Hasil untuk "Manufacturing industries"

Amine Belhadi, Sachin S. Kamble, C. Jabbour et al.

Highlights • Insights into the impact of COVID-19 outbreak on automobile and airline supply chain is provided.• Integrated time-to-recovery and financial impact analysis, empirical survey and semi-structured interviews were used.• Localized supply sources and industry 4.0 technologies identified as significant strategies by automobile industry.• Business continuity by defining operations at the airport and flights perceived significant strategy by airline industry.• Real-time information sharing and cooperation among supply chain stakeholders is critical.

David S. Evans

Abdul Moktadir, Towfique Rahman, Hafizur Rahman et al.

Chinthamu Narender, Ashish, P Mathiyalagan et al.

Predictive maintenance (PdM) is one of the major methods used in modern manufacturing to realize downtime minimization, lower the cost of maintenance and maximize machine service life by analyzing the collected data using data mining methodologies. However existing works mainly focus on conventional ML models without provide systems design real world applications systems and do not include any dimension related to network security dimension, cost and benefit analyzing dimension utility dimension and light weight A.I model for edge computing. In this paper, we contribute with a systematic literature review of state-of-the-art data-mining techniques for predictive maintenance with emphasis on hybrid AI frameworks, deep learning and online data processing approaches, as well as, privacy-aware methods. We contribute by providing a number of real-world industrial use case which differentiate us from previous researched; we discuss details of cybersecurity issues in IoT-enabled PdM; and we discuss use of XAI (Explainable AI) to build interpretable models. Moreover, this survey introduces marginal AI applications in edge computing, predictive maintenance frameworks with scalability, and AI-powered anomaly identification for enhancing predictions in industrial-scale production. It also covers a review of predictive maintenance methodologies in addition to a future research agenda, highlighting emerging patterns such as digital twins, Industry 5.0, and reinforcement learning in predictive maintenance. The current study aims to bridge critical gaps in the literature and support valuable direction for researchers, industry practitioners and policymakers for effective predictive maintenance strategies and task performance.

Jiongqi Lin, Wuyin Weng, Linfan Shi et al.

The ever-increasing global demand for shrimp has spurred the growth of the shrimp farming and processing industries. Byproducts derived from shrimp processing, including shrimp heads, viscera, and shells, are underutilized and pose potential environmental pollution risks. Shrimp and its byproducts contain a wide number of components, including proteins, lipids, chitin, carotenoids, and minerals. Therefore, utilizing shrimp and its byproducts holds significant economic and environmental importance, with applications in food, pharmaceutical, and other industries. Shrimp processing technologies, including thermal and non-thermal processing techniques, are reviewed. Besides, the applications of shrimp and its byproducts are summarized, covering their use in food and nutritional supplements, development of active edible films, animal feed additives, and environmental and biotechnological applications. Additionally, the barriers and prospects of utilizing shrimp processing byproducts are also discussed. The extracted active ingredients possess various biological activities, such as antioxidant, antimicrobial, antihypertensive, and anti-inflammatory properties, and can serve as natural and safe food or feed additives or as important ingredients for functional foods and feeds due to their unique functional and nutritional characteristics. More importantly, the bioactive compounds contained in shrimp byproducts offer new approaches for the development of food additives and nutritional supplements. Looking ahead, the development and utilization of shrimp byproducts will move towards environmentally friendly directions, such as energy conversion, bioremediation technologies, and the manufacturing of bioplastics. Moreover, the integration with artificial intelligence technologies is expected to present broad prospects for development.

Tuyboyov Oybek, Baydullayev Azamat, Jeltuxin Andrey et al.

This study explores advanced methodologies for enhancing the precision and efficiency of CNC machining through the integration of experimental, computational, and simulation-based techniques. Utilizing tools such as laser interferometers, autocollimators, and predictive thermal simulations, the research addresses critical challenges in tool positioning accuracy caused by thermal fluctuations and geometric errors. Compensation strategies reduced thermal errors by 15%, while kinematic calibration mitigated angular discrepancies by 10%. Furthermore, the application of Setup-Maps (S-Maps) and Tolerance-Maps (T-Maps) optimized fixture design, reducing setup time by 25% and improving machining precision by 30%. Hybrid optimization techniques, including genetic algorithms, effectively balanced the trade-off between precision and productivity, achieving a 10% reduction in production time without sacrificing accuracy. Real-time calibration, IoT-enabled predictive maintenance, and collaborative robots (Cobots) further enhanced machining reliability, increasing operational uptime by 15% and reducing tool positioning errors by 18%. These advancements enabled sub-micron accuracy in high-precision applications, demonstrating their relevance to industries such as aerospace and medical devices. This research establishes a robust framework for improving CNC machining operations by addressing thermal and geometric errors, optimizing fixture design, and leveraging Industry 4.0 technologies. The proposed methods not only enhance machining precision but also contribute to sustainable manufacturing by minimizing downtime, energy consumption, and waste, paving the way for smarter and more efficient production systems.

Christian Arnold, Daniel Kiel, K. Voigt

The Industrial Internet of Things (IIoT) poses large impacts on business models (BM) of established manufacturing companies within several industries. Thus, this paper aims at analyzing the influence of the IIoT on these BMs with particular respect to differences and similarities dependent on varying industry sectors. For this purpose, we employ an exploratory multiple case study approach based on semi-structured expert interviews in 69 manufacturing companies from the five most important German industries. Owing the lack of previous research, our study contributes to the current state of management literature by revealing the following valuable insights with regard to industry-specific BM changes: The machine and plant engineering companies are mainly facing changing workforce qualifications, the electrical engineering and information and communication technology companies are particularly concerned with the importance of novel key partner networks, and automotive suppliers predominantly exploit IIoT-inherent benefits in terms of an increasing cost efficiency.

Liaoyuan Wang, Yuli Sun, Zhongmin Xiao et al.

With the rapid development of high-end manufacturing industries such as aerospace and national defense, the demand for metal additive manufactured parts with complex internal cavities has been steadily increasing. However, the finishing of complex internal surfaces, especially for irregularly shaped parts, remains a significant challenge due to their intricate geometries. Through a comparative analysis of common finishing methods, the distinctive characteristics and applicability of magnetic abrasive finishing (MAF) are highlighted. To meet the finishing needs of complex metal additive manufactured parts, this paper reviews the current research on magnetic abrasive finishing devices, processing mechanisms, the development of magnetic abrasives, and the MAF processes for intricate internal cavities. Future development trends in MAF for complex internal cavities in additive manufactured parts are also explored; these are (1) investigating multi-technology composite magnetic abrasive finishing equipment designed for complex internal surfaces; (2) studying the dynamic behavior of multiple magnetic abrasive particles in complex cavities and their material removal mechanisms; (3) developing high-performance magnetic abrasives suitable for demanding conditions; and (4) exploring the MAF process for intricate internal surfaces.

Yingfeng Zhang, Shuaiyin Ma, Haidong Yang et al.

Abstract Energy-intensive industries account for almost 51% of energy consumption in China. A continuous improvement in energy efficiency is important for energy-intensive industries. Cleaner production has proven itself as an effective way to improve energy efficiency and reduce energy consumption. However, there is a lack of manufacturing data due to the difficult implementation of sensors in harsh production environment, such as high temperature, high pressure, high acid, high alkali, and smoky environment which hinders the implementation of the cleaner production strategy. Thanks to the rapid development of the Internet of Things, many data can be sensed and collected in the manufacturing processes. In this paper, a big data driven analytical framework is proposed to reduce the energy consumption and emission for energy-intensive manufacturing industries. Then, two key technologies of the proposed framework, namely energy big data acquisition and energy big data mining, are utilized to implement energy big data analytics. Finally, an application scenario of ball mills in a pulp workshop of a partner company is presented to demonstrate the proposed framework. The results show that the energy consumption and energy costs are reduced by 3% and 4% respectively. These improvements can promote the implementation of cleaner production strategy and contribute to the sustainable development of energy-intensive manufacturing industries.

Yunlong Duan, Shulin Liu, Hao Cheng et al.

Abstract A considerable literature has grown up around the theme of the impact of knowledge spillover on the firm's innovation performance. Nevertheless, few literatures draw on any research into the impact of transnational knowledge spillover on innovation quality, especially from conscious and unconscious perspectives. What's more, it has been proven that absorptive capacity plays a crucial role on innovation quality, while the relationship between knowledge spillover and absorptive capacity and its impact on innovation quality have yet to be identified. This study therefore defines the transnational knowledge spillover and distinguishes it from involuntary to voluntary, analyzes their impact on the innovation quality of high-tech industries in host countries, and builds a theoretical model that involves absorptive capacity as the moderating variable. Meanwhile, this study collects data on China's high-tech manufacturing industries from 2010 to 2017 to conduct an empirical analysis and the main results were described as following: with an increase in unconscious transnational knowledge spillover, the innovation quality of high-tech manufacturing industries firstly decreases and then increases. Conversely, with an increase in conscious transnational knowledge spillover, the innovation quality of high-tech manufacturing industries firstly increases and then decreases. Impressively, absorptive capacity has a significant positive moderating effect on the relationship between transnational knowledge spillover and the innovation quality of high-tech industries in host countries.

Syed M. Hussain, Rahimah Mahat, Nek Muhammad Katbar et al.

As a result of its use in the manufacturing and construction industries, research on the flow of nanofluid is rather well-known among academics and professionals in related fields. It is helpful for electrical equipment to utilize it for cooling reasons, which has shown promising results in terms of reducing energy use. As a result, the primary objective of this research is to inspect the impacts that radiation has on the mixed convection of Walters'-B hybridity nanofluid flow of stagnant point in a horizontal circular cylinder under the circumstances of a constant heat flux. It is considered a conventional fluid despite the presence of copper (Cu) and alumina (Al2O3) nanoparticles in the water (H2O) hybridity nanofluid. To make the solution to the resulting controlling system of equations more straightforward, the numerical approach of a neural network with a back-propagation algorithm (NN-BPA) is used. It follows by clarifying how various physical characteristics, such as blended convection, thermal radiation, and stagnant movement, affect temperature, skin friction, thermal transfer, velocity, and graphical profiles of those variables. The LMNN-BPA has the quickest processing algorithm and performs well in general, corresponding to the thorough analysis. Additionally, the mixed convective and viscoelastic properties exhibit both rising and dropping developments regarding skin friction and heat transmission.

Mirabdollah Hosseini, Faezeh Moradi Haghighi

The Competitive Industrial Performance Index (CIP) measures a country's ability to produce, add value, export, and impact global trade through manufacturing industries. To improve industrial competitiveness, focus must be given to expanding production and enhancing its quality with technological advancements. Developing countries need to build technological capacity, increase production, and invest in infrastructure to upgrade their industrial competitiveness. However, Iran's Competitive Industrial Performance has fallen behind, lacking a favorable position in the region and the world. The annual reports of the UNIDO analyzing data from 1990 to 2020 shows that Iran's performance has been weak compared to similar economies. The gap between Iran and the global benchmark (Germany with a score of 0.416) and the regional benchmark (Turkey with a score of 0.117) has widened over the past three decades. Additionally, Iran's manufacturing industry production and export structures have experienced two different directions of transformation in the past two decades. From 2000 to 2010, concurrent with the Third and Fourth Development Plans, the Manufacturing Value Added share in total GDP (MVAsh) increased from 9% to 14%, and the Medium- and High-tech manufacturing Value-Added share (MHVASH) in total manufacturing value added increased from 41% to 45%. However, during the years 2010 to 2020, concurrent with the Fifth and Sixth Development Plans, both of these mentioned indicators regressed. Notably, the regression in the level of technology for high-tech products, from 0.9% to 0.5%, is continuously declining and poses a fundamental challenge for Iran's industrial competitiveness.

Muhammad Ayyaz Khan, Muhammad Ali Khan, Shahid Aziz et al.

Titanium grade 9 (Ti-3Al-2.5V) stands out as a preferred material in various industrial applications because of its suitable properties. Its applications span diverse sectors, including precision manufacturing, where it is utilized to produce honeycomb structures for advanced aeronautics, as well as for certain biomedical components. In parallel, micro-milling has gained widespread utilization across medical, aerospace, and electronic industries due to the increasing demand for miniature products in these domains. This current research study aims to explore the impact of various micro-milling process parameters—specifically, feed rate, cutting speed, and depth of cut—on the surface quality, burr formation, and tool flank wear of titanium grade 9. Research findings reveal that the feed rate plays a major role in influencing surface roughness (contribution ratio (CR): 62.96%) and burr formation (CR: 55.20%). Similarly, cutting speed and depth of cut significantly affect surface roughness, contributing 20.32% and 9.27%, respectively, but are insignificant factors for burr width. Tool flank wear is primarily influenced by cutting speed (CR: 54.02%), with feed rate contributing 33.18%. Additionally, the feed rate and cutting speed are significant factors in determining the length of the burr, with contribution ratios of 77.70% and 7.77%, respectively. Confirmatory tests conducted at optimum parameters selected from the main effects plot validated the experimental results.

Amos Muiruri, Maina Maringa, Willie du Preez

The Ti6Al4V structures in aircraft and biomedical industries are usually exposed to quasi-static loads. Therefore, understanding the quasi-static behaviour of this alloy manufactured by an additive manufacturing process is paramount. This paper documents an investigation of the quasi-static mechanical properties of various microstructures of heat-treated Ti6Al4V(ELI) parts manufactured by laser powder bed fusion (LPBF). The effects of different quasi-static strain rates on different microstructures of these samples and their strain hardening are also presented. The test samples were produced using an EOSINT M280 direct metal laser sintering (DMLS) machine and, thereafter, subdivided into three major groups, namely samples C, D and E, for high-temperature annealing at different heat treatment strategies. A universal hydraulic testing machine (UTM) was used to carry out tests at strain rates of 0.001 s⁻¹, 0.005 s⁻¹ and 0.1 s⁻¹. The three forms of LPBF Ti6Al4V(ELI) were found to be sensitive to quasi-static strain rate, whereby values of yield and flow stresses in each form of alloy increased with increasing strain rate. The order of the strength at each strain rate from the highest to the lowest was found to be samples C, D and E. The effects of strain rate on flow hardening were found to be significant in samples C and insignificant in samples D and E.

Shoufu Lin, Ji Sun, D. Marinova et al.

A. Haupt, A. Lerch

Forward osmosis (FO) is a membrane technology that uses the osmotic pressure difference to treat two fluids at a time giving the opportunity for an energy-efficient water and wastewater treatment. Various applications are possible; one of them is the application in industrial water management. In this review paper, the basic principle of FO is explained and the state-of-the-art regarding FO application in manufacturing industries is described. Examples of FO application were found for food and beverage industry, chemical industry, pharmaceutical industry, coal processing, micro algae cultivation, textile industry, pulp and paper industry, electronic industry, and car manufacturing. FO publications were also found about heavy metal elimination and cooling water treatment. However, so far FO was applied in lab-scale experiments only. The up-scaling on pilot- or full-scale will be the essential next step. Long-term fouling behavior, membrane cleaning methods, and operation procedures are essential points that need to be further investigated. Moreover, energetic and economic evaluations need to be performed before full-scale FO can be implemented in industries.

Ishaq Adeyanju Raji, Nasir Abbas, Mu’azu Ramat Abujiya et al.

While researchers and practitioners may seamlessly develop methods of detecting outliers in control charts under a univariate setup, detecting and screening outliers in multivariate control charts pose serious challenges. In this study, we propose a robust multivariate control chart based on the Stahel-Donoho robust estimator (SDRE), whilst the process parameters are estimated from phase-I. Through intensive Monte-Carlo simulation, the study presents how the estimation of parameters and presence of outliers affect the efficacy of the Hotelling <i>T</i>² chart, and then how the proposed outlier detector brings the chart back to normalcy by restoring its efficacy and sensitivity. Run-length properties are used as the performance measures. The run length properties establish the superiority of the proposed scheme over the default multivariate Shewhart control charting scheme. The applicability of the study includes but is not limited to manufacturing and health industries. The study concludes with a real-life application of the proposed chart on a dataset extracted from the manufacturing process of carbon fiber tubes.

Jinhee Yoo, Jun Yeop Lee, Hwa-Joong Kim

This study aims to examine the trend of industrial competition between the US and China, which is the most crucial determinant in the future development of the global economy. For decades, the global economy has strengthened the global production network based on the division of labor between countries. Thus, the ripple effect of competition between the two countries should be analyzed in terms of the global production network. Therefore, this study uses the product space model, which explains the development process of industries with comparative advantage by country. We constructed the model based on the products of HS 4-digit code for the 2010–2019 period. The analysis results on the trend of the industrial competitiveness of major countries are as follows. First, the current industrial competitiveness of China is concentrated on low-tech industries. In the case of high-tech items, China shows a tendency of lower export sophistication compared to major manufacturing powerhouses such as Germany, the US, Japan, and Korea. Second, with respect to the possibility of a future industrial structure upgrade evaluated by density, the trend of China overtaking other manufacturing powerhouses is observed. As implied by the product space model, the advancement of the industrial structure through active participation in international trade enhances the industrial competitiveness. Therefore, the outcome of US-China industrial competition depends on who ensures more openness and industrial complexity.

Nik Nurharyantie Nik Mohd Kamil, Sri Nur Areena Mohd Zaini, Mohd Yazid Abu

Electrical and electronic industry is one of Malaysia’s leading industries which covers around 24.5% in manufacturing production sector. With a continuous innovation of the Industry, inductor component gets higher demand from customer and it is good if there is a study to convince that those factors are really significant to the production as well. Meanwhile, the current costing being used is difficult to access the complete activities required for each workstation and need separate analysis to measure the un-used capacity in term of resources and cost. The objective of this work is to clarify the relationship between Mahalanobis-Taguchi system (MTS) and time driven activity-based costing (TDABC) in the electronic industry. The data collection is focused on inductor component by consiedring the historical data in 2018. MTS is used as a method to optimize various parameters while TDABC is used to measure the un-used capacity by constructing the time equation and capacity cost rate. There are 7 parameters considered which are condition of wire, condition of winding, condition of epoxy, condition of core, condition of lead part, condition of marking and condition of soldering. As a result, MTS is successfully developed the normal and abnormal Mahalanobis distance (MD). In February, the normal MD is 0.9998 and the abnormal is 15.6538 with 2 significant parameters with signal to noise is 0.1244. In addition, there are 3 parameters consistently influenced along 10 months such as condition of core, condition of lead part and condition of soldering and 2 parameters are not consistently influenced such as condition of epoxy and condition marking. On the other hand, the total used and un-used capacity of time are 257124.02 minutes and 5217031.43 minutes respectively while the total of used and un-used of cost are MYR6,296,493.10 and MYR6214807.07 respectively. Eventually, this work concludes that both methods are a great tool and feasible to be implemented in the electronic industry.

Ali Emrouznejad, Guo-liang Yang, G. Amin

Abstract This paper aims to address the problem of allocating the CO2 emissions quota set by government goal in Chinese manufacturing industries to different Chinese regions. The CO2 emission reduction is conducted in a three-stage phases. The first stage is to obtain the total amount CO2 emission reduction from the Chinese government goal as our total CO2 emission quota to reduce. The second stage is to allocate the reduction quota to different two-digit level manufacturing industries in China. The third stage is to further allocate the reduction quota for each industry into different provinces. A new inverse data envelopment analysis (InvDEA) model is developed to achieve our goal to allocate CO2 emission quota under several assumptions. At last, we obtain the empirical results based on the real data from Chinese manufacturing industries.