Hasil untuk "Manufactures"

Hui Yang, Faisal Aqlan, Richard Zhao

The rapid evolution of modern manufacturing systems is driven by the integration of emerging metaverse technologies such as artificial intelligence (AI), digital twin (DT) with different forms of extended reality (XR) like virtual reality (VR), augmented reality (AR), and mixed reality (MR). These advances confront manufacturing workers with complex and evolving environments that demand digital literacy for problem solving in the future workplace. However, manufacturing industry faces a critical shortage of skilled workforce with digital literacy in the world. Further, global pandemic has significantly changed how people work and collaborate digitally and remotely. There is an urgent need to rethink digital platformization and leverage emerging technologies to propel industrial evolution toward human-centered manufacturing metaverse (MfgVerse). This paper presents a forward-looking perspective on the development of smart MfgVerse, highlighting current efforts in learning factory, cognitive digital twinning, and the new sharing economy of manufacturing-as-a-service (MaaS). MfgVerse is converging into multiplex networks, including a social network of human stakeholders, an interconnected network of manufacturing things or agents (e.g., machines, robots, facilities, material handling systems), a network of digital twins of physical things, as well as auxiliary networks of sales, supply chain, logistics, and remanufacturing systems. We also showcase the design and development of a learning factory for workforce training in extended reality. Finally, future directions, challenges, and opportunities are discussed for human-centered manufacturing metaverse. We hope this work helps stimulate more comprehensive studies and in-depth research efforts to advance MfgVerse technologies.

Juanjo Zulaika, Ibone Oleaga, Anne Sanz et al.

The manufacturing sector is moving from rigid, hardware-dependent systems toward flexible, software-driven environments. This transformation is shaped by the convergence of several Software-Defined technologies: Software-Defined Automation virtualizes industrial control, replacing proprietary PLCs with containerized, programmable solutions that enable scalability and interoperability. Software-Defined Compute and Communications provide a means to distribute intelligence seamlessly across devices, networks, and cloud platforms, reducing latency and enabling dynamic reconfiguration. Software-Defined Manufacturing Systems, usually implemented as Digital Twins, are real-time virtual models of machines and processes, allowing predictive analysis, optimization, and closer integration between human operators and intelligent systems. This work presents XWAVE, a project that unites these three Software-Defined paradigms to present a modular, fully software-defined manufacturing system.

Christopher Martin, Apurva Patil, Wei Li et al.

Roll-to-roll (R2R) manufacturing is a continuous processing technology essential for scalable production of thin-film materials and printed electronics, but precise control remains challenging due to subsystem interactions, nonlinearities, and process disturbances. This paper proposes a Model Predictive Path Integral (MPPI) control formulation for R2R systems, leveraging a GPU-based Monte-Carlo sampling approach to efficiently approximate optimal controls online. Crucially, MPPI easily handles non-differentiable cost functions, enabling the incorporation of complex performance criteria relevant to advanced manufacturing processes. A case study is presented that demonstrates that MPPI significantly improves tension regulation performance compared to conventional model predictive control (MPC), highlighting its suitability for real-time control in advanced manufacturing.

Pablo Antolin, Michael Barton, Georges-Pierre Bonneau et al.

With the evolution of new manufacturing technologies such as multi-material 3D printing, one can think of new type of objects that consist of considerably less, yet heterogeneous, material, consequently being porous, lighter and cheaper, while having the very same functionality as the original object when manufactured from one single solid material. We aim at questioning five decades of traditional paradigms in geometric CAD and focus at new generation of CAD objects that are not solid, but contain heterogeneous free-form internal microstructures. We propose a unified manufacturing pipeline that involves all stages, namely design, optimization, manufacturing, and inspection of microstructured free-form geometries. We demonstrate our pipeline on an industrial test case of a blisk blade that sustains the desired pressure limits, yet requires significantly less material when compared to the solid counterpart.

Rodrigo Silva Sotolani, Isabella de Araújo Cionini Menezes, Napoleão Verardi Galegale et al.

O progresso da Indústria 4.0 tem relevância cada vez maior, considerando o aumento das vulnerabilidades de segurança da informação e da complexidade em priorizá-las na tomada de decisões. Observou-se uma lacuna de pesquisa neste tema. O objetivo deste artigo é identificar critérios na literatura científica que possam ser utilizados em um método de análise multicritério, visando a priorização de tratamento de vulnerabilidades de segurança na Indústria 4.0. Um método como o Analytic Hierarchy Process (AHP) é uma proposta de solução. A metodologia utilizada foi a revisão exploratória da literatura encontrada nas bases SCOPUS e Web of Science. O resultado identificou oito critérios e 34 subcritérios relacionados ao tratamento das vulnerabilidades de segurança na Indústria 4.0. A contribuição teórica vai ao encontro do preenchimento da lacuna em relação a este tema. A contribuição prática permite que organizações da Industria 4.0 apliquem os critérios identificados na análise multicritério para o tratamento das suas vulnerabilidades de segurança e assim alcancem melhores decisões para a entrega de produtos e serviços contribuindo para sociedade. Pesquisas futuras podem ser conduzidas por meio de entrevistas ou questionários para validação com profissionais da área dos critérios encontrados, como também a aplicação prática do método AHP.

COCÎRLEA Maria Denisa, COMAN Andrei, POPOVICI Lucia-Florina et al.

Cotton is a textile material frequently used in the medical system. The possibility of using natural extracts for coloring biotextiles is a current way of giving sustainability attributes to articles used in medicine. The influence of five different classical mordants and biomordants (citric acid, tannic acid, ferrous sulfate II, copper sulfate and ferrous sulfate with oxalic acid), by meta-mordanting and dyeing by exhaustion and sonication methods with Rhus typhina L. fruit extract, was evaluated. The investigation was carried out by measuring the chromatic coordinates, water and dry rubbing resistance, as well as measuring the FT-IR absorption spectra. The mixture between ferrous sulfate and 4% oxalic acid produced, according to the ATR-FTIR analysis, the greatest changes in the cotton structure, but significantly reduces the color changes resulting from the simple use of sulfate. The promising obtained results such as low color changes when using biomordants, good resistance to water and friction, encourage to continue the research regarding the application of this extract in the friendly technology of cotton dyeing.

Alexander D. Zemskov, Yao Fu, Runchao Li et al.

In Industry 4.0, the digital twin is one of the emerging technologies, offering simulation abilities to predict, refine, and interpret conditions and operations, where it is crucial to emphasize a heightened concentration on the associated security and privacy risks. To be more specific, the adoption of digital twins in the manufacturing industry relies on integrating technologies like cyber-physical systems, the Industrial Internet of Things, virtualization, and advanced manufacturing. The interactions of these technologies give rise to numerous security and privacy vulnerabilities that remain inadequately explored. Towards that end, this paper analyzes the cybersecurity threats of digital twins for advanced manufacturing in the context of data collection, data sharing, machine learning and deep learning, and system-level security and privacy. We also provide several solutions to the threats in those four categories that can help establish more trust in digital twins.

Mahsa Nasri, Uttkarsh Narayan, Mustafa Feyyaz Sonbudak et al.

Apprenticeship and training programs in advanced manufacturing frequently encounter safety and accessibility concerns due to using heavy machinery. Virtual Reality (VR) training addresses such constraints while maintaining the spatial and procedural learning requirements of such training. However, designing effective VR training is challenging because advanced manufacturing processes are complex and require experts to train novices for a long time. This paper presents a VR Training Apprenticeship (VRTA) tailored for cold spray, which we carefully designed to teach novices step-by-step this particular advanced manufacturing process. To assess its effectiveness, we conducted an exploratory study ($n = 22$). We evaluated user experience (UX) measures in the form of quantitative scales, users' qualitative insights, and task performance with real-world machinery after the VR training. We discuss how the VRTA design contributed to the effectiveness and the challenges of considering VR training for advanced manufacturing.

Ahmed R. Sadik, Bodo Urban

Cooperative manufacturing is a new trend in industry, which depends on the existence of a collaborative robot. A collaborative robot is usually a light-weight robot which is capable of operating safely with a human co-worker in a shared work environment. During this cooperation, a vast amount of information is exchanged between the collaborative robot and the worker. This information constructs the cooperative manufacturing knowledge, which describes the production components and environment. In this research, we propose a holonic control solution, which uses the ontology concept to represent the cooperative manufacturing knowledge. The holonic control solution is implemented as an autonomous multi-agent system that exchanges the manufacturing knowledge based on an ontology model. Ultimately, the research illustrates and implements the proposed solution over a cooperative assembly scenario, which involves two workers and one collaborative robot, whom cooperate together to assemble a customized product.

Younes Chahid, Carolyn Atkins, Greg Lister et al.

Despite the established role of additive manufacturing (AM) in aerospace and medical fields, its adoption in astronomy remains low. Encouraging AM integration in a risk-averse community necessitates documentation and dissemination of previous case studies. The objective of this study is to create the first review of AM in astronomy hardware, answering: where is AM currently being used in astronomy, what is the status of its adoption, and what challenges are preventing its widespread use? The review starts with an introduction to astronomical instruments size/cost challenges, alongside the role of manufacturing innovation. This is followed by highlighting the benefits/challenges of AM and used materials/processes in both space-based and ground-based applications. The review case studies include mirrors, optomechanical structures, compliant mechanisms, brackets and tooling applications that are either in research phase or are implemented.

A. Hirschman

E. Grilli

Erdong Wang, Jiahui Zhou, Xiao Guo et al.

ABSTRACTMetallic lattice structures based on triply periodic minimum surfaces (TPMS) have attracted extensive attention for their potential application in lightweight and energy absorption. The underlying phenomena, mechanisms and modelling under the crushing responses from quasi-static to shock conditions still remain to be revealed. This work systematically investigates the mechanical behaviour of graded additively Schoen-F-RD (FRD) lattice structures under various loading rates. Under dynamic compression, FRD lattices exhibit the ability to withstand larger densification strains at higher plateau strengths, thus, holding enhanced energy absorption capabilities. At medium strain rates, it is the rate-dependence of lattice base material dominates in the strength enhancement, while, at higher strain rates, the role of inertia effect becomes notable. Furthermore, an empirical formula is introduced to predict the shock stress responses. Finally, constitutive models with strain-rates are proposed for the uniform and graded lattices. These findings can provide excellent guidance on the design of energy-absorbing structures.

Yanning Zhang, Cory Groden, E. Nyberg et al.

Bimetallic structures of Ti6Al4V-W7Ni3Fe were fabricated via directed energy deposition (DED)-based additive manufacturing (AM). Our research demonstrates the ability of DED-based AM to control Ti6Al4V-W7Ni3Fe bimetallic structures with tailorable mechanical and thermal performance. The thermal conductivity of the bimetallic structures was three times higher than Ti6Al4V at 300°C. Uniaxial compression along the transverse direction showed a failure strain of 63% compared to pure Ti6Al4V, while the longitudinal direction showed a failure strain of only 37% of Ti6Al4V. Variable hardness was observed throughout the sample due to diffusion of elements and intermetallic phase formations. Scanning electron microscopy revealed that the interfaces in the as-printed samples were crack-free with elemental gradients.

Jinyi Ma, Yong Wu, Qin Pan et al.

The Baeyer-Villiger Oxidation (BVO) of ketones and aldehydes produce lactones and formates, while aerobic carboxylation of aldehydes manufactures carboxylic acids, both having high added value. This work prepared a series of Al-containing silicates modified with organic ligands and SnO₂ nanoparticles, which were then employed as catalyst in BVO and carboxylation. Characterizations revealed the morphology of the synthesized catalyst was changed from micron-sized thin sheets to smaller blocks, and then to uniform nanoparticles (size of 50 nm) having the doped SnO₂ nanoparticles with a size of 29 nm. All catalysts showed high BET surface areas featuring silt-like mesopores. In determining the priority of BVO and carboxylation, an influence evaluation of the parameters showed the order to be substrate > oxidant > solvent > catalyst. Cyclic aliphatic ketones were suitable for BVO, but linear aliphatic and aromatic aldehydes for carboxylation. Coordination of (<i>S</i>)-binaphthol or doping of Sn into catalyst showed little influence on BVO under <i>m</i>-CPBA, but the Sn-doped catalyst largely increased BVO under (NH₄)₂S₂O₈ and H₂O₂. Calculations revealed that the catalyst containing both Al and Sn could give BVO intermediates lower energies than the Sn-beta zeolite model. The present system exhibited merits including wider substrate scope, innocuous catalytic metal, greener oxidant, as well as lower catalyst cost.

Alexandra Brintrup, George Baryannis, Ashutosh Tiwari et al.

While the increased use of AI in the manufacturing sector has been widely noted, there is little understanding on the risks that it may raise in a manufacturing organisation. Although various high level frameworks and definitions have been proposed to consolidate potential risks, practitioners struggle with understanding and implementing them. This lack of understanding exposes manufacturing to a multitude of risks, including the organisation, its workers, as well as suppliers and clients. In this paper, we explore and interpret the applicability of responsible, ethical, and trustworthy AI within the context of manufacturing. We then use a broadened adaptation of a machine learning lifecycle to discuss, through the use of illustrative examples, how each step may result in a given AI trustworthiness concern. We additionally propose a number of research questions to the manufacturing research community, in order to help guide future research so that the economic and societal benefits envisaged by AI in manufacturing are delivered safely and responsibly.

Hongliang Li, Herschel C. Pangborn, Ilya Kovalenko

The manufacturing sector has a substantial influence on worldwide energy consumption. Therefore, improving manufacturing system energy efficiency is becoming increasingly important as the world strives to move toward a more resilient and sustainable energy paradigm. Batch processes are a major contributor to energy consumption in manufacturing systems. In batch manufacturing, a number of parts are grouped together before starting a batch process. To improve the scheduling and control of batch manufacturing processes, we propose a system-level energy-efficient Digital Twin framework that considers Time-of-Use (TOU) energy pricing for runtime decision-making. As part of this framework, we develop a model that combines batch manufacturing process dynamics and TOU-based energy cost. We also provide an optimization-based decision-making algorithm that makes batch scheduling decisions during runtime. A simulated case study showcases the benefits of the proposed framework.

Jiayao Chen, Pengfei Tan, Xiaojiang Liu et al.

Multi Jet Fusion (MJF) is a fast-growing powder bed fusion(PBF) additive manufacturing technique which features low production costs and high production speeds. However, MJF currently suffers from limited choice of commercially available composite powders. Here, a new type of composite powder, aramid fibre (AF)–filled polyamide 12 (PA12), was developed for MJF to enhance the mechanical properties of the printed parts. The process–structure–property relationship was established by analysing the fibre arrangement in the composite and systematically investigating the effects of the fibre fraction, fibre length, layer thickness, build orientation, and post-annealing process on the structures and mechanical properties of the printed parts. The results showed significant enhancement in the mechanical performance of the AF/PA12 composites parts in the roller recoating direction along which the fibres preferred to align. The ultimate tensile strength and Young’s modulus of the optimised composite parts were increased by 27% and 179% and further improved by 40% and 216% through a post-annealing process, respectively, compared with those of the neat PA12 part. The manufacturing methodology of these high-strength light-weight composites can be further extended to other PBF techniques for applications in a broad spectrum of fields.

NICULESCU Olga, GAIDAU Carmen, RAPA Maria et al.

Biocides used in the leather and footwear industry, based on beta-naphthol, benzothiazole and sulfone derivatives, etc. are toxic to humans and environment, some of these being prohibited by the directives in force. Many studies revealed utilization of essential oils for leather and leather objects protection against fungi. Fungi grow quickly under high humidity conditions and temperatures ranging between 25 and 30°C. Research aims the replacement of potentially toxic biocides with ecologic materials – essential oils extracted from plants. The biological activity of essential oils depends on their composition. Essential oils that contain substituted phenols (eugenol) exhibit strong antibacterial and antioxidant effects.The paper presents the possibility of using essential oil of laurel as alternative preservatives for skin treatment. Essential oil isolated from laurel (Laurus Nobilis) containing: eugenol – 46.95%, DL limonene – 43.37% and alpha terpinolene – 7.14%. Testing of essential oil of laurel was carried out monitoring the manner in which mold growth is influenced by the treatment applied to the sample through the resistance to mold in simulated contamination conditions. Following the study, it is concluded that the selected essential oil of laurel can be used as an antifungal agent in the field of natural leather footwear processing. The natural leathers for linings can be treated with new product based on essential oil with antifungal and antibacterial effect.

Javier Cravino, Sebastián Sotelo

We study how international trade affects manufacturing employment and the relative wage of unskilled workers when goods and services are traded with different intensities. Manufacturing trade reduces manufacturing prices worldwide, which reduces manufacturing employment if manufactures and services are complements. International trade also raises real income, which reduces manufacturing employment if services are more income elastic than manufactures. Manufacturing production is unskilled-labor-intensive, so that these changes increase the skill premium. We incorporate these mechanisms in a quantitative trade model and show that reductions in trade costs had a negative impact on manufacturing employment and the relative wage of unskilled workers. (JEL F16, J24, J31, L60)