Showing papers in "Journal of Manufacturing Systems in 2021"

TL;DR: A comprehensive and in-depth review of these literatures to analyze digital twin from the perspective of concepts, technologies, and industrial applications is conducted.

TL;DR: 5-dimension digital twin model provides reference guidance for understanding and implementing digital twin, and the frequently-used enabling technologies and tools for digital twin are investigated and summarized to provide Technologies and tools references for the applications of digital twin in the future.

TL;DR: In this paper, the authors discuss the co-existence of two industrial revolutions, namely Industry 4.0 and Industry 5.0, and present five questions that need to be answered.

TL;DR: The definitions, frameworks, major design steps, new blueprint models, key enabling technologies, design cases, and research directions of digital twins-based SMS design are presented and it is expected that this survey will shed new light on urgent industrial concerns in developing new SMSs in the Industry 4.0 era.

TL;DR: Two key enabling technologies for enabling the customized and software-defined design of flow-type smart manufacturing systems are presented, including the generalized encapsulation of the quad-play CMCO model and the digital twin technique.

TL;DR: A survey covering the main CPS architecture models available in the industrial environment, emphasizing their key characteristics and technologies, as well as the correlations among them, pointing objectives, advantages and contribution for the IIoT introduction in I4.0.

TL;DR: In this paper, a five-dimensional digital twin (DT) is proposed to fuse both real and simulated data to provide more information for the prediction of machine availability on one hand; and on the other hand, it helps to detect disturbances through comparing the physical machine with its continuously updated digital counterpart in real time, triggering timely rescheduling when needed.

TL;DR: The authors show that integrating a smart agent into the industrial platforms further expands the usage of the system-level digital twin, where intelligent control algorithms are trained and verified upfront before deployed to the physical world for implementation.

TL;DR: This work proposed a Digital Twin modeling method based on biomimicry principles that can adaptively construct a multi-physics digital twin of the machining process, and developed multiple Digital Twin sub-models, e.g., geometry model, behavior model and process model.

TL;DR: A comprehensive review of associated topics such as the concept of big data, model driven and data driven methodologies and the challenges and opportunities for future research are highlighted.

TL;DR: A comprehensive survey and investigation of how widespread AI and IoT are among manufacturing SMEs is presented, and the current limitations and opportunities towards enabling predictive analytics are discussed.

TL;DR: A digital twin reference model for smart assembly process design is presented, an application framework for DT-based smart assembly with three layers is proposed, and an approach application for an experimental simplified satellite assembly case is shown to verify the proposed application framework and method effectiveness.

TL;DR: In this article, the authors conducted a literature survey to answer two research questions, i.e. what the main research contributions are to combat the COVID-19 pandemic from the robotic technologies' perspective, and what the promising supporting technologies are needed during and after the pandemic to help and guide future robotics research.

TL;DR: A systematic review of the recent literature on AR applications developed for smart manufacturing shows how AR has been used to facilitate various manufacturing operations with intelligence and guidelines for implementing AR assisted functions with practical applications in smart manufacturing in the near future.

TL;DR: This paper focuses on the modeling methods for rapidly creating a virtual model and the connection implementation mechanism between a physical world production system at a workshop level and its mirrored virtual model.

TL;DR: A vision for next-generation Industry 4.0 manufacturing machines, which will exhibit extraordinary Smart and Reconfigurable (SR*) capabilities is established.

TL;DR: Experimental results show that the proposed fault diagnosis approach has promising performance in identifying different faults related to the reducer of a 6-axial multi-joint industrial robot accurately and reliably.

TL;DR: The framework of data construction is designed based on the functional requirements, which are analyzed according to the characteristics of manufacturing data, and application of cutting tool wear prediction is taken as a case study to show the feasibility and effectiveness of the proposed data construction method.

TL;DR: A new framework of data-driven smart customisation augmented by digital twin is presented to facilitate improved collaboration of all stakeholders in the customisation process and to make the process more responsive, adaptable and predictive.

TL;DR: This paper addresses the problems of data management and analytics for decision-aid by proposing a new vision of Digital Shadow (DS), which would be considered as the core component of a future Digital Twin.

TL;DR: In this article, the authors systematically summarize and analyze all major efforts put forward by the additive manufacturing industry, academics, researchers, users, and individuals during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rapidly achieved global pandemic status.

TL;DR: In this article, the authors discuss the barriers to digital transformation before, during, and what may well continue after the COVID-19 pandemic and offer a new strategy discipline, Strategic Doing, that may be useful to manufacturing firms in achieving successful digital transformation.

TL;DR: A digital twin-based big data virtual and real fusion (DT-BDVRL) reference framework supported by Industrial Internet towards smart manufacturing and the results that run in virtual spaces are synchronized and executed in physical spaces through Industrial Internet platforms, effectively improving the physical execution effect of BDLA models.

TL;DR: This work stepwise introduces an industrial knowledge graph (IKG)-based multi-agent reinforcement learning (MARL) method for achieving the Self-X cognitive manufacturing network and proposes a graph neural network-based embedding algorithm to achieve semantic-based self-configurable solution searching and task decomposition.

TL;DR: A general architecture of digital-Twin visualization for flexible manufacturing systems (FMS) is presented and a digital-twin modeling concept of "Geometric information (G)-Historical samples (H)-Object attribute (O)-Snapshot collection (S)-Topology constraint (T)" (GHOST) is proposed, and methods for developing virtual digital- twin scenes architecture are presented.

TL;DR: CNN accuracy was demonstrated to be a function of flaw size, suggesting that smaller flaws may be produced by mechanisms that do not alter the surface morphology of the build plate, and improved generalizability was demonstrated, i.e., the ability to generalize to more diverse data than either the training or validation data sets.

TL;DR: An integrated system that consists of a real material handling system and its digital twin, based on physics simulation is described, which demonstrates the versatile advantages of the implemented solution for a manufacturing system with respect to the three digital twin functions prediction, monitoring and diagnosis.

TL;DR: An evolutionary concurrent modeling method for DT (ECoM4DT) is proposed not only inheriting the theory from classic M&S methods but also highlighting the characteristics of DT compared with traditional models to systemically guide the DT modeling process.

TL;DR: A digital twin-based assembly data management and process traceability approach for complex products is proposed and the Digital Twin-based Assembly Process Management and Control System (DT-APMCS) was designed to verify the efficiency of the proposed approach.

TL;DR: A novel deep convolutional neural network-bootstrap-based integrated prognostic approach for the remaining useful life (RUL) prediction of rolling bearing is developed and the RUL prediction interval can be effectively quantified without relying on the bearing’s physical or statistical prior information based on bootstrap implementation paradigm.