critical review of these characteristics, an evolution of the process chain model for micro-manufacturing is proposed, considering machining, measurement/characterization, referencing processes, and their combination into a suitable sequence. The proposed model accounts for relevant aspects of micro-manufacturing, such as size effects and technological fingerprints at the microscale. This paper also discusses the hierarchical properties of multiple micro-manufacturing process chains and some specific techniques to address the critical issue of referencing processes. Furthermore, some relevant case studies involving micro-electrical discharge machining, micro-injection molding, additive manufacturing, and micro-milling are presented to demonstrate how the micro-manufacturing potentiality can be increased using process chains.

Md Habibor Rahman, Rocco Cassandro, Thorsten Wuest et al.

An attack taxonomy offers a consistent and structured classification scheme to systematically understand, identify, and classify cybersecurity threat attributes. However, existing taxonomies only focus on a narrow range of attacks and limited threat attributes, lacking a comprehensive characterization of manufacturing cybersecurity threats. There is little to no focus on characterizing threat actors and their intent, specific system and machine behavioral deviations introduced by cyberattacks, system-level and operational implications of attacks, and potential countermeasures against those attacks. To close this pressing research gap, this work proposes a comprehensive attack taxonomy for a holistic understanding and characterization of cybersecurity threats in manufacturing systems. Specifically, it introduces taxonomical classifications for threat actors and their intent and potential alterations in system behavior due to threat events. The proposed taxonomy categorizes attack methods/vectors and targets/locations and incorporates operational and system-level attack impacts. This paper also presents a classification structure for countermeasures, provides examples of potential countermeasures, and explains how they fit into the proposed taxonomical classification. Finally, the implementation of the proposed taxonomy is illustrated using two realistic scenarios of attacks on typical smart manufacturing systems, as well as several real-world cyber-physical attack incidents and academic case studies. The developed manufacturing attack taxonomy offers a holistic view of the attack chain in manufacturing systems, starting from the attack launch to the possible damages and system behavior changes within the system. Furthermore, it guides the design and development of appropriate protective and detective countermeasures by leveraging the attack realization through observed system deviations.