Hasil untuk "Manufacturing industries"

Yogesh Kumar Dwivedi, Laurie Hughes, Elvira Ismagilova et al.

As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology. This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.

F. Tao, He Zhang, Ang Liu et al.

Digital twin (DT) is one of the most promising enabling technologies for realizing smart manufacturing and Industry 4.0. DTs are characterized by the seamless integration between the cyber and physical spaces. The importance of DTs is increasingly recognized by both academia and industry. It has been almost 15 years since the concept of the DT was initially proposed. To date, many DT applications have been successfully implemented in different industries, including product design, production, prognostics and health management, and some other fields. However, at present, no paper has focused on the review of DT applications in industry. In an effort to understand the development and application of DTs in industry, this paper thoroughly reviews the state-of-the-art of the DT research concerning the key components of DTs, the current development of DTs, and the major DT applications in industry. This paper also outlines the current challenges and some possible directions for future work.

Saurabh Vaidya, P. Ambad, S. Bhosle

Abstract Digitization and intelligentization of manufacturing process is the need for today’s industry. The manufacturing industries are currently changing from mass production to customized production. The rapid advancements in manufacturing technologies and applications in the industries help in increasing productivity. The term Industry 4.0 stands for the fourth industrial revolution which is defined as a new level of organization and control over the entire value chain of the life cycle of products; it is geared towards increasingly individualized customer requirements. Industry 4.0 is still visionary but a realistic concept which includes Internet of Things, Industrial Internet, Smart Manufacturing and Cloud based Manufacturing. Industry 4.0 concerns the strict integration of human in the manufacturing process so as to have continuous improvement and focus on value adding activities and avoiding wastes. The objective of this paper is to provide an overview of Industry 4.0 and understanding of the nine pillars of Industry 4.0 with its applications and identifying the challenges and issues occurring with implementation the Industry 4.0 and to study the new trends and streams related to Industry 4.0.

David H. Autor, David Dorn, Gordon H. Hanson

We analyze the effect of rising Chinese import competition between 1990 and 2007 on U.S. local labor markets, exploiting cross-market variation in import exposure stemming from initial differences in industry specialization and instrumenting for U.S. imports using changes in Chinese imports by other high-income countries. Rising imports cause higher unemployment, lower labor force participation, and reduced wages in local labor markets that house import-competing manufacturing industries. In our main specification, import competition explains one-quarter of the contemporaneous aggregate decline in U.S. manufacturing employment. Transfer benefits payments for unemployment, disability, retirement, and healthcare also rise sharply in more trade-exposed labor markets.

Keliang Zhou, Taigang Liu, Lifen Zhou

Gürhan Günday, G. Ulusoy, K. Kilic et al.

Frank M. H. Neffke, M. Henning, R. Boschma

Glenn Ellison, E. Glaeser, W. Kerr

Jeffrey Miller, A. Roth

J. Dunning

A. Jaffe, K. Palmer

John Bound

K. Padachi

N. Madlool, R. Saidur, M. S. Hossain et al.

C. Eckel, J. Neary, Andy Bernard et al.

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.

Marcel FIGURA, Denis JURACKA, Jorma IMPPOLA

Artificial intelligence (AI) is becoming a significant driver of transformation in the business environment, particularly for startups, which are characterized by high levels of adaptability, innovation orientation and strategic flexibility. Startups that incorporate artificial intelligence into their structures and processes are reshaping traditional approaches to value creation. This study aims to explore how artificial intelligence influences the formation and evolution of business models in startups across different industries. The analysis draws on academic literature, case study evidence and current market observations in order to identify key areas where artificial intelligence may affect fundamental business model components. The comparative part of the study focuses on differences between AI-driven startups and traditional companies that did not emerge as startups and do not rely on artificial intelligence as a core strategic technology. The comparison is carried out within three selected industries, namely banking, automotive manufacturing and retail. The study applies the Business Model Canvas (BCM) as a conceptual tool to evaluate the configuration of business models in both categories of companies. The results of the analysis indicate that startups using artificial intelligence are creating new types of business models in which AI plays a significant role in shaping the value proposition, sales channels, and revenue streams. Traditional companies, on the other hand, are not transforming their business models radically but are integrating artificial intelligence mainly in support areas. Building on these findings, this research contributes to the broader understanding of business model innovation under the influence of artificial intelligence and outlines the analytical foundations for identifying structural distinctions between AI-driven startups and traditional companies.

Fatemeh Sadat Daneshmand

Flexible manufacturing systems in Industry 4.0 require robots capable of handling objects in unstructured environments without rigid positioning constraints. This paper presents a computer vision system that enables industrial robots to detect and grasp pen components in arbitrary orientations without requiring structured trays, while maintaining robust performance under varying lighting conditions. We implement and evaluate a Mask R-CNN-based approach on a complete pen manufacturing line at ZHAW, addressing three critical challenges: object detection without positional constraints, robustness to extreme lighting variations, and reliable performance with cost-effective cameras. Our system achieves 95% detection accuracy across diverse lighting conditions while eliminating the need for structured component placement, demonstrating a 30% reduction in setup time and significant improvement in manufacturing flexibility. The approach is validated through extensive testing under four distinct lighting scenarios, showing practical applicability for real-world industrial deployment.

Arne Burdack, Maximilian Stargardt, Christoph Winkler et al.

In a world increasingly powered by renewables and aiming for greenhouse gas-neutral industrial production, the future competitiveness of todays top manufacturing countries is questioned. This study applies detailed energy system modeling to quantify the Renewable Pull, an incentive for industry relocation exerted by countries with favorable renewable conditions. Results reveal that the Renewable Pull is not a cross-industrial phenomenon but strongly depends on the relationship between energy costs and transport costs. The intensity of the Renewable Pull varies, with China, India, and Japan facing a significantly stronger effect than Germany and the United States. Incorporating national capital cost assumptions proves critical, reducing Germanys Renewable Pull by a factor of six and positioning it as the second least affected top manufacturing country after Saudi Arabia. Using Germany as a case study, the analysis moreover illustrates that targeted import strategies, especially within the EU, can nearly eliminate the Renewable Pull, offering policymakers clear options for risk mitigation.

Mandar Golvaskar, Sammy A. Ojo, Manigandan Kannan

To improve the microstructure and mechanical properties of fundamental materials including aluminum, stainless steel, superalloys, and titanium alloys, traditional manufacturing techniques have for years been utilized in critical sectors including the aerospace and nuclear industries. However, additive manufacturing has become an efficient and effective means for fabricating these materials with superior mechanical attributes, making it easier to develop complex parts with relative ease compared to conventional processes. The waste generated in additive manufacturing processes are usually in the form of powders, while that of conventional processes come in the form of chips. The current study focuses on the features and uses of various typical recycling methods for traditional and additive manufacturing that are presently utilized to recycle material waste from both processes. Additionally, the main factors impacting the microstructural features and density of the chip-unified components are discussed. Moreover, it recommends a novel approach for recycling chips, while improving the process of development, bonding quality of the chips, microstructure, overall mechanical properties, and fostering sustainable and environmentally friendly engineering.