Hasil untuk "Manufactures"

N. Mirabella, V. Castellani, Serenella Sala

D. Gu, W. Meiners, K. Wissenbach et al.

D. Huntzinger, T. Eatmon

Sau Lee, T. O'Connor, Xiaochuan Yang et al.

M. Holweg

T. Dunne, Mark, Roberts et al.

S. R. S. Kalpakjian

J. Naylor, M. Naim, D. Berry

Timothy Besley, R. Burgess

J. Tybout

G. Boothroyd

Smita B. Brunnermeier, Mark A. Cohen

Bin He, Kai-Jian Bai

As the next-generation manufacturing system, intelligent manufacturing enables better quality, higher productivity, lower cost, and increased manufacturing flexibility. The concept of sustainability is receiving increasing attention, and sustainable manufacturing is evolving. The digital twin is an emerging technology used in intelligent manufacturing that can grasp the state of intelligent manufacturing systems in real-time and predict system failures. Sustainable intelligent manufacturing based on a digital twin has advantages in practical applications. To fully understand the intelligent manufacturing that provides the digital twin, this study reviews both technologies and discusses the sustainability of intelligent manufacturing. Firstly, the relevant content of intelligent manufacturing, including intelligent manufacturing equipment, systems, and services, is analyzed. In addition, the sustainability of intelligent manufacturing is discussed. Subsequently, a digital twin and its application are introduced along with the development of intelligent manufacturing based on the digital twin technology. Finally, combined with the current status, the future development direction of intelligent manufacturing is presented.

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

Additive manufacturing (AM) is increasingly used in different fields. At first, it was specific to prototyping and proof of concepts. Nowadays, it is used in many areas. AM allows the fabrication of non-removable assemblies in one go, with two or more different materials. The complexity of the parts is not limited with the tool access or other blocking issues of traditional processes. The only limitation is the imagination of the designer. This brings up a change of paradigm when thinking the design of new parts, or the reengineering of existing assemblies. To benefit from these advantages, a new design approach must be developed; it should take into account the specificities of the process, and help the designer find optimum solutions. The design methodologies have been developed for a long time, they are mostly thought for a specific life cycle or a specific manufacturing process. Because of the differences of AM technologies, the design thinking of these processes is important in the laboratories using AM. The aim of this paper is to present the traditional methodologies, outline the need for a specific one, and present a new methodology concerning the DFAM (design for additive manufacturing), including the factors influencing the design, and the added value compared to the cited methodologies.

M. Revilla‐León, M. Özcan

Ji Zhou, Peigen Li, Yanhong Zhou et al.

Abstract Intelligent manufacturing is a general concept that is under continuous development. It can be categorized into three basic paradigms: digital manufacturing, digital-networked manufacturing, and new-generation intelligent manufacturing. New-generation intelligent manufacturing represents an in-depth integration of new-generation artificial intelligence (AI) technology and advanced manufacturing technology. It runs through every link in the full life-cycle of design, production, product, and service. The concept also relates to the optimization and integration of corresponding systems; the continuous improvement of enterprises’ product quality, performance, and service levels; and reduction in resources consumption. New-generation intelligent manufacturing acts as the core driving force of the new industrial revolution and will continue to be the main pathway for the transformation and upgrading of the manufacturing industry in the decades to come. Human-cyber-physical systems (HCPSs) reveal the technological mechanisms of new-generation intelligent manufacturing and can effectively guide related theoretical research and engineering practice. Given the sequential development, cross interaction, and iterative upgrading characteristics of the three basic paradigms of intelligent manufacturing, a technology roadmap for “parallel promotion and integrated development” should be developed in order to drive forward the intelligent transformation of the manufacturing industry in China.

S. S. Rosa, D. Prazeres, A. Azevedo et al.

Vaccines are one of the most important tools in public health and play an important role in infectious diseases control. Owing to its precision, safe profile and flexible manufacturing, mRNA vaccines are reaching the stoplight as a new alternative to conventional vaccines. In fact, mRNA vaccines were the technology of choice for many companies to combat the Covid-19 pandemic, and it was the first technology to be approved in both United States and in Europe Union as a prophylactic treatment. Additionally, mRNA vaccines are being studied in the clinic to treat a number of diseases including cancer, HIV, influenza and even genetic disorders. The increased demand for mRNA vaccines requires a technology platform and cost-effective manufacturing process with a well-defined product characterisation. Large scale production of mRNA vaccines consists in a 1 or 2-step in vitro reaction followed by a purification platform with multiple steps that can include Dnase digestion, precipitation, chromatography or tangential flow filtration. In this review we describe the current state-of-art of mRNA vaccines, focusing on the challenges and bottlenecks of manufacturing that need to be addressed to turn this new vaccination technology into an effective, fast and cost-effective response to emerging health crises.

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

The purpose of this article is to collect and structure the various characteristics, technologies and enabling factors available in the current body of knowledge that are associated with smart manufacturing. Eventually, it is expected that this selection of characteristics, technologies and enabling factors will help compare and distinguish other initiatives such as Industry 4.0, cyber-physical production systems, smart factory, intelligent manufacturing and advanced manufacturing, which are frequently used synonymously with smart manufacturing. The result of this article is a comprehensive list of such characteristics, technologies and enabling factors that are regularly associated with smart manufacturing. This article also considers principles of “semantic similarity” to establish the basis for a future smart manufacturing ontology, since it was found that many of the listed items show varying overlaps; therefore, certain characteristics and technologies are merged and/or clustered. This results in a set of five defining characteristics, 11 technologies and three enabling factors that are considered relevant for the smart manufacturing scope. This article then evaluates the derived structure by matching the characteristics and technology clusters of smart manufacturing with the design principles of Industry 4.0 and cyber-physical systems. The authors aim to provide a solid basis to start a broad and interdisciplinary discussion within the research and industrial community about the defining characteristics, technologies and enabling factors of smart manufacturing.

Peng Zhang, O. Deschenes, Kyle C. Meng et al.

N. Sarah Arden, Adam C. Fisher, Katherine M. Tyner et al.

Over the last two centuries, medicines have evolved from crude herbal and botanical preparations into more complex manufacturing of sophisticated drug products and dosage forms. Along with the evolution of medicines, the manufacturing practices for their production have advanced from small-scale manual processing with simple tools to large-scale production as part of a trillion-dollar pharmaceutical industry. Today's pharmaceutical manufacturing technologies continue to evolve as the internet of things, artificial intelligence, robotics, and advanced computing begin to challenge the traditional approaches, practices, and business models for the manufacture of pharmaceuticals. The application of these technologies has the potential to dramatically increase the agility, efficiency, flexibility, and quality of the industrial production of medicines. How these technologies are deployed on the journey from data collection to the hallmark digital maturity of Industry 4.0 will define the next generation of pharmaceutical manufacturing. Acheiving the benefits of this future requires a vision for it and an understanding of the extant regulatory, technical, and logistical barriers to realizing it.