Hasil untuk "Manufacturing industries"

Glenn Ellison, E. Glaeser

Andreas Felsberger, Fahham Hasan Qaiser, A. Choudhary et al.

Abstract Industry 4.0 offers massive potential for implementing sustainability, which is a growing concern for global manufacturing industries. This paper investigates the impact of the implementation of Industry 4.0 with specific emphasis on digital transformation on the sustainability dimensions of European manufacturing industries. In doing so, we propose a framework to identify the implications of Industry 4.0 on the reconciliation of the firm’s existing and new dynamic capabilities, competencies, and market requirements to achieve sustainable competitive advantage. Using a multiple case research design, we study six European manufacturing companies, including aerospace manufacturing (AM) and electronic component and systems (ECS) manufacturing. The novelty of our study lies in developing a set of theoretical propositions that reveals interrelations between Industry 4.0, the dynamic capabilities of the firm and distinct dimensions of sustainability. Our findings show that the reconciliation of dynamic capabilities mediates the impact of Industry 4.0 on economic, environmental, and social aspects. The study provides insights to practitioners to strengthen their dynamic capabilities in order to achieve sustainability while implementing Industry 4.0. Moreover, the findings also facilitate investment decisions in Industry 4.0 projects.

Uttam U. Deshpande, Supriya Shanbhag, Ramesh Koti et al.

Phone calls are strictly forbidden in certain locations due to the potential security threats. Mobile phones’ growing capabilities have also increased the risk of their misuse in places that are restricted, like manufacturing plants. Unauthorized mobile phone use in these environments can lead to significant safety hazards, operational disruptions, and security breaches. There is an urgent need to develop an intelligent system that can identify the presence of individuals as well as cellphone usage. We propose an advanced Artificial Intelligence and Computer Vision-based real-time cell phone detection system to detect mobile phone usage in restricted zones. Modern deep learning approaches, such as YOLOv8 for real-time object detection to accurately detect cell phone usage, are combined with dense layers of ResNet-50 to perform image classification tasks. We highlight the critical need for such detection systems in manufacturing settings and discuss the specific challenges encountered. To support this research, we have developed a custom dataset of 2,150 images, which features a diverse array of images with varying foreground and background elements to reflect real-world conditions. Our experimental results demonstrate that YOLOv8 achieves a Mean Average Precision (mAP50) of 49.5% at 0.5 IoU for cellphone detection tasks and an accuracy of 96.03% for prediction tasks. These findings underscore the effectiveness of our AI and CV-based system in detecting unauthorized mobile phone usage in restricted zones.

Bo Zhu, Yufei Zhang, Xiru Li et al.

Given the strong interdependencies between the economic and financial systems, nonfinancial firms have a significant impact on systemic risk. In this scenario, we examine the influence of bank liquidity hoarding on risk spillovers of nonfinancial firms, as it can impact firms' financing environment and operational conditions. Using data from 118 publicly listed Chinese companies between 2008 and 2021, we find that bank liquidity hoarding increases nonfinancial firms' risk spillovers, especially in the manufacturing and real estate industries. Furthermore, the path analysis results show that bank liquidity hoarding exacerbates extreme risk spillovers by increasing financing costs and intensifying corporate maturity mismatches. Moreover, the effect of bank liquidity hoarding is mitigated in firms with higher profitability and lower corporate financialization. This research bolsters the efficacy of macroprudential policies aimed at managing systemic risks amplified by bank liquidity hoarding within the financial and economic systems.

En LEI

Non-metallic minerals such as crystalline graphite, fluorite, high-purity quartz and boron are widely used in strategic emerging industries such as high-end equipment manufacturing, biomedicine, new materials, new energy and new generation information technology. This paper comprehensively summarizes the characteristics of strategic non-metallic mineral resources in Qinghai, and completes the analysis and evaluation of their availability by mineral types in combination with the beneficiation and purification test results of graphite, quartz, talc and garnet, and the processing technical performance of fluorite, boron ore and barite. According to the demand and development trend of lithium battery, new energy, new materials and other industries in the province for various mineral raw materials, it is considered that the five minerals of crystalline graphite, fluorite, high-purity quartz, boron ore and barite have the most development and utilization prospects. The research results will provide a basis for the development of mineral resources and the layout of related industries in Qinghai.

Mohamed Abdel-Basset, Weiping Ding, Rehab Mohamed et al.

Ivan Guevara, Stephen Ryan, Amandeep Singh et al.

Industry 4.0 is positioned at the junction of different disciplines, aiming to re-engineer processes and improve effectiveness and efficiency. It is taking over many industries whose traditional practices are being disrupted by advances in technology and inter-connectivity. In this context, enhanced agriculture systems incorporate new components that are capable of generating better decision making (humidity/temperature/soil sensors, drones for plague detection, smart irrigation, etc.) and also include novel processes for crop control (reproducible environmental conditions, proven strategies for water stress, etc.). At the same time, advances in model-driven development (MDD) simplify software development by introducing domain-specific abstractions of the code that makes application development feasible for domain experts who cannot code. XMDD (eXtreme MDD) makes this way to assemble software even more user-friendly and enables application domain experts who are not programmers to create complex solutions in a more straightforward way. Key to this approach is the introduction of high-level representations of domain-specific functionalities (called SIBs, service-independent building blocks) that encapsulate the programming code and their organisation in reusable libraries, and they are made available in the application development environment. This way, new domain-specific abstractions of the code become easily comprehensible and composable by domain experts. In this paper, we apply these concepts to a smart agriculture solution, producing a proof of concept for the new methodology in this application domain to be used as a portable demonstrator for MDD in IoT and agriculture in the Confirm Research Centre for Smart Manufacturing. Together with model-driven development tools, we leverage here the capabilities of the Nordic Thingy:53 as a multi-protocol IoT prototyping platform. It is an advanced sensing device that handles the data collection and distribution for decision making in the context of the agricultural system and supports edge computing. We demonstrate the importance of high-level abstraction when adopting a complex software development cycle within a multilayered heterogeneous IT ecosystem.

Zhibin Zhang, Dian Wang

Based on the vertical connection between upstream and downstream industries, a unique theoretical model is constructed to analyse the impact mechanism of the opening of producer services on downstream manufacturing wage growth. The empirical tests are carried out using the data of China's manufacturing listed companies from 1999 to 2020. Our findings indicate that the opening of producer services has an inverted-U-shaped impact on downstream manufacturing wage growth, and the average level of the opening of producer services in the sample period is lower than the corresponding threshold. Overall, it is in the stage of promoting the wage growth of the downstream manufacturing industry. The opening of producer services mainly affects the wage growth of the downstream manufacturing industry through two channels: labour productivity and labour income share. The results of heterogeneity analysis show that the wages of capital and technology-intensive and low-competitive manufacturing industries are relatively strongly promoted by the opening of producer services. Therefore, promoting the orderly opening of producer services and strengthening the technological links between industries will help promote the wage growth of downstream manufacturing industries.

Osei-Agyeman Yeboah, Nicholas Mensah Amoah, Kwadwo Antwi-Wiafe

The high price of energy due to the green energy policy will cause adjustments across the U.S. economy is predicted in the present computable general equilibrium with specific factors model. This includes energy input, especially electricity with capital and labor to produce manufacturing and service goods. 2022 labor, energy, and sector-specific capital input data on U.S. manufacturing, service, and agricultural sectors is applied to specific factors of the computable general equilibrium model. The model, which assumes constant returns, full employment, competitive pricing, and perfect labor mobility across industries hypothesizes a range of price changes due to project potential adjustments in factor prices and outputs. The U.S manufacturing sector is revealed to have a higher degree of noncompetitive pricing for energy factor inputs, but not on labor and capital as advocates for green energy tout by the new technology. The policy has virtually no significant impact on the service and agricultural sectors. The high price of green energy will cause an elastic decrease in all energy inputs. The output from energy-intensive manufacturing only rises in the long run by 4 % while service and agriculture fall. Clear winners are the owners of energy resources through their price-searching behavior. This includes the government, which owns a large share of hydrocarbon reserves.

Rahul Sharma, Maziar Raissi, Y. B. Guo

Compared to physics-based computational manufacturing, data-driven models such as machine learning (ML) are alternative approaches to achieve smart manufacturing. However, the data-driven ML's "black box" nature has presented a challenge to interpreting its outcomes. On the other hand, governing physical laws are not effectively utilized to develop data-efficient ML algorithms. To leverage the advantages of ML and physical laws of advanced manufacturing, this paper focuses on the development of a physics-informed machine learning (PIML) model by integrating neural networks and physical laws to improve model accuracy, transparency, and generalization with case studies in laser metal deposition (LMD).

Reza Shahabi-Shahmiri, Reza Tavakkoli-Moghaddam, Zdenek Hanzalek et al.

Identifying appropriate manufacturing systems for products can be considered a pivotal manufacturing task contributing to the optimization of operational and planning activities. It has gained importance in the food industry due to the distinct constraints and considerations posed by perishable and non-perishable items in this problem. Hence, this study proposes a new mathematical model according to knowledge discovery as well as an assignment model to optimize manufacturing systems for perishable, non-perishable, and hybrid products tailored to meet their unique characteristics. In the presented model, three objective functions are taken into account: (1) minimizing production costs by assigning the products to the right set of manufacturing systems, (2) maximizing the product quality by assigning the products to the systems, and (3) minimizing total CO2 emissions of the machines. A numerical example is utilized to evaluate the performance of AUGMECON2VIKOR compared to AUGMECON2. The results show that AUGMECON2VIKOR obtains superior Pareto solutions across all objective functions. Furthermore, the sensitivity analysis explores the positive green impacts, influencing both cost and quality.

Xavier Guidetti, Nathan Mingard, Raul Cruz-Oliver et al.

In material extrusion additive manufacturing, the extrusion process is commonly controlled in a feed-forward fashion. The amount of material to be extruded at each printing location is pre-computed by a planning software. This approach is inherently unable to adapt the extrusion to external and unexpected disturbances, and the quality of the results strongly depends on a number of modeling and tuning parameters. To overcome these limitations, we propose the first framework for Force Controlled Printing for material extrusion additive manufacturing. We utilize a custom-built extruder to measure the extrusion force in real time, and use this quantity as feedback to continuously control the material flow in closed-loop. We demonstrate the existence of a strong correlation between extrusion force and line width, which we exploit to deposit lines of desired width in a width range of 33 % up to 233 % of the nozzle diameter. We also show how Force Controlled Printing outperforms conventional feed-forward extrusion in print quality and disturbance rejection, while requiring little tuning and automatically adapting to changes in the hardware settings. With no adaptation, Force Controlled Printing can deposit lines of desired width under severe disturbances in bed leveling, such as at layer heights ranging between 20 % and 200 % of the nominal height.

Jay Lee, Dai-Yan Ji, Yuan-Ming Hsu

This paper presents a topological analytics approach within the 5-level Cyber-Physical Systems (CPS) architecture for the Stream-of-Quality assessment in smart manufacturing. The proposed methodology not only enables real-time quality monitoring and predictive analytics but also discovers the hidden relationships between quality features and process parameters across different manufacturing processes. A case study in additive manufacturing was used to demonstrate the feasibility of the proposed methodology to maintain high product quality and adapt to product quality variations. This paper demonstrates how topological graph visualization can be effectively used for the real-time identification of new representative data through the Stream-of-Quality assessment.

Steve Yuwono, Ahmar Kamal Hussain, Dorothea Schwung et al.

In this study, we introduce Modular State-based Stackelberg Games (Mod-SbSG), a novel game structure developed for distributed self-learning in modular manufacturing systems. Mod-SbSG enhances cooperative decision-making among self-learning agents within production systems by integrating State-based Potential Games (SbPG) with Stackelberg games. This hierarchical structure assigns more important modules of the manufacturing system a first-mover advantage, while less important modules respond optimally to the leaders' decisions. This decision-making process differs from typical multi-agent learning algorithms in manufacturing systems, where decisions are made simultaneously. We provide convergence guarantees for the novel game structure and design learning algorithms to account for the hierarchical game structure. We further analyse the effects of single-leader/multiple-follower and multiple-leader/multiple-follower scenarios within a Mod-SbSG. To assess its effectiveness, we implement and test Mod-SbSG in an industrial control setting using two laboratory-scale testbeds featuring sequential and serial-parallel processes. The proposed approach delivers promising results compared to the vanilla SbPG, which reduces overflow by 97.1%, and in some cases, prevents overflow entirely. Additionally, it decreases power consumption by 5-13% while satisfying the production demand, which significantly improves potential (global objective) values.

Chiara Franciosi, A. Voisin, S. Miranda et al.

Abstract The current societal, industrial and governmental environment makes sustainability requirements ubiquitous, pushing manufacturing organisations to reach new sustainability targets. Hence, sustainable manufacturing attracted much attention over the last decade as an emerging manufacturing approach, aiming to empower enterprises to meet sustainability challenges. In such a goal, maintenance is major lever for sustainable manufacturing because it provides the company with the ability to keep its production system efficient and its product at the required quality. Maintenance affects production volume and costs, asset performance, equipment availability, final product quality, workers and end-users health and safety, surrounding natural environment and social welfare. The goal of this paper is two-fold. Firstly, a systematic literature review was performed to analyse the relationship between maintenance and sustainability issues, the maintenance impacts on sustainability of manufacturing industries and, finally, to identify the indicators used for assessing such impacts. The conducted literature review indicated that maintenance impacts on sustainability should be better investigated, and the relationships between maintenance processes and sustainability indicators should be defined and formalised. Thanks to this research challenge, the second goal of this study was to provide an original conceptual framework for measuring maintenance impacts on sustainability. Therefore, maintenance impacts on sustainability and the relationships between sustainability indicators and maintenance processes, were identified. The developed framework can guide and help several stakeholders to define direct and indirect impacts of maintenance on sustainability aspects, to select the indicators of interest for measuring such impacts, and to be more aware about maintenance and sustainability relationship.

C. Singh, D. Singh, J. S. Khamba

PurposeGreen Lean concepts offer methods for managing manufacturing organizations with the goal of improving organizational performance. Green Lean practices are good options to increase the environmental and operational performance of manufacturing industries. However, there are some barriers to implement Green Lean in manufacturing industries. This paper aims to identify these barriers by reviewing the literature and analyze inter-relationships amongst selected barriers.Design/methodology/approachThis paper deals with the identification of barriers to the adoption of Green Lean practices in manufacturing industries. Using the DEMATEL approach and using the insights of experts, a cause and effect relationship diagram was generated through which the effect of barriers was analyzed.FindingsTwelve barriers were categorized in terms of cause and effect, and the interrelationships of barriers were also analyzed. Threshold value is calculated as 0.134 and the values lower than a were eliminated to obtain the digraph. “Resistance to change,” “lack of top management commitment” and “lack of training to employees” are the most prominent barriers on the basis of their prominent score.Research limitations/implicationsAnalysis in the research is highly dependent on expert judgments and opinions may be biased. However, the initial matrix obtained from the experts is hindered by the ambiguity about some relationships. But this can be improved by using fuzzy and grey set theories. The barriers used for the analysis are not from a specific type of manufacturing industry.Practical implicationsThe findings will help the manufacturing organizations to simplify the most important barriers, the least significant barriers and the relationships between these barriers. This Berlin knowledge will enable administrators to increase awareness of the barriers in Green Lean implementation. “The top management commitment” and “government support” are most important for the removal of barriers to Green Lean strategies.Originality/valueVery few scholars have used the DEMATEL approach to examine the sequence of the barriers to Green Lean implementation. The present study attempts to incorporate the DEMATEL model to assess the sequence of barriers to the implementation of Green Lean. This study investigates the degree of influence of barriers on each other and categorizes the barriers into cause and effect groups. This study is also intended to pave the way for future research in the path of the elimination of barriers to Green Lean strategies.

Deusdedith Pastory Maganga, Ismail W. R. Taifa

PurposeThis research aimed at developing the Quality 4.0 transition framework for Tanzanian manufacturing industries.Design/methodology/approachThe survey method was used in this study to gather practitioners' perspectives. The approach included open-ended and closed-ended structured questionnaires to assess respondents' perceptions of Quality 4.0 awareness and manufacturers' readiness to transit to Quality 4.0. The study's objective was to adopt non-probability and purposive sampling strategies. The study focused on fifteen Tanzanian manufacturing industries. The data were analysed qualitatively and quantitatively using MAXQADA 2020 and Minitab 20 software packages, respectively.FindingsThe study demonstrated a high level of awareness of Quality 4.0 among Tanzanian manufacturing industries (i.e. 100% in Quality 4.0 traditional attributes and 53% in Quality 4.0 modern attributes). Individuals acquire knowledge in various ways, including through quality training, work experience, self-reading and Internet surfing. The result also revealed that most manufacturing industries in Tanzania use Quality 3.0 or a lower approach to manage quality. However, Tanzanian manufacturing industries are ready to embrace Quality 4.0 since practitioners are aware of the concepts and could see benefits such as customer satisfaction, product improvement, process and continuous improvement, waste reduction and decision support when using the Quality 4.0 approach. The challenges hindering Quality 4.0 adoption in Tanzania include reliable electricity, high-speed Internet and infrastructure inadequacy to support the adoption, skilled workforces familiar with Quality 4.0-enabled technologies and a financial set-up to support technology investment. Moreover, the study developed a transition framework for an organisation to transition from traditional quality approaches such as quality control, quality assurance and total quality management to Quality 4.0, a modern quality approach aligned with the fourth industrial revolution era.Research limitations/implicationsThe current study solely looked at manufacturing industries, leaving other medical, service, mining and construction sectors. Furthermore, no focus was laid on the study's Quality 4.0 implementation frameworks.Originality/valueThis is probably the first Quality 4.0 transition framework for Tanzanian manufacturing industries, perhaps with other developing countries.

Priyank Verma, J. Breslin, Donna O’Shea

The rapid development in manufacturing industries due to the introduction of IIoT devices has led to the emergence of Industry 4.0 which results in an industry with intelligence, increased efficiency and reduction in the cost of manufacturing. However, the introduction of IIoT devices opens up the door for a variety of cyber threats in smart industries. The detection of cyber threats against such extensive, complex, and heterogeneous smart manufacturing industries is very challenging due to the lack of sufficient attack traces. Therefore, in this work, a Federated Learning enabled Deep Intrusion Detection framework is proposed to detect cyber threats in smart manufacturing industries. The proposed FLDID framework allows multiple smart manufacturing industries to build a collaborative model to detect threats and overcome the limited attack example problem with individual industries. Moreover, to ensure the privacy of model gradients, Paillier-based encryption is used in communication between edge devices (representative of smart industries) and the server. The deep learning-based hybrid model, which consists of a Convolutional Neural Network, Long Short Term Memory, and Multi-Layer Perceptron is used in the intrusion detection model. An exhaustive set of experiments on the publically available dataset proves the effectiveness of the proposed framework for detecting cyber threats in smart industries over the state-of-the-art approaches.

Hai-Hsuan Cheng, Wan-Sheng Yu, Shu-Chuang Tseng et al.

Abstract According to the Taiwan Water Resources Agency, Ministry of Economic Affairs, the average water demand shortage is 530.6 million m3 yr−1 during the period of 2011 to 2019, and the situation will worsen in the near future due to global climate change. Therefore, reclaimed water has been an important new water source in Taiwan, particularly for industrial consumers such as high-tech industries in Science Parks. In order to meet the targeted reclaimed water supply of 1.32 million m3 d−1 (CMD) in 2031, Taiwan is focusing on two major reclaimed water sources, including reclaimed water from high water-consuming industries and municipal wastewater treatment plants. This report reviews current technologies used for reclaimed water including units for pretreatment, desalting, polishing, and reclamation. Case studies in Taiwan including reclaimed water from high water-consuming industries such as thin film transistor-liquid crystal display (TFT-LCD) and semiconductor industries, as well as from municipal wastewater treatment plants are presented. The TFT-LCD company Innolux and semiconductor company Advaned Semiconductor Engineering have implemented total recycled water system to recycle and reclaim wastewater from manufacturing processes, achieving a total recycled water of 290 million m3 yr−1 with about 97% recovery and 3.5 million m3 yr−1 with 80% recovery, respectively. The Fengshan reclaimed water treatment plant produces 40,436 CMD reclaimed water from municipal wastewater for the China Steel Cooperation’s steel-making processes, at an overall operation and maintenance cost of 11.5 NT dollars m−3. Meanwhile the Yongkang plant produces 15,500 CMD of reclaimed water for semiconductor and TFT-LCD manufacturing processes at an overall operation and maintenance costs of 25.8 NT dollars m−3, which is due to low urea and boron limits requested by the user. Finally, challenges and future prospects for promoting the use of reclaimed water to meet the targeted supply in 2031 will be discussed.

Juliana Botelho Moreira, Thaisa Duarte Santos, Camila Gonzales Cruz et al.

The use of natural polymers has increased due to concern about environmental pollution caused by plastics and emerging pollutants from fossil fuels. In this context, polysaccharides from macroalgae and microalgae arise as natural and abundant resources for various biological, biomedical, and food applications. Different nanomaterials are produced from these polysaccharides to act as effective carriers in the food and pharmaceutical industry: drug and nutrient carriers, active compound encapsulation, and delivery of therapeutic agents to tumor tissues. Polysaccharides-based nanomaterials applied as functional ingredients incorporated into foods can improve texture properties and decrease the caloric density of food products. These nanostructures also present the potential for developing food packaging with antioxidant and antimicrobial properties. In addition, polysaccharides-based nanomaterials are biocompatible, biodegradable, and safe for medical practices to prevent and manage various chronic diseases, such as diabetes, obesity, and cardiovascular disease. In this sense, this review article addresses the use of algal polysaccharides for manufacturing nanomaterials and their potential applications in food and biomedical areas. In addition, the paper discusses the general aspects of algae as a source of polysaccharides, the nanomaterials produced from these polymers, as well as recent studies and the potential use of algal polysaccharides for industries.