Hasil untuk "Manufactures"

Praveen Kumar Reddy Maddikunta, Viet Quoc Pham, B. Prabadevi et al.

Abstract Industry 5.0 is regarded as the next industrial evolution, its objective is to leverage the creativity of human experts in collaboration with efficient, intelligent and accurate machines, in order to obtain resource-efficient and user-preferred manufacturing solutions compared to Industry 4.0. Numerous promising technologies and applications are expected to assist Industry 5.0 in order to increase production and deliver customized products in a spontaneous manner. To provide a very first discussion of Industry 5.0, in this paper, we aim to provide a survey-based tutorial on potential applications and supporting technologies of Industry 5.0. We first introduce several new concepts and definitions of Industry 5.0 from the perspective of different industry practitioners and researchers. We then elaborately discuss the potential applications of Industry 5.0, such as intelligent healthcare, cloud manufacturing, supply chain management and manufacturing production. Subsequently, we discuss about some supporting technologies for Industry 5.0, such as edge computing, digital twins, collaborative robots, Internet of every things, blockchain, and 6G and beyond networks. Finally, we highlight several research challenges and open issues that should be further developed to realize Industry 5.0.

Aidan Fuller, Zhong Fan, Charles Day

Digital Twin technology is an emerging concept that has become the centre of attention for industry and, in more recent years, academia. The advancements in industry 4.0 concepts have facilitated its growth, particularly in the manufacturing industry. The Digital Twin is defined extensively but is best described as the effortless integration of data between a physical and virtual machine in either direction. The challenges, applications, and enabling technologies for Artificial Intelligence, Internet of Things (IoT) and Digital Twins are presented. A review of publications relating to Digital Twins is performed, producing a categorical review of recent papers. The review has categorised them by research areas: manufacturing, healthcare and smart cities, discussing a range of papers that reflect these areas and the current state of research. The paper provides an assessment of the enabling technologies, challenges and open research for Digital Twins.

Yuqian Lu, Chao Liu, K. Wang et al.

Abstract This paper reviews the recent development of Digital Twin technologies in manufacturing systems and processes, to analyze the connotation, application scenarios, and research issues of Digital Twin-driven smart manufacturing in the context of Industry 4.0. To understand Digital Twin and its future potential in manufacturing, we summarized the definition and state-of-the-art development outcomes of Digital Twin. Existing technologies for developing a Digital Twin for smart manufacturing are reviewed under a Digital Twin reference model to systematize the development methodology for Digital Twin. Representative applications are reviewed with a focus on the alignment with the proposed reference model. Outstanding research issues of developing Digital Twins for smart manufacturing are identified at the end of the paper.

A. Frank, Lucas Santos Dalenogare, N. F. Ayala

Abstract Industry 4.0 has been considered a new industrial stage in which several emerging technologies are converging to provide digital solutions. However, there is a lack of understanding of how companies implement these technologies. Thus, we aim to understand the adoption patterns of Industry 4.0 technologies in manufacturing firms. We propose a conceptual framework for these technologies, which we divided into front-end and base technologies. Front-end technologies consider four dimensions: Smart Manufacturing, Smart Products, Smart Supply Chain and Smart Working, while base technologies consider four elements: internet of things, cloud services, big data and analytics. We performed a survey in 92 manufacturing companies to study the implementation of these technologies. Our findings show that Industry 4.0 is related to a systemic adoption of the front-end technologies, in which Smart Manufacturing plays a central role. Our results also show that the implementation of the base technologies is challenging companies, since big data and analytics are still low implemented in the sample studied. We propose a structure of Industry 4.0 technology layers and we show levels of adoption of these technologies and their implication for manufacturing companies.

S. Nahavandi

Staying at the top is getting tougher and more challenging due to the fast-growing and changing digital technologies and AI-based solutions. The world of technology, mass customization, and advanced manufacturing is experiencing a rapid transformation. Robots are becoming even more important as they can now be coupled with the human mind by means of brain–machine interface and advances in artificial intelligence. A strong necessity to increase productivity while not removing human workers from the manufacturing industry is imposing punishing challenges on the global economy. To counter these challenges, this article introduces the concept of Industry 5.0, where robots are intertwined with the human brain and work as collaborator instead of competitor. This article also outlines a number of key features and concerns that every manufacturer may have about Industry 5.0. In addition, it presents several developments achieved by researchers for use in Industry 5.0 applications and environments. Finally, the impact of Industry 5.0 on the manufacturing industry and overall economy is discussed from an economic and productivity point of view, where it is argued that Industry 5.0 will create more jobs than it will take away.

F. Tao, Qinglin Qi, Lihui Wang et al.

Abstract State-of-the-art technologies such as the Internet of Things (IoT), cloud computing (CC), big data analytics (BDA), and artificial intelligence (AI) have greatly stimulated the development of smart manufacturing. An important prerequisite for smart manufacturing is cyber–physical integration, which is increasingly being embraced by manufacturers. As the preferred means of such integration, cyber–physical systems (CPS) and digital twins (DTs) have gained extensive attention from researchers and practitioners in industry. With feedback loops in which physical processes affect cyber parts and vice versa, CPS and DTs can endow manufacturing systems with greater efficiency, resilience, and intelligence. CPS and DTs share the same essential concepts of an intensive cyber–physical connection, real-time interaction, organization integration, and in-depth collaboration. However, CPS and DTs are not identical from many perspectives, including their origin, development, engineering practices, cyber–physical mapping, and core elements. In order to highlight the differences and correlation between them, this paper reviews and analyzes CPS and DTs from multiple perspectives.

Shunyu Liu, Y. Shin

Abstract In this paper, the recent progress on Ti6Al4V fabricated by three mostly developed additive manufacturing (AM) techniques-directed energy deposition (DED), selective laser melting (SLM) and electron beam melting (EBM)-is thoroughly investigated and compared. Fundamental knowledge is provided for the creation of links between processing parameters, resultant microstructures and associated mechanical properties. Room temperature tensile and fatigue properties are also reviewed and compared to traditionally manufactured Ti6Al4V parts. The presence of defects in as-built AM Ti6Al4V components and the influences of these defects on mechanical performances are also critically discussed.

M. Thompson, G. Moroni, Tom Vaneker et al.

Andreas Schumacher, Selim Erol, W. Sihn

Abstract Manufacturing enterprises are currently facing substantial challenges with regard to disruptive concepts such as the Internet of Things, Cyber Physical Systems or Cloud-based Manufacturing – also referred to as Industry 4.0. Subsequently, increasing complexity on all firm levels creates uncertainty about respective organizational and technological capabilities and adequate strategies to develop them. In this paper we propose an empirically grounded novel model and its implementation to assess the Industry 4.0 maturity of industrial enterprises in the domain of discrete manufacturing. Our main goal was to extend the dominating technology focus of recently developed models by including organizational aspects. Overall we defined 9 dimensions and assigned 62 items to them for assessing Industry 4.0 maturity. The dimensions “Products”, “Customers”, “Operations” and “Technology” have been created to assess the basic enablers. Additionally, the dimensions “Strategy”, “Leadership”, Governance, “Culture” and “People” allow for including organizational aspects into the assessment. Afterwards, the model has been transformed into a practical tool and tested in several companies whereby one case is presented in the paper. First validations of the model's structure and content show that the model is transparent and easy to use and proved its applicability in real production environments.

T. Stock, G. Seliger

Abstract The current globalization is faced by the challenge to meet the continuously growing worldwide demand for capital and consumer goods by simultaneously ensuring a sustainable evolvement of human existence in its social, environmental and economic dimensions. In order to cope with this challenge, industrial value creation must be geared towards sustainability. Currently, the industrial value creation in the early industrialized countries is shaped by the development towards the fourth stage of industrialization, the so-called Industry 4.0. This development provides immense opportunities for the realization of sustainable manufacturing. This paper will present a state of the art review of Industry 4.0 based on recent developments in research and practice. Subsequently, an overview of different opportunities for sustainable manufacturing in Industry 4.0 will be presented. A use case for the retrofitting of manufacturing equipment as a specific opportunity for sustainable manufacturing in Industry 4.0 will be exemplarily outlined.

D. Herzog, V. Seyda, E. Wycisk et al.

Abstract Additive Manufacturing (AM), the layer-by layer build-up of parts, has lately become an option for serial production. Today, several metallic materials including the important engineering materials steel, aluminium and titanium may be processed to full dense parts with outstanding properties. In this context, the present overview article describes the complex relationship between AM processes, microstructure and resulting properties for metals. It explains the fundamentals of Laser Beam Melting, Electron Beam Melting and Laser Metal Deposition, and introduces the commercially available materials for the different processes. Thereafter, typical microstructures for additively manufactured steel, aluminium and titanium are presented. Special attention is paid to AM specific grain structures, resulting from the complex thermal cycle and high cooling rates. The properties evolving as a consequence of the microstructure are elaborated under static and dynamic loading. According to these properties, typical applications are presented for the materials and methods for conclusion.

W. Sames, F. List, S. Pannala et al.

Michael Lieder, A. Rashid

L. Monostori, B. Kádár, T. Bauernhansl et al.

Jie Ren, Yin Zhang, Dexin Zhao et al.

Longfei Zhou, Jenna Miller, Jeremiah Vezza et al.

Additive manufacturing has revolutionized manufacturing across a spectrum of industries by enabling the production of complex geometries with unparalleled customization and reduced waste. Beginning as a rapid prototyping tool, additive manufacturing has matured into a comprehensive manufacturing solution, embracing a wide range of materials, such as polymers, metals, ceramics, and composites. This paper delves into the workflow of additive manufacturing, encompassing design, modeling, slicing, printing, and post-processing. Various additive manufacturing technologies are explored, including material extrusion, VAT polymerization, material jetting, binder jetting, selective laser sintering, selective laser melting, direct metal laser sintering, electron beam melting, multi-jet fusion, direct energy deposition, carbon fiber reinforced, laminated object manufacturing, and more, discussing their principles, advantages, disadvantages, material compatibilities, applications, and developing trends. Additionally, the future of additive manufacturing is projected, highlighting potential advancements in 3D bioprinting, 3D food printing, large-scale 3D printing, 4D printing, and AI-based additive manufacturing. This comprehensive survey aims to underscore the transformative impact of additive manufacturing on global manufacturing, emphasizing ongoing challenges and the promising horizon of innovations that could further elevate its role in the manufacturing revolution.

Dan Li, Yaoqin Gan, Feng Li et al.

The dual roles of Mg in the room-temperature strengthening and high-temperature softening of LPBF-Processed AlMnScZr alloys are systematically elucidated. The addition of Mg promotes compositional supercooling during solidification, transforming the as-built columnar grains of the Mg-free alloy into a refined bimodal structure comprising alternating coarse and fine equiaxed regions. Mg also accelerates the formation of primary Al6Mn precipitates, increasing their size from ∼43.7 nm in the Mg-free alloy to 54.0 and 114.6 nm in alloys containing 1.5 and 3.5 wt.% Mg, respectively. During aging, Mg addition significantly enhances the dispersion and number density of secondary Al6Mn particles, while exerting negligible influence on the formation of nanoscale Al3(Sc, Zr) precipitates (∼3 nm). The synergistic effects of solid-solution strengthening, precipitation hardening, and grain refinement yield superior room-temperature properties, achieving ultimate tensile strengths of 538 MPa (1.5 wt.% Mg) and 626 MPa (3.5 wt.% Mg). However, Mg addition also accelerates thermal softening in the AlMnScZr alloy. For instance, the yield strength at 300 °C drops to 108 MPa in the 3.5 wt.% Mg alloy, compared to 198 MPa in the base alloy. This strength loss is identified as being due to Mg-promoted coarsening of Al6Mn particles during thermal exposure.

Kelly L. O’Neill, Taite McLoughlin, Georgia Van der Linden et al.

Melt electrowriting (MEW) produces high-resolution, highly porous microfibre scaffolds that are consistently replicable. While typically used to produce planar scaffolds, tubular microfibre structures are increasingly needed for tissue engineering (TE) applications (e.g. vascular TE). Designing such microfibre tubes is challenging due to the continuous fibre deposition required by MEW, and the difficulty of coding a three-dimensional geometry without the ability to previsualise it. This study introduces a new design approach that simplifies programming, provides a digital scaffold preview, and rapidly generates G-code iterations in AeroBasic script (compatible with Aerotech axis system) by using Rhinoceros, a well-known CAD 3D modelling software, and its built-in algorithmic design plugin, Grasshopper. The resulting 1 and 3 mm inner diameter MEW tubes consisted of fibre diameters 10.8 ± 0.7 µm or 20.7 ± 0.9 µm and matched the programmed design. This visual prototyping platform through Rhinoceros and Grasshopper offers a new method in predicting fibre paths and incorporating scaffold design parameters, meeting the need for diverse tubular scaffolds in different fields. In this study, we investigate whether a digital preview of tubular scaffolds and corresponding G-code generation system can enhance the accuracy and efficiency of designing tubular microfibre scaffolds for biofabrication applications.

Jingwen Gao, Chenyang Zhu, Shubo Gao et al.

Laser powder bed fusion (LPBF) is the dominant metal additive manufacturing technique due to its advantages in near-net-shape production of complex parts with high resolution. However, conventional quality control of LPBF-fabricated parts, including microstructure characterisation, often relies on trial-and-error experiments. These methods can be time-consuming, resource-intensive, and potentially destructive to specimens. This study introduces an image-to-image translation Cycle-consistent Generative Adversarial Network (CycleGAN)-based framework for generating statistically equivalent microstructures of LPBF-fabricated samples directly from corresponding as-printed surface inputs. The results demonstrate that the framework can effectively generate crystallographic and morphological features across 22 different process parameters for LPBF-fabricated pure nickel. The distribution of microstructural descriptors, such as grain size, grain shape, and even grain boundary misorientation angles, shows no significant difference from that measured by experiments. The generated microstructural mapping using image inputs with CycleGAN outperforms those from other generation methods on both qualitative and quantitative evaluations. The developed framework is material-agnostic and can be fine-tuned for other LPBF materials via transfer learning, providing potential applications in in-situ process optimisation and microstructure design in LPBF manufacturing.