Hasil untuk "Production capacity. Manufacturing capacity"

Min‐Sik Park, Guoxiou Wang, Yong‐Mook Kang et al.

Alexandros-Stavros Toumanidis, Savvas Koltsakidis, Dimitrios Tzetzis

Injection mold design is a repetitive and time-consuming process with common individual tasks related to each other. This study presents the development of an automatic computer-aided design (CAD) tool for basic injection molds with complete modeling and no other interaction by the user after inserting the part, built on the SolidWorks Application Programming Interface 2022 (API) and Visual Basic for Applications 7.1 2012(VBA). The tool combines user input forms and supplier catalog data as inputs in an algorithm to automatically generate mold structures, cavity blocks, runner system, ejection system and straight drilled cooling channels without further manual modeling. Three case studies with one-, two-, and four-cavity molds demonstrate the approach. The results show that complete mold assemblies can be produced in less than 10 min rather than hours while maintaining standard component dimensions. Although the present version applies to rule-based geometric placement rather than thermal or injection process optimization, it provides a framework for future integration of more complex mold structures and functions such as slides, hot runner system, unscrewing geometries, conformal cooling, heat-transfer-based design, family molds and machine selection. This work demonstrates how API-driven automation can reduce design time, standardize layouts, and lay the groundwork for next-generation injection mold development.

Mostafa Meraj Pasha, Zhijian Pei, Md Shakil Arman et al.

Binder jetting additive manufacturing (BJAM) offers an effective approach for fabricating silicon carbide (SiC) parts with complex geometries; however, part quality is strongly influenced by process variables. Binder saturation and drying time are key process variables in BJAM, yet their individual influences on the density and dimensional deviation of SiC green parts remain underexplored. To address this gap, this study systematically investigates the effects of binder saturation and drying time on the dimensional deviation and density of SiC green parts by evaluating four binder saturation levels (60%, 80%, 100%, and 120%) and three drying times (15, 30, and 45 s). The results show that increasing binder saturation reduces green part density and increases dimensional deviation, whereas increasing drying time improves density and reduces dimensional deviation. Excessive drying, however, causes severe warpage, preventing the fabrication of dimensionally accurate parts. These findings highlight the need to optimize binder saturation and drying time to improve the density of printed parts while minimizing dimensional deviation.

Xiaohan Li, Sebastian Pattinson

Defects in extrusion additive manufacturing remain common despite its prevalent use. While numerous AI-driven approaches have been proposed to improve quality assurance, the inherently dynamic nature of the printing process poses persistent challenges. Regardless of how comprehensive the training dataset is, out-of-distribution data remains inevitable. Consequently, deterministic models often struggle to maintain robustness and, in some cases, fail entirely when deployed in new or slightly altered printing environments. This work introduces an agent that dynamically adjusts flow rate and temperature setpoints in real time, optimizing process control while addressing bottlenecks in training efficiency and uncertainty management. It integrates a vision-based uncertainty quantification module with a reinforcement learning controller, using probabilistic distributions to describe printing segments. While the underlying networks are deterministic, these evolving distributions introduce adaptability into the decision-making process. The vision system classifies material extrusion with a certain level of precision, generating corresponding distributions. A deep Q-learning controller interacts with a simulated environment calibrated to the vision system precision, allowing the agent to learn optimal actions while demonstrating appropriate hesitation when necessary. By executing asynchronous actions and applying progressively tightened elliptical reward shaping, the controller develops robust, adaptive control strategies that account for the coupling effects between process parameters. When deployed with zero-shot learning, the agent effectively bridges the sim-to-real gap, correcting mild and severe under- and over-extrusion reliably. Beyond extrusion additive manufacturing, this scalable framework enables practical AI-driven quality assurance across various additive manufacturing processes.

Ana Teresa Sucgang, Laurent Cuzacq, Jean-Louis Bobet et al.

Rapid technological advancements and the growing focus on sustainable practices have significantly expanded the potential applications of aluminum (Al) and its alloys, leading to a steady increase in demand over the years. This study investigated the densification of Al and Al-based materials using pressure-less liquid-phase sintering. Samples with 4–20 vol.% AlSi₁₂ sintered at 640 °C for 1 h achieved the highest relative density (RD) and the lowest global porosity (GP) without exhibiting any shape deformation. In general, increasing the amount of sintering aid improves the density of the samples. This was confirmed by microstructural analysis using SEM, which revealed the progression of density—from initial particle coalescence at 4 vol.% AlSi₁₂ to the development of microstructures with filled pores and well-defined grain boundaries at 20 vol.% AlSi₁₂. X-ray diffraction (XRD) analysis also revealed an expanded lattice parameter, with minimal microstrain and a crystallite size closely resembling those of the initial Al powder. Samples with a relative density greater than 90% demonstrated thermal conductivities ranging from 170 to 200 W/mK and an average hardness of 29 HV5. Densification was further enhanced by increasing the compaction pressure from 50 MPa to 100–200 MPa for samples containing 12–20 vol.% AlSi₁₂. The Al-based material compacted at 200 MPa and with 15 vol.% AlSi₁₂ achieved the highest RD of approximately 99%. It exhibited a thermal conductivity of 195 W/mK at 30 °C and 190 W/mK at 70 °C, along with a hardness of 30 HV5.

Rani Nopriyanti, Muhammad Fauzi, Heri Setiawan et al.

Mesin 3D concrete printing memiliki beberapa bagian, diantaranya adalah bagian pilar. Pilar terbagi menjadi pilar utama dan pilar support. Pilar utama terbagi menjadi tiga bagian yaitu pilar sumbu X, pilar sumbu Y, dan pilar sumbu Z. Penelitian ini berfokus kepada proses fabrikasi pilar support dan estimasi estimasi biaya - waktu untuk mesin 3D concrete printing dengan dimensi 7020 x 400 x 354 mm. Pilar support  terbuat dari besi profil siku dengan dimensi 30 x 30 x 3 mm, dan berfungsi untuk menopang pilar sumbu Z dan menjaga kesejajarannya. Berdasarkan dokumen operation plan yang telah dibuat dan perhitungan biaya-waktu menunjukkan bahwa proses fabrikasi dapat diselesaikan dalam 7,16 jam dengan biaya Rp. 1.588.140,-. Hasil penelitian ini memberikan gambaran detail mengenai proses pembuatan pilar support yang dapar diaplikasikan pada proyek serupa.  

Karlah-Jade Norkunas, R. Harding, J. Dale et al.

Agroinfiltration is a simple and effective method of delivering transgenes into plant cells for the rapid production of recombinant proteins and has become the preferred transient expression platform to manufacture biologics in plants. Despite its popularity, few studies have sought to improve the efficiency of agroinfiltration to further increase protein yields. This study aimed to increase agroinfiltration-based transient gene expression in Nicotiana benthamiana by improving all levels of transgenesis. Using the benchmark pEAQ-HT deconstructed virus vector system and the GUS reporter enzyme, physical, chemical, and molecular features were independently assessed for their ability to enhance Agrobacterium-mediated transformation and improve protein production capacities. Optimal Agrobacterium strain, cell culture density and co-cultivation time for maximal transient GUS (β-glucuronidase) expression were established. The effects of chemical additives in the liquid infiltration media were investigated and acetosyringone (500 μM), the antioxidant lipoic acid (5 μM), and a surfactant Pluronic F-68 (0.002%) were all shown to significantly increase transgene expression. Gene products known to suppress post-transcriptional gene silencing, activate cell cycle progression and confer stress tolerance were also assessed by co-expression. A simple 37 °C heat shock to plants, 1–2 days post infiltration, was shown to dramatically increase GUS reporter levels. By combining the most effective features, a dual vector delivery system was developed that provided approximately 3.5-fold higher levels of absolute GUS protein compared to the pEAQ-HT platform. In this paper, different strategies were assessed and optimised with the aim of increasing plant-made protein capacities in Nicotiana benthamiana using agroinfiltration. Chemical additives, heat shock and the co-expression of genes known to suppress stress and gene silencing or stimulate cell cycle progression were all proven to increase agroinfiltration-based transient gene expression. By combining the most effective of these elements a novel expression platform was developed capable of producing plant-made protein at a significantly higher level than a benchmark hyper-expression system.

Mohamed Achraf El youbi El idrissi, Loubna Laaouina, Adil Jeghal et al.

Given the recognized advantages of additive manufacturing (AM) printing systems in comparison with conventional subtractive manufacturing systems, AM technology has become increasingly adopted in 3D manufacturing, with usage rates increasing dramatically. This strong growth has had a significant and direct impact not only on energy consumption but also on manufacturing time, which in turn has generated significant costs. As a result, this problem has attracted the attention of industry actors and the research community, and several studies have focused on predicting and reducing energy consumption and additive manufacturing time, which has become one of the main objectives of research in this field. However, there is no effective model yet for predicting and optimizing energy consumption and printing time in a fused deposition modeling (FDM) process while taking into account the correct part orientation that minimizes both of these costs. In this paper, a neural-network-based model has been proposed to solve this problem using experimental data from isovolumetrically shaped mechanical parts. The data will serve as the basis for proposing the appropriate model using a specific methodology based on five performance criteria with the following statistical values: R2-squared > 99%, explained variance > 99%, MAE < 0.99%, MSE < 0.02% and RMSE < 1.36%. These values show just how effective the proposed model will be in estimating energy consumption and FDM printing time, taking into account the best choice of part orientation for the lowest cost. This model provides a global understanding of the primary energy and time requirements for manufacturing while also improving the system’s cost efficiency. The results of this work can be extended and applied to other additive manufacturing processes in future work.

Nguyen Khoi Quan, Nguyen Le My Anh, Andrew W. Taylor-Robinson

Abstract Background A global surplus of coronavirus disease 2019 (COVID-19) vaccines exists as a result of difficulties in aligning the demand and supply for vaccine manufacturing and delivery. World leaders have accelerated vaccine development, approval, production and distribution as a pragmatic approach to addressing the immediate public health challenges of the first two and a half years of the pandemic. Main body The currently predominant, highly transmissible Omicron variant of severe acute respiratory syndrome coronavirus 2 has brought us closer to the threshold required to achieve herd immunity by greatly increasing rates of natural infection. Paradoxically, in parallel with rising vaccination levels in industrialized nations, this indirectly reduces the need for mass vaccine campaigns. Principal concerns that contribute to low vaccination rates which persist in several other countries, particularly of the Global South, are vaccine hesitancy and unequal access to vaccination. Social uncertainty fueled by fake news, misinformation, unfounded lay opinions and conspiracy theories has inevitably led to an erosion of public trust in vaccination. Conclusion To address the current mismatch between supply and demand of COVID-19 vaccines, there should be a focus on three principles: decelerating vaccine production, increasing distribution across communities, and optimizing cost-effectiveness of distribution logistics. Slowing down and switching from large-scale production to effectively ‘made to order’ is a feasible option, which should be commensurate with management capacity. Transparent and evidence-based data should be widely and freely disseminated to the public through multimedia channels to mitigate miscommunication and conspiracy theories. Use of soon-to-expire stockpiles should be prioritized not only to enhance booster dose rollouts in adults but to expand immunization campaigns to children (especially those aged 5–11 years), subject to national approval. Future research should ideally aim to develop vaccines that only require basic, affordable storage and maintenance procedures as opposed to sophisticated and expensive protocols. Graphical Abstract

Thomas Feldhausen, Mithulan Paramanathan, Jesse Heineman et al.

Computer-aided manufacturing (CAM) techniques for hybrid manufacturing have led to new application areas in the manufacturing industry. In the tooling industry, cooling channels are used to enable specific heating and cooling cycles to improve the performance of the process. These internal cooling channels have been designed with limited manufacturing processes in mind, so, until recently, they were often straight in shape for cross-drilling operations and manufactured from a cast billet. To show a novel application of this common technology, a tool with integrated conformal cooling channels was manufactured using hybrid manufacturing (blown-powder DED and CNC machining) techniques. The computer-aided manufacturing strategy used, and the lessons learned are presented and discussed to enable future work in this industrial application space.

Pascal Krutz, André Leonhardt, Alexander Graf et al.

Given the use of high-strength steels to achieve lightweight construction goals, conventional shear-cutting processes are reaching their limits. Therefore, so-called high-speed impact cutting (HSIC) is used to achieve the required cut surface qualities. A new machine concept consisting of linear motors and an impact mass is presented to investigate HSIC. It allows all relevant parameters to be flexibly adjusted and measured. The design and construction of the test bench, as well as the mechanism for coupling the impact mass, are described. To validate the theoretically determined process speeds, the cutting process was recorded with high-speed cameras, and HSIC with a mild deep-drawing steel sheet was performed. It was discovered that very good cutting edges could be produced, which showed a significantly lower hardening depth than slowly cut reference samples. In addition, HSIC was numerically modelled in LS-DYNA, and the calculated cutting edges were compared with the real ones. With the help of adaptive meshing, a very good agreement for the cutting edges could be achieved. The results show the great potential of using a linear motor in HSIC.

Jeremy Cleeman, Kian Agrawala, Rajiv Malhotra

Machine Learning (ML) is of increasing interest for modeling parametric effects in manufacturing processes. But this approach is limited to established processes for which a deep physics-based understanding has been developed over time, since state-of-the-art approaches focus on reducing the experimental and/or computational costs of generating the training data but ignore the inherent and significant cost of developing qualitatively accurate physics-based models for new processes . This paper proposes a transfer learning based approach to address this issue, in which a ML model is trained on a large amount of computationally inexpensive data from a physics-based process model (source) and then fine-tuned on a smaller amount of costly experimental data (target). The novelty lies in pushing the boundaries of the qualitative accuracy demanded of the source model, which is assumed to be high in the literature, and is the root of the high model development cost. Our approach is evaluated for modeling the printed line width in Fused Filament Fabrication. Despite extreme functional and quantitative inaccuracies in the source our approach reduces the model development cost by years, experimental cost by 56-76%, computational cost by orders of magnitude, and prediction error by 16-24%.

Abstract The full text of this preprint has been withdrawn, as it was submitted in error. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.

João P. M. Pragana, Rui F. V. Sampaio, Ivo M. F. Bragança et al.

This paper presents a new mechanical joining process to assemble aluminum busbars in energy distribution systems. The process is based on the extension of injection lap riveting to the connection of busbars made from the same material as the rivets and requires redesigning the joints to ensure complete filling with good mechanical interlocking and appropriate contact pressures on the overlapping area. The experimental work was carried out in unit cells and involved the fabrication of the riveted joints and the evaluation of their electrical resistance at different service temperatures. Comparisons with the bolted joints that were fabricated and tested for reference purposes show that injection riveted joints provide lower values of electrical resistance and require much less space for assembly due to the absence of material protrusions above and below their surfaces. Numerical simulation with finite elements allows the relating of the reduction in electrical resistance with the changes in the electric current flow when the bolts are replaced by the new type of rivets. The experimental and numerical predictions revealed that the new type of rivets experience an increase in electrical resistance of up to 6 μΩ (30%) when the service temperature approaches 105 °C. Still, the resistance at this temperature (26.2 μΩ) is more than 3 times smaller than that of the bolted joints (80.5 μΩ).

Dandan Jiang, Han Hao, Lu Yang et al.

Capacity is one of the most important performance metrics for wireless communication networks. It describes the maximum rate at which the information can be transmitted of a wireless communication system. To support the growing demand for wireless traffic, wireless networks are becoming more dense and complicated, leading to a higher difficulty to derive the capacity. Unfortunately, most existing methods for the capacity calculation take a polynomial time complexity. This will become unaffordable for future ultra-dense networks, where both the number of base stations (BSs) and the number of users are extremely large. In this paper, we propose a fast algorithm TOSE to estimate the capacity for ultra-dense wireless networks. Based on the spiked model of random matrix theory (RMT), our algorithm can avoid the exact eigenvalue derivations of large dimensional matrices, which are complicated and inevitable in conventional capacity calculation methods. Instead, fast eigenvalue estimations can be realized based on the spike approximations in our TOSE algorithm. Our simulation results show that TOSE is an accurate and fast capacity approximation algorithm. Its estimation error is below 5%, and it runs in linear time, which is much lower than the polynomial time complexity of existing methods. In addition, TOSE has superior generality, since it is independent of the distributions of BSs and users, and the shape of network areas.

Jingang Yu, Bao-Yu Yue, Xiongwei Wu et al.

Honglei Li, Liang Cong, Huazheng Ma et al.

Abstract The rapidly growing deployment of lithium-ion batteries in electric vehicles is associated with a great waste of natural resource and environmental pollution caused by manufacturing and disposal. Repurposing the retired lithium-ion batteries can extend their useful life, creating environmental and economic benefits. However, the residual capacity of retired lithium-ion batteries is unknown and can be drastically different owing to various working history and calendar life. In this study, we used the incremental capacity (IC) curve to estimate the residual capacity of waste power batteries. First, through experimental means, the parameters of the battery and the IC charging curve are measured. Second, to achieve rapid capacity estimation, a battery capacity estimation method based on the adaptive genetic algorithm-back propagation neural network (AGA-BPNN) is proposed and compared with other classic machine learning methods. The proposed algorithm reduced the error of capacity estimation to 3%. Finally, through the analysis of the IC curve, a method for identifying aging mechanism of large-scale decommissioned batteries is obtained. This research provides effective support for the capacity-based classification of large-scale decommissioned power batteries.

Jan Holmström, Jouni Partanen, Jukka Tuomi et al.

PurposeThe purpose of this paper is to describe and evaluate the potential approaches to introduce rapid manufacturing (RM) in the spare parts supply chain.Design/methodology/approachAlternative conceptual designs for deploying RM technology in the spare parts supply chain were proposed. The potential benefits are illustrated for the aircraft industry. The general feasibility was discussed based on literature.FindingsThe potential supply chain benefits in terms of simultaneously improved service and reduced inventory makes the distributed deployment of RM very interesting for spare parts supply. However, considering the trade‐offs affecting deployment it is proposed that most feasible is centralized deployment by original equipment manufacturers (OEMs), or deployment close to the point of use by generalist service providers of RM.Research limitations/implicationsThe limited part range that is currently possible to produce using the technology means that a RM‐based service supply chain is feasible only in very particular situations.Practical implicationsOEMs should include the consideration of RM in their long‐term service supply chain development.Originality/valueThe paper identifies two distinct approaches for deploying RM in the spare parts supply chain.

William de Paula Ferreira, Leonardo Carlos da Cruz, Michael David de Souza Dutra

The aim of this study is to develop a solution to the problem of distribution of goods proposed by the Mathematical Competitive Game 2017-2018, jointly organized by the French Federation of Mathematical Games and Mathematical Modelling Company. Referred to as a production-inventory-distribution-routing problem (PIDRP), it is an NP-hard combinatorial optimization problem, which received the least attention in the literature. The research is quantitative model-based and combines exact and heuristic methods to propose a multiple-phase resolution approach to PIDRP. The results show that the use of clusters ensures practical operational aspects and provides good feasible solutions for the PIDRP in short and long-term planning. The theoretical contribution of this study lies in the PIDRP modeling strategy, and the practical contribution consists in solving a real-life PIDRP-based using optimization techniques.

Irene Pessolano Filos, Raffaella Sesana, Massimiliano Di Biase et al.

Technological progress in hybrid bearings developed high wear and abrasion resistant materials for rolling elements. The manufacturing process of bearing balls presents new challenges, as nowadays, it requires time-consuming and costly processes. In this frame, the bearing manufacturing industry is demanding improvements in materials, geometry, and processes. This work aims to investigate new abrasive coatings for grinding wheels for Si₃N₄ ball manufacturing. Tribological pin on disk tests are performed on samples of grinding materials (disk) versus a Si₃N₄ ball (pin). Two samples of specimens coated with an electrodeposited diamond and diamond-reinforced metal matrix composite are examined to measure the abrasion rate and the wear resistance of Silicon Nitride Si₃N₄ balls, considering the influence of sliding speed and the effect of coating deposition on diamond particle density and granulometry. The measurements estimated the specific wear coefficient <i>k</i>, the height wear surface <i>h</i>, and the wear rate <i>u</i> of the Si₃N₄ balls. The results pointed out that by increasing the sliding speed, the abraded volume increases for both the coatings. The parameters affecting the abrasion effectiveness of both the coatings are the surface roughness, the abrasive particle dimension, and the sliding speed.