Hasil untuk "Manufactures"

H. Akagi

Hunt Allcott, Daniel E. Keniston

Rongfei Li, Francis Assadian

The use of robotic technology has drastically increased in manufacturing in the 21st century. But by utilizing their sensory cues, humans still outperform machines, especially in the micro scale manufacturing, which requires high-precision robot manipulators. These sensory cues naturally compensate for high level of uncertainties that exist in the manufacturing environment. Uncertainties in performing manufacturing tasks may come from measurement noise, model inaccuracy, joint compliance (e.g., elasticity) etc. Although advanced metrology sensors and high-precision microprocessors, which are utilized in nowadays robots, have compensated for many structural and dynamic errors in robot positioning, but a well-designed control algorithm still works as a comparable and cheaper alternative to reduce uncertainties in automated manufacturing. Our work illustrates that a multi-robot control system can reduce various uncertainties to a great amount.

André de Mendonça Santos, Maíra Pinto Oliveira

O desenvolvimento da Indústria 4.0 promoveu grandes mudanças na logística, impulsionando a automação, digitalização e integração dos processos operacionais. Com base neste contexto, este estudo tem como objetivo avaliar a aplicação das tecnologias da Indústria 4.0 em uma empresa de transporte e serviços localizada em Feira de Santana-BA, analisando os desafios e as possibilidades de sua implementação. Com isso, foi realizada uma pesquisa qualitativa, composta por pesquisa bibliográfica, entrevista com gestor e visita técnica à empresa. A análise foi conduzida por meio da matriz SWOT da empresa, permitindo a identificação de fatores internos e externos que influenciam a adoção dessas tecnologias. A partir deste diagnóstico, foram propostas estratégias para otimizar os processos em termos de logística, reduções diretas das limitações operacionais e novas oportunidades a serem exploradas dentro do contexto da Logística 4.0.

Xin Lin, Xun Li, Yan Zhou et al.

Non-equilibrium solidification during NiTi alloys manufacturing via Laser Beam Powder Bed Fusion (PBF-LB), induces dendritic morphologies and heterogeneous precipitate distribution. However, the underlying mechanisms governing dislocation interactions remain poorly understood. An improved phase-field model coupled with melt pool temperature field is proposed to simulate the impact of dendritic growth behavior on precipitate distribution under spatiotemporal variations of temperature gradient (G) and solidification rate (R). Additionally, this study proposes a volume-conserved deformation scheme that couples the phase-field crystal (PFC) method with a thermal-stress dynamics model to simulate the influence of precipitates on dislocation motion. The results reveal that reduced volumetric energy density enhances cooling rates, decreasing primary dendrite arm spacing (PDAS), mitigating elemental segregation, and refining Ti2Ni precipitates with homogeneous grain boundary dispersion. Uniformly dispersed precipitates shorten dislocation recovery time and increase pinning probability at phase interfaces, resulting in effectively hindering dislocation glide while promoting multiplication and entanglement. This leads to high-density disordered dislocation networks, contrasting with well-defined dislocation cells formed under high energy density.

Mohamed Abdelaal

In this paper, we explore the transformative impact of Artificial Intelligence (AI) in the manufacturing sector, highlighting its potential to revolutionize industry practices and enhance operational efficiency. We delve into various applications of AI in manufacturing, with a particular emphasis on human-machine interfaces (HMI) and AI-powered milling machines, showcasing how these technologies contribute to more intuitive operations and precision in production processes. Through rigorous market analysis, the paper presents insightful data on AI adoption rates among German manufacturers, comparing these figures with global trends and exploring the specific uses of AI in production, maintenance, customer service, and more. In addition, the paper examines the emerging field of Generative AI and the potential applications of large language models in manufacturing processes. The findings indicate a significant increase in AI adoption from 6% in 2020 to 13.3% in 2023 among German companies, with a projection of substantial economic impact by 2030. The study also addresses the challenges faced by companies, such as data quality and integration hurdles, providing a balanced view of the opportunities and obstacles in AI implementation.

Ahmed R. Sadik, Bodo Urban, Omar Adel

Worker-Robot Cooperation is a new industrial trend, which aims to sum the advantages of both the human and the industrial robot to afford a new intelligent manufacturing techniques. The cooperative manufacturing between the worker and the robot contains other elements such as the product parts and the manufacturing tools. All these production elements must cooperate in one manufacturing workcell to fulfill the production requirements. The manufacturing control system is the mean to connect all these cooperative elements together in one body. This manufacturing control system is distributed and autonomous due to the nature of the cooperative workcell. Accordingly, this article proposes the holonic control architecture as the manufacturing concept of the cooperative workcell. Furthermore, the article focuses on the feasibility of this manufacturing concept, by applying it over a case study that involves the cooperation between a dual-arm robot and a worker. During this case study, the worker uses a variety of hand gestures to cooperate with the robot to achieve the highest production flexibility

Xiao Liu, Alessandra Mileo, Alan F. Smeaton

In-situ monitoring incorporating data from visual and other sensor technologies, allows the collection of extensive datasets during the Additive Manufacturing (AM) process. These datasets have potential for determining the quality of the manufactured output and the detection of defects through the use of Machine Learning during the manufacturing process. Open and annotated datasets derived from AM processes are necessary for the machine learning community to address this opportunity, which creates difficulties in the application of computer vision-related machine learning in AM. This systematic review investigates the availability of open image-based datasets originating from AM processes that align with a number of pre-defined selection criteria. The review identifies existing gaps among the current image-based datasets in the domain of AM, and points to the need for greater availability of open datasets in order to allow quality assessment and defect detection during additive manufacturing, to develop.

Ghi Tran Nha, Trung Nguyen Tan, Long Nguyen Thanh et al.

This study is conducted to explain entrepreneurial support resources of firms based on social network theory in developing countries, the case of Vietnam. Partial Least Squares Structural Modeling (PLS-SEM) was conducted with a sample size of 220 entrepreneurs in SMEs. The results supported the positive link between formal and informal networks and entrepreneurial firm performance. Second, the study explored the partial mediating role of access to entrepreneurial resources between formal networks, informal networks, and entrepreneurial firm performance. In addition, the results also provide practical value to entrepreneurs in actively building relationship networking in the entrepreneurship ecosystem. Finally, the study proposed some implications for entrepreneurs, limitations, and further research.

Md Habibor Rahman, Erfan Yazdandoost Hamedani, Young-Jun Son et al.

Identifying, analyzing, and evaluating cybersecurity risks are essential to assess the vulnerabilities of modern manufacturing infrastructures and to devise effective decision-making strategies to secure critical manufacturing against potential cyberattacks. In response, this work proposes a graph-theoretic approach for risk modeling and assessment to address the lack of quantitative cybersecurity risk assessment frameworks for smart manufacturing systems. In doing so, first, threat attributes are represented using an attack graphical model derived from manufacturing cyberattack taxonomies. Attack taxonomies offer consistent structures to categorize threat attributes, and the graphical approach helps model their interdependence. Second, the graphs are analyzed to explore how threat events can propagate through the manufacturing value chain and identify the manufacturing assets that threat actors can access and compromise during a threat event. Third, the proposed method identifies the attack path that maximizes the likelihood of success and minimizes the attack detection probability, and then computes the associated cybersecurity risk. Finally, the proposed risk modeling and assessment framework is demonstrated via an interconnected smart manufacturing system illustrative example. Using the proposed approach, practitioners can identify critical connections and manufacturing assets requiring prioritized security controls and develop and deploy appropriate defense measures accordingly.

Michael Chiu, Jyotiraditya Panda, Abraham Goldsmith et al.

We propose a blockchain-based solution for enabling verifiability of manufacturing processes. We base our solution on the methodology of verifiable computing which, originally developed for cloud computing, enables clients to outsource computations to more powerful servers without the need to trust that the server correctly performed desired computation. Verifiable computing accomplishes this by enabling the client to generate cryptographic objects that the server must use to produce a cryptographic proof that verifies the correctness of results. The black box nature of servers in cloud computing is analogous to that of the manufacturing processes of an upstream manufacturer. In this work, we develop a one-to-one correspondence between physical processes and their digital representations as state sequences which is needed for the implementation of verifiable computing. Because direct application of verifiable computing in this case would be computationally prohibitive, we introduce a blockchain to provide a computationally feasible methodology for verifiable computing applied to physical processes. We implement and show the results of our implementation on a proof of concept, developed on Hyperledger Fabric.

Massimo Carraturo, Andrea Mazzullo

A key challenge for Industry 4.0 applications is to develop control systems for automated manufacturing services that are capable of addressing both data integration and semantic interoperability issues, as well as monitoring and decision making tasks. To address such an issue in advanced manufacturing systems, principled knowledge representation approaches based on formal ontologies have been proposed as a foundation to information management and maintenance in presence of heterogeneous data sources. In addition, ontologies provide reasoning and querying capabilities to aid domain experts and end users in the context of constraint validation and decision making. Finally, ontology-based approaches to advanced manufacturing services can support the explainability and interpretability of the behaviour of monitoring, control, and simulation systems that are based on black-box machine learning algorithms. In this work, we provide a novel ontology for the classification of process-induced defects known from the metal additive manufacturing literature. Together with a formal representation of the characterising features and sources of defects, we integrate our knowledge base with state-of-the-art ontologies in the field. Our knowledge base aims at enhancing the modelling capabilities of additive manufacturing ontologies by adding further defect analysis terminology and diagnostic inference features.

Liyao Song, Bai Chen, Bo Li et al.

Purpose – The supercritical design of tail rotor drive shaft has attracted more attention in helicopter design due to its high power–weight ratio and low maintenance cost. However, there exists excessive vibration when the shaft passes through the critical frequency. Dry friction damper is the equipment applied to the drive shaft to suppress the excessive vibration. In order to figure out the damping mechanism of the dry friction damper and improve the damping efficiency, the dynamic model of the shaft/damper system is established based on the Jeffcott rotor model. Design/methodology/approach – The typical frequency response of the system is studied through bifurcation diagrams, amplitude-frequency characteristic curves and waterfall frequency response spectrum. The typical transient responses under frequency sweeps are also obtained. Findings – The results show that the response of the system changes from periodic no-rub motion to quasi-periodic rub-impact motion, and then to synchronous full annular rub-impact, and finally, back to periodic no-rub motion. The slip of the rub-impact ring improves the stability of the system. Besides, the effects of the system parameters including critical dry friction force, rub-impact friction coefficient, initial clearance on the stability and the vibration damping capacity are studied. It is observed that the stability changes significantly varying the three parameters respectively. The vibration damping capacity is mainly affected by the critical dry friction force and the initial clearance. Originality/value – Presented results provide guidance for the design of the dry friction damper.

Sourish Dutta

The topic of my research is "Learning and Upgrading in Global Value Chains: An Analysis of India's Manufacturing Sector". To analyse India's learning and upgrading through position, functions, specialisation & value addition of manufacturing GVCs, it is required to quantify the extent, drivers, and impacts of India's Manufacturing links in GVCs. I have transformed this overall broad objective into three fundamental questions: (1) What is the extent of India's Manufacturing Links in GVCs? (2) What are the determinants of India's Manufacturing Links in GVCs? (3) What are the impacts of India's Manufacturing Links in GVCs? These three objectives represent my three chapters in my PhD thesis.

Tim Bolender, Gereon Bürvenich, Manuela Dalibor et al.

Digital Twins are part of the vision of Industry 4.0 to represent, control, predict, and optimize the behavior of Cyber-Physical Production Systems (CPPSs). These CPPSs are long-living complex systems deployed to and configured for diverse environments. Due to specific deployment, configuration, wear and tear, or other environmental effects, their behavior might diverge from the intended behavior over time. Properly adapting the configuration of CPPSs then relies on the expertise of human operators. Digital Twins (DTs) that reify this expertise and learn from it to address unforeseen challenges can significantly facilitate self-adaptive manufacturing where experience is very specific and, hence, insufficient to employ deep learning techniques. We leverage the explicit modeling of domain expertise through case-based reasoning to improve the capabilities of Digital Twins for adapting to such situations. To this effect, we present a modeling framework for self-adaptive manufacturing that supports modeling domain-specific cases, describing rules for case similarity and case-based reasoning within a modular Digital Twin. Automatically configuring Digital Twins based on explicitly modeled domain expertise can improve manufacturing times, reduce wastage, and, ultimately, contribute to better sustainable manufacturing.

Mojtaba Mozaffar, Jian Cao

We present a novel computational paradigm for process design in manufacturing processes that incorporates simulation responses to optimize manufacturing process parameters in high-dimensional temporal and spatial design spaces. We developed a differentiable finite element analysis framework using automatic differentiation which allows accurate optimization of challenging process parameters such as time-series laser power. We demonstrate the capability of our proposed method through three illustrative case studies in additive manufacturing for: (i) material and process parameter inference using partial observable data, (ii) controlling time-series thermal behavior, and (iii) stabilizing melt pool depth. This research opens new avenues for high-dimensional manufacturing design using solid mechanics simulation tools such as finite element methods. Our codes are made publicly available for the research community at https://github.com/mojtabamozaffar/differentiable-simulation-am.

Agus Darmawan, D. Daniel Sheu

This paper presents a framework for preventive maintenance (PM) scheduling in the semiconductor industry. We propose an approach for finding PM’s start time within a PM window to minimize production losses due to maintenance activities. In this study, we consider re-entrant process in which wafers will enter the same equipment location several times, but in different stages and sometimes different processes. Due to the optimization problem’s complexity, we develop meta-heuristics such as a genetic algorithm and particle swarm optimization to solve it and compare with the resource leveling as well as the baseline. In the algorithm, we embed discrete event simulation to mimic a wafer fab process and get its performance. The proposed approach able to identify the best arrangement of PM’s start time within a PM window and provides a way to optimize PM schedules for a complex system by simultaneously utilizing meta-heuristics and discrete event simulation.

Ahmad Azmy

This study aimed to analyzed employee engagement and job satisfaction with workforce agility through talent management as a mediating variable. The object of research was carried out at one of the public transportation companies. The number of respondents was 100 people. This research is purposive because it is following the research needs. The analysis tool uses the Partial Least Square (PLS) method. It aims to analyze specifically the variables and indicators that affect workforce agility. The results showed that employee engagement and job satisfaction had a positive effect on workforce agility. The role of talent management as a mediating variable affects workforce agility. Organizations must maximize the role of talent management to prepare employee competencies according to business challenges. The implementation of employee engagement and job satisfaction will make employees more agile, responsive, and have high initiatives to generate business innovation. Job satisfaction is very much needed in maintaining performance stability. The business process is very dependent on how the role and involvement of employees in executing the business plan. The four variables explain that workforce agility makes employee responsiveness higher in advancing the company's business. Therefore, organizations must be responsive and adaptive in empowering human resources optimally