Hasil untuk "Manufacturing industries"

E. Mansfield

Eric Sandelands

Pinglin He, Yulong Sun, Ying Zhang et al.

ABSTRACT In this article, we use an event study approach to empirically study the market performance and response trends of Chinese industries to the COVID-19 pandemic. The study found that transportation, mining, electricity & heating, and environment industries have been adversely impacted by the pandemic. However, manufacturing, information technology, education and health-care industries have been resilient to the pandemic.

T. Olsen, Brian Tomlin

Industry 4.0 connotes a new industrial revolution centered around cyber-physical systems. It posits that the real-time connection of physical and digital systems, along with new enabling technologies, will change the way that work is done and therefore, how work should be managed. It has the potential to break, or at least change, the traditional operations trade-offs among the competitive priorities of cost, flexibility, speed, and quality. This article describes the technologies inherent in Industry 4.0 and the opportunities and challenges for research in this area. The focus is on goods-producing industries, which includes both the manufacturing and agricultural sectors. Specific technologies discussed include additive manufacturing, the internet of things, blockchain, advanced robotics, and artificial intelligence.

G. D. Goh, V. Dikshit, A. Nagalingam et al.

Sohel Ahmad, R. Schroeder

M. Yusliza, J. Yong, M. I. Tanveer et al.

Abstract This study examined the relationship between green intellectual capital and sustainable performance. While many studies have focused on sustainability, this study is one of the first that focuses exclusively on green intellectual capital. This research used survey data from 112 manufacturing firms in Malaysia. As anticipated, the results found that green intellectual capital positively influenced economic, environmental, and social performance. The findings of this study have various implications for green companies and organizations in general and green manufacturing firms in particular. The novelty of this study is unfolding the contribution of green intellectual capital as an intangible resource for organizations in achieving sustainable performance and a competitive advantage for future researchers. Manufacturing industries of developing or developed countries can enhance their cleaner production capabilities by incorporating this model as a strategy.

L. Chiffre, S. Carmignato, J. Kruth et al.

W. Dauth, S. Findeisen, Jens Suedekum

We analyze the effects of the unprecedented rise in trade between Germany and “the East” (China and Eastern Europe) in the period 1988–2008 on German local labor markets. Using detailed administrative data, we exploit the cross-regional variation in initial industry structures and use trade flows of other high-income countries as instruments for regional import and export exposure. We find that the rise of the East in the world economy caused substantial job losses in German regions specialized in import-competing industries, both in manufacturing and beyond. Regions specialized in export-oriented industries, however, experienced even stronger employment gains and lower unemployment. In the aggregate, we estimate that this trade integration has caused some 442,000 additional jobs in the economy and contributed to retaining the manufacturing sector in Germany. This is almost exclusively driven by the rise of Eastern Europe, not by China. We also conduct an analysis at the individual worker level, and find that trade had a stabilizing overall effect on employment relationships.

S. Thornhill

M. Javaid, Abid Haleem, R. Singh et al.

Artificial intelligence (AI) contributes to the recent developments in Industry 4.0. Industries are focusing on improving product consistency, productivity and reducing operating costs, and they want to achieve this with the collaborative partnership between robotics and people. In smart industries, hyperconnected manufacturing processes depend on different machines that interact using AI automation systems by capturing and interpreting all data types. Smart platforms of automation can play a decisive role in transforming modern production. AI provides appropriate information to take decision-making and alert people of possible malfunctions. Industries will use AI to process data transmitted from the Internet of things (IoT) devices and connected machines based on their desire to integrate them into their equipment. It provides companies with the ability to track their entire end-to-end activities and processes fully. This literature review-based paper aims to brief the vital role of AI in successfully implementing Industry 4.0. Accordingly, the research objectives are crafted to facilitate researchers, practitioners, students and industry professionals in this paper. First, it discusses the significant technological features and traits of AI, critical for Industry 4.0. Second, this paper identifies the significant advancements and various challenges enabling the implementation of AI for Industry 4.0. Finally, the paper identifies and discusses significant applications of AI for Industry 4.0. With an extensive review-based exploration, we see that the advantages of AI are widespread and the need for stakeholders in understanding the kind of automation platform they require in the new manufacturing order. Furthermore, this technology seeks correlations to avoid errors and eventually to anticipate them. Thus, AI technology is gradually accomplishing various goals of Industry 4.0.

Qingfei Min, Yangguang Lu, Zhiyong Liu et al.

Abstract Digital twins, along with the internet of things (IoT), data mining, and machine learning technologies, offer great potential in the transformation of today’s manufacturing paradigm toward intelligent manufacturing. Production control in petrochemical industry involves complex circumstances and a high demand for timeliness; therefore, agile and smart controls are important components of intelligent manufacturing in the petrochemical industry. This paper proposes a framework and approaches for constructing a digital twin based on the petrochemical industrial IoT, machine learning and a practice loop for information exchange between the physical factory and a virtual digital twin model to realize production control optimization. Unlike traditional production control approaches, this novel approach integrates machine learning and real-time industrial big data to train and optimize digital twin models. It can support petrochemical and other process manufacturing industries to dynamically adapt to the changing environment, respond in a timely manner to changes in the market due to production optimization, and improve economic benefits. Accounting for environmental characteristics, this paper provides concrete solutions for machine learning difficulties in the petrochemical industry, e.g., high data dimensions, time lags and alignment between time series data, and high demand for immediacy. The approaches were evaluated by applying them in the production unit of a petrochemical factory, and a model was trained via industrial IoT data and used to realize intelligent production control based on real-time data. A case study shows the effectiveness of this approach in the petrochemical industry.

Mridul Gupta, M. Khan

ABSTRACT Manufacturing processes such as casting, rolling, forging, extrusion, material removal processes, etc. are some of the common techniques used today in manufacturing industries. However, these processes must be carried out with utmost precision and precaution. Even the slightest negligence can give rise to anomalies which can be classified as defects or discontinuities depending upon their repairability. Non-Destructive Testing or NDT is amode of testing which has been adopted by industries for decades to test mass manufactured products for anomalies. This paper discusses various NDT methods, such as Visual Testing, Magnetic Particle Inspection, Penetrant Testing, Ultrasonic Testing, Radiographic Testing, Acoustic Emission and Eddy Current Testing. The paper also discusses how technical advancements have broadened the scope of NDT even in industries that may not be manufacturing oriented, indicating that NDT is not limited to detection of anomalies. Even though these developments have made NDT more favourable, this paper highlights the fact that these techniques are mostly manual and heavily dependent upon inspectors’ knowledge and experience, leaving room for errors. This paper also discusses the key challenges and future scope in the field of NDT.

Mark Doms, Timothy Dunne, Timothy Dunne

Md. Abdul Moktadir, S. Ali, Simonov Kusi‐Sarpong et al.

Abstract Researchers and practitioners are giving significant attention to Industry 4.0 due to its numerous benefits to manufacturing organizations. Several aspects of Industry 4.0 have been studied in the literature. However, studies on the challenges for implementing Industry 4.0 in manufacturing operations have received less attention. To address this gap, this study identifies a set of challenges (framework) for implementing Industry 4.0 in manufacturing industries. This framework is evaluated in the leather industry of Bangladesh aided by a novel multi-criteria decision-making method named Best-Worst method (BWM). The findings of the study showed that ‘lack of technological infrastructure’ is the most pressing challenge that may hurdle the implementation of Industry 4.0 whereas ‘environmental side-effects’ is the less among the challenges that may hinder implementation of Industry 4.0 in the Bangladeshi leather industry. This result may help decision makers, industrial managers and practitioners in the Bangladeshi leather industry to realize the actual challenges confronting them when attempting to implement Industry 4.0 and focus their attention on how to address these challenges to pave ways for a successful implementation of Industry 4.0.

Peter T. Ward, Rebecca Duray

Ana Revenga

Bouazizi Maher, Soula Mohamed, Ben Rhouma Amir

Woven laminate composites are extensively used in various industries, especially in yacht manufacturing, where a comprehensive understanding of their elastic mechanical properties, failure mechanisms, and the effects of layer stacking in E-glass/polyester composites is essential. A combined experimental and numerical approach was employed to investigate these aspects. Tensile and three-point bending tests were performed to assess the elastic mechanical properties of woven specimens, and optical microscopy was used to analyze fracture surfaces and identify failure mechanisms. For numerical analysis, a two-step homogenization method was applied. The numerical model was validated by comparing its results with experimental data.

E. Bartelsman, E. Bartelsman, W. Gray et al.

P. Nagovnak, C. Schützenhofer, M. Rahnama Mobarakeh et al.

The goals set forth by the European Green Deal require extensive preparation and coordination of all stakeholders. As a valuable tool, energy scenarios can generate the necessary information for stakeholders to envision the right steps in preparing this transition. The manufacturing industries represent an especially important sector to investigate. They are responsible for both high energy consumption and GHG emission figures on the one hand side and provide great economic value for member countries on the other. We aim to provide a close investigation of all thirteen industrial subsectors that can be used as a solid information basis both for stakeholders within the manufacturing industries and policymakers. Our approach includes all industrial production processes. We achieve this by considering both transformation processes, such as blast furnaces or industrial power plants, and final energy-application. In addition, both scope 1 and 2 emissions of manufacturing industry are assessed in an effort to transparently indicate the interdependencies of industrial decarbonisation efforts with the overall energy system. We propose the integration of a novel stakeholder-based scenario, that puts special emphasis on first-hand information on mid to long-term planning of key industrial representatives, thereby going beyond existing scenario narratives (e.g., scenarios according to the European Monitoring Mechanism). Thus, a balanced deep decarbonisation scenario using best-available technologies can be compared with existing industry plans. To address these points, we have chosen Austria as a case study. Results indicate that industry stakeholders are in general agreement on their subsector-specific technology deployment and already envision investments towards a low-carbon pathway for their respective subsectors. While today's manufacturing industries rely at large on a great diversity of (mostly fossil) energy carrier supply, deeply decarbonised manufacturing industries of the future may be based on the following main energy carriers; electricity, CO2-neutral gases, and biomass. To mitigate emissions from geogenic sources, carbon capture technologies are needed. On the other hand, the synthesis of olefins in the chemical industry may provide a sink for CO2 assuming long-term use after production. In addition to the option of using it across subsectors, captured CO2 will have to be stored or sold to other economies. Comparison of the developed scenarios allows the identification of no-regret measures to enable climate neutrality by 2050 that should be deployed as soon as possible by push and pull incentives. The model results of the two transition scenarios show the need for technology promotion as well as infrastructure development needs and allow the identification of possible corridors, focal points, and fuel shifts – on the subsector level as well as in energy policy. Among others, the modelled magnitude of renewable energy consumption shows the need for swift expansion of existing national renewable energy potentials and energy infrastructure, especially for energy intensive industry regions. In light of the current energy consumption in other economic sectors (most notably in buildings or transport) and limited renewable potentials, large import shares of national gross domestic energy consumption are likely for Austria in the future.