Hasil untuk "Manufactures"

D. Gu, Xinyu Shi, R. Poprawe et al.

Cross-scale coordination Laser-based additive manufacturing has the potential to revolutionize how components are designed. Gu et al. suggest moving away from a strategy that designs and builds components in a serial manner for a more wholistic method of optimization for metal parts. The authors summarize several key developments in laser powder bed fusion and directed energy deposition and outline a number of issues that still need to be overcome. A more integrated approach will help to reduce the number of steps required for fabrication and expand the types of structures available for end-use components. Science, abg1487, this issue p. eabg1487 A Review explains that material-structure-performance integration enables high-performance, multifunctional laser-metal additive manufacturing. BACKGROUND Metallic components are the cornerstone of modern industries such as aviation, aerospace, automobile manufacturing, and energy production. The stringent requirements for high-performance metallic components impede the optimization of materials selection and manufacturing. Laser-based additive manufacturing (AM) is a key strategic technology for technological innovation and industrial sustainability. As the number of applications increases, so do the scientific and technological challenges. Because laser AM has domain-by-domain (e.g., point-by-point, line-by-line, and layer-by-layer) localized forming characteristics, the requisite for printing process and performance control encompasses more than six orders of magnitude, from the microstructure (nanometer- to micrometer-scale) to macroscale structure and performance of components (millimeter- to meter-scale). The traditional route of laser-metal AM follows a typical “series mode” from design to build, resulting in a cumbersome trial-and-error methodology that creates challenges for obtaining high-performance goals. ADVANCES We propose a holistic concept of material-structure-performance integrated additive manufacturing (MSPI-AM) to cope with the extensive challenges of AM. We define MSPI-AM as a one-step AM production of an integral metallic component by integrating multimaterial layout and innovative structures, with an aim to proactively achieve the designed high performance and multifunctionality. Driven by the performance or function to be realized, the MSPI-AM methodology enables the design of multiple materials, new structures, and corresponding printing processes in parallel and emphasizes their mutual compatibility, providing a systematic solution to the existing challenges for laser-metal AM. MSPI-AM is defined by two methodological ideas: “the right materials printed in the right positions” and “unique structures printed for unique functions.” The increasingly creative methods for engineering both micro- and macrostructures within single printed components have led to the use of AM to produce more complicated structures with multimaterials. It is now feasible to design and print multimaterial components with spatially varying microstructures and properties (e.g., nanocomposites, in situ composites, and gradient materials), further enabling the integration of functional structures with electronics within the volume of a laser-printed monolithic part. These complicated structures (e.g., integral topology optimization structures, biomimetic structures learned from nature, and multiscale hierarchical lattice or cellular structures) have led to breakthroughs in both mechanical performance and physical/chemical functionality. Proactive realization of high performance and multifunctionality requires cross-scale coordination mechanisms (i.e., from the nano/microscale to the macroscale). OUTLOOK Our MSPI-AM continues to develop into a practical methodology that contributes to the high performance and multifunctionality goals of AM. Many opportunities exist to enhance MSPI-AM. MSPI-AM relies on a more digitized material and structure development and printing, which could be accomplished by considering different paradigms for AM materials discovery with the Materials Genome Initiative, standardization of formats for digitizing materials and structures to accelerate data aggregation, and a systematic printability database to enhance autonomous decision-making of printers. MSPI-oriented AM becomes more intelligent in processes and production, with the integration of intelligent detection, sensing and monitoring, big-data statistics and analytics, machine learning, and digital twins. MSPI-AM further calls for more hybrid approaches to yield the final high-performance/multifunctional achievements, with more versatile materials selection and more comprehensive integration of virtual manufacturing and real production to navigate more complex printing. We hope that MSPI-AM can become a key strategy for the sustainable development of AM technologies. Material-structure-performance integrated additive manufacturing (MSPI-AM). Versatile designed materials and innovative structures are simultaneously printed within an integral metallic component to yield high performance and multifunctionality, integrating in parallel the core elements of material, structure, process, and performance and a large number of related coupling elements and future potential elements to enhance the multifunctionality of printed components and the maturity and sustainability of laser AM technologies. Laser-metal additive manufacturing capabilities have advanced from single-material printing to multimaterial/multifunctional design and manufacturing. Material-structure-performance integrated additive manufacturing (MSPI-AM) represents a path toward the integral manufacturing of end-use components with innovative structures and multimaterial layouts to meet the increasing demand from industries such as aviation, aerospace, automobile manufacturing, and energy production. We highlight two methodological ideas for MSPI-AM—“the right materials printed in the right positions” and “unique structures printed for unique functions”—to realize major improvements in performance and function. We establish how cross-scale mechanisms to coordinate nano/microscale material development, mesoscale process monitoring, and macroscale structure and performance control can be used proactively to achieve high performance with multifunctionality. MSPI-AM exemplifies the revolution of design and manufacturing strategies for AM and its technological enhancement and sustainable development.

Fabian Duffner, Niklas Kronemeyer, J. Tübke et al.

Sachin S. Kamble, A. Gunasekaran, Shradha Gawankar

Abstract Industry 4.0 and its other synonyms like Smart Manufacturing, Smart Production or Internet of Things, have been identified as major contributors in the context of digital and automated manufacturing environment. The term industry 4.0 comprises a variety of technologies to enable the development of the value chain resulting in reduced manufacturing lead times, and improved product quality and organizational performance. Industry 4.0 has attracted much attention in the recent literature, however there are very few systematic and extensive review of research that captures the dynamic nature of this topic. The rapidly growing interest from both academics and practitioners in Industry 4.0 has urged the need for review of up-to-date research and development to develop a new agenda. Selected 85 papers were classified in five research categories namely conceptual papers on Industry 4.0, human-machine interactions, machine-equipment interactions, technologies of Industry 4.0 and sustainability. The review primarily attempted to seek answers to the following two questions: (1) What are different research approaches used to study Industry 4.0? and (2) What is the current status of research in the domains of Industry 4.0?. We propose a sustainable Industry 4.0 framework based on the findings of the review with three critical components viz., Industry 4.0 technologies, process integration and sustainable outcomes. Finally, the scope of future research is discussed in detail.

J. Müller, Oana Buliga, K. Voigt

The article analyzes how Industry 4.0 triggers changes in the business models of manufacturing SMEs (small and medium-sized enterprises), by conducting a qualitative research with a sample of 68 German SMEs from three industries (automotive suppliers, mechanical and plant engineering, as well as electrical engineering and ICT). As SMEs play an essential role in industrial value creation, the article examines significant, yet at present understudied implications of Industry 4.0 along industrial value chains. First, the results show that Industry 4.0 encompasses three dimensions, namely high-grade digitization of processes, smart manufacturing, and inter-company connectivity. Second, the article shows how Industry 4.0 affects the three business model elements of manufacturing SMEs – value creation, value capture, and value offer – by giving specific examples for business model innovation in each of the three elements. Third, it shows that both the role as a user and/or provider of Industry 4.0 and whether a company is internally motivated and/or externally pressured towards implementation have an impact on which business model elements are innovated. Fourth, the study delineates four SME categories, designed to help managers to evaluate their own company's positioning towards Industry 4.0: craft manufacturers, preliminary stage planners, Industry 4.0 users, and full-scale adopters.

Y. .. Wang, T. Voisin, J. McKeown et al.

Jinjiang Wang, Yulin Ma, Laibin Zhang et al.

Abstract Smart manufacturing refers to using advanced data analytics to complement physical science for improving system performance and decision making. With the widespread deployment of sensors and Internet of Things, there is an increasing need of handling big manufacturing data characterized by high volume, high velocity, and high variety. Deep learning provides advanced analytics tools for processing and analysing big manufacturing data. This paper presents a comprehensive survey of commonly used deep learning algorithms and discusses their applications toward making manufacturing “smart”. The evolvement of deep learning technologies and their advantages over traditional machine learning are firstly discussed. Subsequently, computational methods based on deep learning are presented specially aim to improve system performance in manufacturing. Several representative deep learning models are comparably discussed. Finally, emerging topics of research on deep learning are highlighted, and future trends and challenges associated with deep learning for smart manufacturing are summarized.

Bintao Wu, Z. Pan, D. Ding et al.

Abstract Due to the feasibility of economically producing large-scale metal components with relatively high deposition rates, significant progress has been made in the understanding of the Wire Arc Additive Manufacturing (WAAM) process, as well as the microstructure and mechanical properties of the fabricated components. As WAAM has evolved, a wide range of materials have become associated with the process and its applications. This article reviews the emerging research on WAAM techniques and the commonly used metallic feedstock materials, and also provides a comprehensive over view of the metallurgical and material properties of the deposited parts. Common defects produced in WAAM components using different alloys are described, including deformation, porosity, and cracking. Methods for improving the fabrication quality of the additively manufactured components are discussed, taking into account the requirements of the various alloys. This paper concludes that the wide application of WAAM still presents many challenges, and these may need to be addressed in specific ways for different materials in order to achieve an operational system in an acceptable time frame. The integration of materials and manufacturing process to produce defect-free and structurally-sound deposited parts remains a crucial effort into the future.

Elisa Negria, Luca Fumagallia, M. Macchia

John H. Martin, Brennan D. Yahata, Jacob M. Hundley et al.

M. Attaran

S. Williams, F. Martina, A. Addison et al.

H. Bikas, P. Stavropoulos, G. Chryssolouris

Additive manufacturing is a technology rapidly expanding on a number of industrial sectors. It provides design freedom and environmental/ecological advantages. It transforms essentially design files to fully functional products. However, it is still hampered by low productivity, poor quality and uncertainty of final part mechanical properties. The root cause of undesired effects lies in the control aspects of the process. Optimization is difficult due to limited modelling approaches. Physical phenomena associated with additive manufacturing processes are complex, including melting/solidification and vaporization, heat and mass transfer etc. The goal of the current study is to map available additive manufacturing methods based on their process mechanisms, review modelling approaches based on modelling methods and identify research gaps. Later sections of the study review implications for closed-loop control of the process.

I. Gibson, D. Rosen, B. Stucker et al.

Sameer Mittal, M. A. Khan, D. Romero et al.

Abstract The objective of this paper is to critically review currently available Smart Manufacturing (SM) and Industry 4.0 maturity models, and analyze their fit recognizing the specific requirements of Small and Medium-sized Enterprises (SMEs). To this end, this paper presents features that are characteristic for SMEs and identify research gaps needed to be addressed to successfully support manufacturing SMEs in their progress towards Industry 4.0. The results of this study show that only a limited number of the SM and Industry 4.0 roadmaps, maturity models, frameworks and readiness assessments that are available today reflect the specific requirements and challenges of SMEs. The main findings include: (1) the current standard starting “level 1″ (base level) of most maturity models appears to be disconnected from the real digitization and smart manufacturing maturity level of many SMEs. Therefore, we propose a “level 0″ specifically designed to reflect the ‘real - base level’ for SMEs; (2) the transition from this new base level, “level 0″, to the current standard “level 1”, requires significant effort including a mind-set change; (3) maturity models and readiness assessments can be associated with an SM toolkit, and (4) SMEs need to develop their own, unique SM or Industry 4.0 vision and roadmap. This study provides insights that help towards developing a realistic SM (Industry 4.0) maturity model for SMEs that reflects their industrial realities more accurately. With the help of SM maturity models that are more customized to the SME specific requirements, the SMEs’ stakeholders will be able to better define their SM (Industry 4.0) vision, roadmap, and strategic projects. It will ultimately lower the entry barrier and reduce the risk of the transition process towards SM and Industry 4.0 and support the critical change in culture. Summarizing, we identified manufacturing SMEs’ specific requirements, conducted a literature review of current SM maturity models, and discussed how these maturity models reflect the SME specific requirements.

L. Li

In this study, we have compared Germany's “Industry 4.0” and China's “Made-in-China 2025” and estimated China's locus in “Made-in-China 2025”. “Made-in-China 2025” has clear goals, measures and sector focus. Its guiding principles are to enhance industrial capability through innovation-driven manufacturing, optimize the structure of Chinese industry, emphasize quality over quantity, train and attract talent, and achieve green manufacturing and environment. Data show that currently China is no longer the lowest–cost labor market; it is being squeezed by newly emerging low-cost producers such as Vietnam, Cambodia, and Laos. Meanwhile, China is not the strongest player in the high-tech arena; well-established industrialized nations, the US, Germany, and Japan, have all effectively deployed digital technology to create new industrial environments, produce new products, and improve their well-established brands. Having analyzed the data from the World Bank and China's National Bureau of Statistics, we find an upward trajectory in China in manufacturing capability development, research and development commitment, and human capital investment. However, implementing an ambitious strategic plan such as “Made-in-China 2025” is coupled with challenges. This research helps us understand the relationship between technological entrepreneurship and socio-economic changes in emerging economies such as China. Furthermore, the experience accumulated in China can be referenced by both emerging economies and developed nations to advance their technological entrepreneurship.

Michael A. McDonald

Jian Qin, Ying Liu, R. Grosvenor

Abstract With rapid advancements in industry, technology and applications, many concepts have emerged in manufacturing. It is generally known that the far-sighted term ‘Industry 4.0’ was published to highlight a new industrial revolution. Many manufacturing organizations and companies are researching this topic. However, the achievement criteria of Industry 4.0 are as yet uncertain. In addition, the technology roadmap of accomplishing Industry 4.0 is still not clear in industry nor in academia to date. This paper focuses on the fundamental conception of Industry 4.0 and the state of current manufacturing systems. It also identifies the research gaps between current manufacturing systems and Industry 4.0 requirements. The major contribution is an implementation structure of Industry 4.0, consisting of a multi-layered framework is described, and is shown how it can assist people in understanding and achieving the requirements of Industry 4.0.

Surajit Bag, J. Pretorius, Shivam Gupta et al.

ABSTRACT The significance of big data analytics-powered artificial intelligence has grown in recent years. The literature indicates that big data analytics-powered artificial intelligence has the ability to enhance supply chain performance, but there is limited research concerning the reasons for which firms engaging in manufacturing activities adopt big data analytics-powered artificial intelligence. To address this gap, our study employs institutional theory and resource-based view theory to elucidate the way in which automotive firms configure tangible resources and workforce skills to drive technological enablement and improve sustainable manufacturing practices and furthermore develop circular economy capabilities. We tested the research hypothesis using primary data collected from 219 automotive and allied manufacturing companies operating in South Africa. The contribution of this work lies in the statistical validation of the theoretical framework, which provides insight regarding the role of institutional pressures on resources and their effects on the adoption of big data analytics-powered artificial intelligence, and how this affects sustainable manufacturing and circular economy capabilities under the moderating effects of organizational flexibility and industry dynamism.

Carla Gonçalves Machado, M. Winroth, Elias Hans Dener Ribeiro da Silva

This systematic review intends to identify how sustainable manufacturing research is contributing to the development of the Industry 4.0 agenda and for a broader understanding about the links between the Industry 4.0 and Sustainable Manufacturing by mapping and summarising existing research efforts, identifying research agendas, as well as gaps and opportunities for research development. A conceptual framework formed by the principles and technological pillars of Industry 4.0, sustainable manufacturing scope, opportunities previously identified, and sustainability dimensions, guided analysis of 35 papers from 2008–2018, selected by a systematic approach. Bibliometrics data and social network analysis complement results identifying how research is being organised and its respective research agendas, relevant publications, and status of the research lifecycle. Results point to that the current research is aligned with the goals defined by different national industrial programs. There are, however, research gaps and opportunities for field development, becoming more mature and having a significant contribution to fully developing the agenda of Industry 4.0.

T. Zheng, M. Ardolino, A. Bacchetti et al.

Industry 4.0 (I4.0) encompasses a plethora of digital technologies effecting on manufacturing enterprises. Most research on this topic examines the effects in the smart factory domain, focusing on production scheduling. However, there is still a lack of comprehensive research on the applications of I4.0 enabling technologies in manufacturing life-cycle processes. This paper is thus intended to provide a systematic literature review answering the following research question: What are the applications of I4.0 enabling technologies in the business processes of manufacturing companies? The study analyses 186 articles and the results show that production scheduling and control is the process most often investigated, while there is also an increasing trend in servitization and circular supply chain management. Moreover, there is extensive combined use of IoT, Big Data Analytics and Cloud, whose applications cover a wide range of processes. On the contrary, other technology like Blockchain is not as widely discussed in the domain of I4.0. This picture calls for a future research agenda extending the scope of investigation into I4.0 in manufacturing. Furthermore, the results of this research can prove extremely useful for practitioners who wish to implement one or more technologies, providing them with solutions for applications in manufacturing.