1,894 research outputs found

    Robust Digital Twin Compositions for Industry 4.0 Smart Manufacturing Systems

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    Industry 4.0 is an emerging business paradigm that is reaping the benefits of enabling technologies driving intelligent systems and environments. By acquiring, processing and acting upon various kinds of relevant context information, smart automated manufacturing systems can make well-informed decisions to adapt and optimize their production processes at runtime. To manage this complexity, the manufacturing world is proposing the ‘Digital Twin’ model to represent physical products in the real space and their virtual counterparts in the virtual space, with data connections to tie the virtual and real products together for an augmented view of the manufacturing workflow. The benefits of such representations are simplified process simulations and efficiency optimizations, predictions, early warnings, etc. However, the robustness and fidelity of digital twins are a critical concern, especially when independently developed production systems and corresponding digital twins interfere with one another in a manufacturing workflow and jeopardize the proper behavior of production systems. We therefore evaluate the addition of safeguards to digital twins for smart cyber-physical production systems (CPPS) in an Industry 4.0 manufacturing workflow in the form of feature toggles that are managed at runtime by software circuit breakers. Our evaluation shows how these improvements can increase the robustness of interacting digital twins by avoiding local errors from cascading through the distributed production or manufacturing workflow.status: publishe

    Smart manufacturing scheduling: A literature review

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    [EN] Within the scheduling framework, the potential of digital twin (DT) technology, based on virtualisation and intelligent algorithms to simulate and optimise manufacturing, enables an interaction with processes and modifies their course of action in time synchrony in the event of disruptive events. This is a valuable capability for automating scheduling and confers it autonomy. Automatic and autonomous scheduling management can be encouraged by promoting the elimination of disruptions due to the appearance of defects, regardless of their origin. Hence the zero-defect manufacturing (ZDM) management model oriented towards zero-disturbance and zero-disruption objectives has barely been studied. Both strategies combine the optimisation of production processes by implementing DTs and promoting ZDM objectives to facilitate the modelling of automatic and autonomous scheduling systems. In this context, this particular vision of the scheduling process is called smart manufacturing scheduling (SMS). The aim of this paper is to review the existing scientific literature on the scheduling problem that considers the DT technology approach and the ZDM model to achieve self-management and reduce or eliminate the need for human intervention. Specifically, 68 research articles were identified and analysed. The main results of this paper are to: (i) find methodological trends to approach SMS models, where three trends were identified; i.e. using DT technology and the ZDM model, utilising other enabling digital technologies and incorporating inherent SMS capabilities into scheduling; (ii) present the main SMS alignment axes of each methodological trend; (iii) provide a map to classify the literature that comes the closest to the SMS concept; (iv) discuss the main findings and research gaps identified by this study. Finally, managerial implications and opportunities for further research are identified.This work was supported by the Spanish Ministry of Science, Innovation and Universities project entitled 'Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0) ' (RTI2018-101344-B-I00) , the European Union H2020 research and innovation programme with grant agreement No. 825631 "Zero Defect Manufacturing Platform (ZDMP) " and the European Union H2020 research and innovation programme with agreement No. 958205 "In-dustrial Data Services for Quality Control in Smart Manufacturing (i4Q) ".Serrano-Ruiz, JC.; Mula, J.; Poler, R. (2021). Smart manufacturing scheduling: A literature review. Journal of Manufacturing Systems. 61:265-287. https://doi.org/10.1016/j.jmsy.2021.09.0112652876

    Modeling 4.0: Conceptual Modeling in a Digital Era

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    Digitization provides entirely new affordances for our economies and societies. This leads to previously unseen design opportunities and complexities as systems and their boundaries are re-defined, creating a demand for appropriate methods to support design that caters to these new demands. Conceptual modeling is an established means for this, but it needs to be advanced to adequately depict the requirements of digitization. However, unlike the actual deployment of digital technologies in various industries, the domain of conceptual modeling itself has not yet undergone a comprehensive renewal in light of digitization. Therefore, inspired by the notion of Industry 4.0, an overarching concept for digital manufacturing, in this commentary paper, we propose Modeling 4.0 as the notion for conceptual modeling mechanisms in a digital environment. In total, 12 mechanisms of conceptual modeling are distinguished, providing ample guidance for academics and professionals interested in ensuring that modeling techniques and methods continue to fit contemporary and emerging requirements

    A comparison of processing techniques for producing prototype injection moulding inserts.

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    This project involves the investigation of processing techniques for producing low-cost moulding inserts used in the particulate injection moulding (PIM) process. Prototype moulds were made from both additive and subtractive processes as well as a combination of the two. The general motivation for this was to reduce the entry cost of users when considering PIM. PIM cavity inserts were first made by conventional machining from a polymer block using the pocket NC desktop mill. PIM cavity inserts were also made by fused filament deposition modelling using the Tiertime UP plus 3D printer. The injection moulding trials manifested in surface finish and part removal defects. The feedstock was a titanium metal blend which is brittle in comparison to commodity polymers. That in combination with the mesoscale features, small cross-sections and complex geometries were considered the main problems. For both processing methods, fixes were identified and made to test the theory. These consisted of a blended approach that saw a combination of both the additive and subtractive processes being used. The parts produced from the three processing methods are investigated and their respective merits and issues are discussed

    Reducing risk in pre-production investigations through undergraduate engineering projects.

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    This poster is the culmination of final year Bachelor of Engineering Technology (B.Eng.Tech) student projects in 2017 and 2018. The B.Eng.Tech is a level seven qualification that aligns with the Sydney accord for a three-year engineering degree and hence is internationally benchmarked. The enabling mechanism of these projects is the industry connectivity that creates real-world projects and highlights the benefits of the investigation of process at the technologist level. The methodologies we use are basic and transparent, with enough depth of technical knowledge to ensure the industry partners gain from the collaboration process. The process we use minimizes the disconnect between the student and the industry supervisor while maintaining the academic freedom of the student and the commercial sensitivities of the supervisor. The general motivation for this approach is the reduction of the entry cost of the industry to enable consideration of new technologies and thereby reducing risk to core business and shareholder profits. The poster presents several images and interpretive dialogue to explain the positive and negative aspects of the student process

    Simple Design Approach for Shared Digital Twins

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    The collaborative utilization of data becomes increasingly important in industry and requires increased consideration of interoperability and data sovereignty aspects. Distributed systems play a decisive role in this context, which allow for a closer communication between the stakeholders involved and are characterized by the shared use of data and devices. At the same time new concepts emerge that enable a structured mapping of data. These include Digital Twins, which primarily allow a holistic digital representation of an entire asset lifecycle. Digital Twins offer significant potential for distributed systems and form a suitable basis for the collaborative utilization of an asset's lifecycle data. Although studies assume an increased use of Digital Twins in cross-company networks, they are still predominantly used as a purely company-internal concept. In the context of this publication, we demonstrate how to get started easily with the design of Digital Twins intended for use in collaborative distributed systems

    Harnessing the Power of Digital Twins for Enhanced Material Behavior Prediction and Manufacturing Process Optimization in Materials Engineering

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    The advent of Industry 4.0 and the digital revolution have brought forth innovative technologies such as digital twins, which have the potential to redefine the landscape of materials engineering. Digital twins, virtual representations of physical entities, can model and predict material behavior, enabling enhanced design, testing, and manufacturing of materials. However, the comprehensive utilization of digital twins for predictive analysis and process optimization in materials engineering remains largely uncharted. This research intends to delve into this intriguing intersection, investigating the capabilities of digital twins in predicting material behavior and optimizing manufacturing processes, thereby contributing to the evolution of advanced materials manufacturing. Our study will commence with a detailed exploration of the concept of digital twins and their specific applications in materials engineering, emphasizing their ability to simulate intricate material behaviors and processes in a virtual environment. Subsequently, we will focus on exploiting digital twins for predicting diverse material behaviors such as mechanical properties, failure modes, and phase transformations, demonstrating how digital twins can utilize a combination of historical data, real-time monitoring, and sophisticated algorithms to predict outcomes accurately. Furthermore, we will delve into the role of digital twins in optimizing materials manufacturing processes, including casting, machining, and additive manufacturing, illustrating how digital twins can model these processes, identify potential issues, and suggest optimal parameters. We will present detailed case studies to provide practical insights into the implementation of digital twins in materials engineering, including the advantages and challenges. The final segment of our research will address the current challenges in implementing digital twins, such as data quality, model validation, and computational demands, proposing potential solutions and outlining future directions. This research aims to underline the transformative potential of digital twins in materials engineering, thereby paving the way for more efficient, sustainable, and intelligent material design and manufacturing processes

    IoTwins: Design and implementation of a platform for the management of digital twins in industrial scenarios

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    With the increase of the volume of data produced by IoT devices, there is a growing demand of applications capable of elaborating data anywhere along the IoT-to-Cloud path (Edge/Fog). In industrial environments, strict real-time constraints require computation to run as close to the data origin as possible (e.g., IoT Gateway or Edge nodes), whilst batch-wise tasks such as Big Data analytics and Machine Learning model training are advised to run on the Cloud, where computing resources are abundant. The H2020 IoTwins project leverages the digital twin concept to implement virtual representation of physical assets (e.g., machine parts, machines, production/control processes) and deliver a software platform that will help enterprises, and in particular SMEs, to build highly innovative, AI-based services that exploit the potential of IoT/Edge/Cloud computing paradigms. In this paper, we discuss the design principles of the IoTwins reference architecture, delving into technical details of its components and offered functionalities, and propose an exemplary software implementation

    Applications of Industry 4.0 Digital Technologies Towards A Construction Circular Economy: Thematic, Gap Analysis and Conceptual Framework

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    Purpose This paper aims to explore the emerging relationship between Industry 4.0 (I4.0) digital technologies (e.g. blockchain, Internet of Things (IoT) and artificial intelligence (AI)) and the construction industry’s gradual transition into a circular economy (CE) system to foster the adoption of circular economy in the construction industry. Design/methodology/approach A critical and thematic analysis conducted on 115 scientific papers reveals a noticeable growth in adopting digital technologies to leverage a CE system. Moreover, a conceptual framework is developed to show the interrelationship between different I4.0 technologies to foster the implantation of CE in the construction industry. Findings Most of the existing bodies of research provide conceptual solutions rather than developing workable applications and the future of smart cities. Moreover, the coalescence of different technologies is highly recommended to enable tracking of building assets’ and components’ (e.g. fixtures and fittings and structural components) performance, which enables users to optimize the salvage value of components reusing or recycling them just in time and extending assets’ operating lifetime. Finally, circular supply chain management must be adopted for both new and existing buildings to realise the industry's CE ambitions. Hence, further applied research is required to foster CE adoption for existing cities and infrastructure that connects them. Originality/value This paper investigates the interrelationships between most emerging digital technologies and circular economy and concludes with the development of a conceptual digital ecosystem to integrate IoT, blockchain and AI into the operation of assets to direct future practical research application
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