Hasil untuk "Manufactures"

Yerikho Alexander, Fibi Eko Putra, Putri Anggun Sari

PT. Electronics Components Indonesia manufactures capacitors and focuses on enhancing productivity and operational efficiency of the frame welding machines through effective maintenance. This study employs a quantitative method to analyze the Overall Equipment Effectiveness (OEE) values, including availability, performance efficiency, and rate of quality, as well as conducting a Six Big Losses analysis. The results indicate that the average availability reached 97.83%, with a significant decrease in August due to downtime. Performance efficiency remained consistently above 90%, although higher product output tended to reduce efficiency. The rate of quality was stable and high, reflecting improvements in production processes and quality control. The average OEE value reached 88%, exceeding the global standard of 85%. To further enhance the effectiveness of the frame welding machines, suggested improvements include operator training, regular performance evaluations, attention to operator well-being, selection of high-quality raw materials, updating SOPs, regular preventive maintenance, improving workplace safety, and investing in backup energy systems. In conclusion, the improvements implemented successfully enhanced the performance and operational quality of the frame welding machines.

D. Rodrik

Robert C. Johnson, Guillermo Noguera

J. Eaton, Samuel Kortum

Gary Gereffi

Robert C. Feenstra, Robert C. Feenstra, Gordon H. Hanson et al.

A. Wood

Deyu Jiang, Miao Luo, Changxi Liu et al.

In this study, a Ti1.5Nb1Ta0.5Zr1Mo0.5 (TNTZM) high-entropy alloy was fabricated using laser powder bed fusion (LPBF). By integrating 63 sets of parameter trials with machine learning (ML) models, an optimised process window was identified, achieving a density of up to 99.9%. The combination of relatively high laser power and low scanning speed resulted in the formation of a stable cellular structure. Subsequent heat treatments at 700, 850, and 1000°C showed that while small-angle misorientations developed at cell-wall interfaces and medium-entropy (Ti–Zr–Mo) second-phase particles precipitated preferentially in the cell walls, the overall cellular architecture remained intact. Mechanical testing showed that these heat-treated samples exhibited yield strengths over 150 MPa higher than the as-built samples, while still retaining nearly 50% ductility under short-term heat treatment. In particular, small-angle grain boundaries and nanoscale second-phase particles together reinforce the cell walls and promote intracellular dislocation accumulation, thereby improving the overall mechanical properties of the alloy. These results demonstrate that combining ML-guided process design with targeted heat treatment is an effective method for additive manufacturing of refractory HEAs with high density and mechanical properties.

Igor Ivkić, Tobias Buhmann, Burkhard List

The global manufacturing landscape is undergoing a fundamental shift from resource-intensive mass production to sustainable, localised manufacturing. This paper presents a comprehensive analysis of a Cloud Crafting Platform that enables Manufacturing as a Service (MaaS) through additive manufacturing technologies. The platform connects web shops with local three-dimensional (3D) printing facilities, allowing customers to purchase products that are manufactured on-demand in their vicinity. We present the platform's Service-Oriented Architecture (SOA), deployment on the Microsoft Azure cloud, and integration with three different 3D printer models in a testbed environment. A detailed cost-benefit analysis demonstrates the economic viability of the approach, which generates significant profit margins. The platform implements a weighted profit-sharing model that fairly compensates all stakeholders based on their investment and operational responsibilities. Our results show that on-demand, localised manufacturing through MaaS is not only technically feasible but also economically viable, while reducing environmental impact through shortened supply chains and elimination of inventory waste. The platform's extensible architecture allows for future integration of additional manufacturing technologies beyond 3D printing.

Kai Xu, Hang Zhao, Ruizhen Hu et al.

Driven by breakthroughs in next-generation artificial intelligence, embodied intelligence is rapidly advancing into industrial manufacturing. In flexible manufacturing, industrial embodied intelligence faces three core challenges: accurate process modeling and monitoring under limited perception, dynamic balancing between flexible adaptation and high-precision control, and the integration of general-purpose skills with specialized industrial operations. Accordingly, this survey reviews existing work from three viewpoints: Industrial Eye, Industrial Hand, and Industrial Brain. At the perception level (Industrial Eye), multimodal data fusion and real-time modeling in complex dynamic settings are examined. At the control level (Industrial Hand), flexible, adaptive, and precise manipulation for complex manufacturing processes is analyzed. At the decision level (Industrial Brain), intelligent optimization methods for process planning and line scheduling are summarized. By considering multi-level collaboration and interdisciplinary integration, this work reveals the key technological pathways of embodied intelligence for closed-loop optimization of perception-decision-execution in manufacturing systems. A three-stage evolution model for the development of embodied intelligence in flexible manufacturing scenarios, comprising cognition enhancement, skill transition, and system evolution, is proposed, and future development trends are examined, to offer both a theoretical framework and practical guidance for the interdisciplinary advancement of industrial embodied intelligence in the context of flexible manufacturing.

F. Wang, N. Cooper, D. Johnson et al.

The development of quantum technology has opened up exciting opportunities to revolutionize computing and communication, timing and navigation systems, enable non-invasive imaging of the human body, and probe fundamental physics with unprecedented precision. Alongside these advancements has come an increase in experimental complexity and a correspondingly greater dependence on compact, efficient and reliable hardware. The drive to move quantum technologies from laboratory prototypes to portable, real-world instruments has incentivized miniaturization of experimental systems relating to a strong demand for smaller, more robust and less power-hungry quantum hardware and for increasingly specialized and intricate components. Additive manufacturing, already heralded as game-changing for many manufacturing sectors, is especially well-suited to this task owing to the comparatively large amount of design freedom it enables and its ability to produce intricate three-dimensional forms and specialized components. Herein we review work conducted to date on the application of additive manufacturing to quantum technologies, discuss the current state of the art in additive manufacturing in optics, optomechanics, magnetic components and vacuum equipment, and consider pathways for future advancement. We also give an overview of the research and application areas most likely to be impacted by the deployment of additive manufacturing techniques within the quantum technology sector.

Yong Zhou, Kun Wang, Junkun Mao et al.

Achieving a high sensitivity for practical applications has always been one of the main developmental directions for wearable flexible pressure sensors. This paper introduces a laser speckle grayscale lithography system and a novel method for fabricating random conical array microstructures using grainy laser speckle patterns. Its feasibility is attributed to the autocorrelation function of the laser speckle intensity, which adheres to a first-order Bessel function of the first kind. Through objective speckle size and exposure dose manipulations, we developed a microstructured photoresist with various micromorphologies. These microstructures were used to form polydimethylsiloxane microstructured electrodes that were used in flexible capacitive pressure sensors. These sensors exhibited an ultra-high sensitivity: 19.76 kPa−1 for the low-pressure range of 0–100 Pa. Their minimum detection threshold was 1.9 Pa, and they maintained stability and resilience over 10,000 test cycles. These sensors proved to be adept at capturing physiological signals and providing tactile feedback, thereby emphasizing their practical value.

Yunqing Li, Xiaorui Liu, Binil Starly

In the current landscape, the predominant methods for identifying manufacturing capabilities from manufacturers rely heavily on keyword matching and semantic matching. However, these methods often fall short by either overlooking valuable hidden information or misinterpreting critical data. Consequently, such approaches result in an incomplete identification of manufacturers' capabilities. This underscores the pressing need for data-driven solutions to enhance the accuracy and completeness of manufacturing capability identification. To address the need, this study proposes a Graph Neural Network-based method for manufacturing service capability identification over a knowledge graph. To enhance the identification performance, this work introduces a novel approach that involves aggregating information from the graph nodes' neighborhoods as well as oversampling the graph data, which can be effectively applied across a wide range of practical scenarios. Evaluations conducted on a Manufacturing Service Knowledge Graph and subsequent ablation studies demonstrate the efficacy and robustness of the proposed approach. This study not only contributes a innovative method for inferring manufacturing service capabilities but also significantly augments the quality of Manufacturing Service Knowledge Graphs.

Cuixia Li, Liqiang Liu, Li Shi

Intelligent manufacturing is a new model that uses advanced technologies such as the Internet of Things, big data, and artificial intelligence to improve the efficiency and quality of manufacturing production. As an important support to promote the transformation and upgrading of the manufacturing industry, cloud service optimization has received the attention of researchers. In recent years, remarkable research results have been achieved in this field. For the sustainability of intelligent manufacturing platforms, in this paper we summarize the process of cloud service optimization for intelligent manufacturing. Further, to address the problems of dispersed optimization indicators and nonuniform/unstandardized definitions in the existing research, 11 optimization indicators that take into account three-party participant subjects are defined from the urgent requirements of the sustainable development of intelligent manufacturing platforms. Next, service optimization algorithms are classified into two categories, heuristic and reinforcement learning. After comparing the two categories, the current key techniques of service optimization are targeted. Finally, research hotspots and future research trends of service optimization are summarized.

Martin Nisser

Fabrication today relies on disparate, large machines spread across industrial facilities. These are operated by domain experts to construct and assemble artefacts in sequential steps from large numbers of parts. This traditional, centralized mass manufacturing paradigm is characterized by large capital costs and inflexibility to changing needs, complex global supply chains hinged on economic and political stability, and waste and over-manufacturing of uniform artefacts that fail to meet the technical and personal needs of today's diverse individuals and use cases. Today, these challenges are particularly severe at points of need, such as the space environment. The space environment is remote and unpredictable, and the ability to manufacture in situ offers unique opportunities to address new challenges as they arise. However, the challenges faced in space are often mirrored on Earth. In hospitals, disaster zones, low resource environments and laboratories, the ability to manufacture customized artefacts at points of need can significantly enhance our ability to respond rapidly to unforeseen events. In this thesis, I introduce digital fabrication platforms with co-developed hardware and software that draw on tools from robotics and human-computer interaction to automate manufacturing of customized artefacts at the point of need. Highlighting three research themes across fabrication machines, modular assembly, and programmable materials, the thesis will cover a digital fabrication platform for producing functional robots, a modular robotic platform for in-space assembly deployed in microgravity, and a method for programming magnetic material to selectively assemble.

Siddarth Reddy Karuka, Abhinav Sunderrajan, Zheng Zheng et al.

Errors or failures in a high-volume manufacturing environment can have significant impact that can result in both the loss of time and money. Identifying such failures early has been a top priority for manufacturing industries and various rule-based algorithms have been developed over the years. However, catching these failures is time consuming and such algorithms cannot adapt well to changes in designs, and sometimes variations in everyday behavior. More importantly, the number of units to monitor in a high-volume manufacturing environment is too big for manual monitoring or for a simple program. Here we develop a novel program that combines both rule-based decisions and machine learning models that can not only learn and adapt to such day-to-day variations or long-term design changes, but also can be applied at scale to the high number of manufacturing units in use today. Using the current state-of-the-art technologies, we then deploy this program at-scale to handle the needs of ever-increasing demand from the manufacturing environment.

Igor Ivkić, Tobias Buhmann, Burkhard List et al.

The landscape of traditional industrial manufacturing is undergoing a pivotal shift from resource-intensive production and long supply chains to more sustainable and regionally focused economies. In this evolving scenario, the move towards local, on-demand manufacturing is emerging as a remedy to the environmentally damaging practice of mass-producing products in distant countries and then transporting them over long distances to customers. This paradigm shift significantly empowers customers, giving them greater control over the manufacturing process by enabling on-demand production and favouring local production sites over traditional mass production and extensive shipping practices. In this position paper we propose a cloud-native Manufacturing as a Service (MaaS) platform that integrates advances in three-dimensional (3D) printing technology into a responsive and eco-conscious manufacturing ecosystem. In this context, we propose a high-level architectural design for a cloud-based MaaS platform that connects web shops of local stores with small and medium-sized enterprises (SMEs) operating 3D printers. Furthermore, we outline an experimental design, including a cost-benefit analysis, to empirically evaluate the operational effectiveness and economic feasibility in a cloud-based additive manufacturing ecosystem. The proposed cloud-based MaaS platform enables on-demand additive manufacturing and opens up a profit sharing opportunity between different stakeholders.

Mohammad Azami, Zahra Kazemi, Sare Moazen et al.

As humankind prepares to establish outposts and infrastructure on the Moon, the ability to manufacture parts and buildings on-site is crucial. While transporting raw materials from Earth can be costly and time-consuming, in-situ resource utilization (ISRU) presents an attractive alternative. This review paper aims to provide a thorough examination of the current state and future potential of Lunar-based manufacturing and construction (LBMC), with a particular focus on the prospect of utilizing in-situ resources and additive manufacturing. The paper analyzes existing research on LBMC from various perspectives, including different manufacturing techniques and compositions, the potential of ISRU for LBMC, characterization of built parts and structures, the role of energy sources and efficiency, the impact of low-gravity and vacuum conditions, and the feasibility of using artificial intelligence, automation, and robotics. By synthesizing these findings, this review offers valuable insights into the challenges and opportunities that lie ahead for LBMC.

Jonghan Lim, Birgit Vogel-Heuser, Ilya Kovalenko

Traditional manufacturing faces challenges adapting to dynamic environments and quickly responding to manufacturing changes. The use of multi-agent systems has improved adaptability and coordination but requires further advancements in rapid human instruction comprehension, operational adaptability, and coordination through natural language integration. Large language models like GPT-3.5 and GPT-4 enhance multi-agent manufacturing systems by enabling agents to communicate in natural language and interpret human instructions for decision-making. This research introduces a novel framework where large language models enhance the capabilities of agents in manufacturing, making them more adaptable, and capable of processing context-specific instructions. A case study demonstrates the practical application of this framework, showing how agents can effectively communicate, understand tasks, and execute manufacturing processes, including precise G-code allocation among agents. The findings highlight the importance of continuous large language model integration into multi-agent manufacturing systems and the development of sophisticated agent communication protocols for a more flexible manufacturing system.

A. Levinson