Search results

The purpose of this study is to explore a new assembly process planning and execution mode to realize rapid response, reduce the labor intensity of assembly workers and improve the assembly efficiency and quality.

Based on the related concepts of digital twin, this paper studies the product assembly planning in digital space, the process execution in physical space and the interaction between digital space and physical space. The assembly process planning is simulated and verified in the digital space to generate three-dimensional visual assembly process specification documents, the implementation of the assembly process specification documents in the physical space is monitored and feed back to revise the assembly process and improve the assembly quality.

Digital twin technology enhances the quality and efficiency of assembly process planning and execution system.

It provides a new perspective for assembly process planning and execution, the architecture, connections and data acquisition approaches of the digital twin-driven framework are proposed in this paper, which is of important theoretical values. What is more, a smart assembly workbench is developed, the specific image classification algorithms are presented in detail too, which is of some industrial application values.

Despite the opportunities of digital twins (DTs) for smart buildings, limited research has been conducted regarding the facility management stage, and this is explained by the high complexity of accurately representing and modelling the physics behind the DTs process. This study thus organises and consolidates the fragmented literature on DTs implementation for smart buildings at the facility management stage by exploring the enablers, applications and challenges and examining the interrelationships amongst them.

A systematic literature review approach is adopted to analyse and synthesise the existing literature relating to the subject topic.

The study revealed six main categories of enablers of DTs for smart building at the facility management stage, namely perception technologies, network technologies, storage technologies, application technologies, knowledge-building and design processes. Three substantial categories of DTs application for smart buildings were revealed at the facility management stage: efficient operation and service monitoring, efficient building energy management and effective smart building maintenance. Subsequently, the top four major challenges were identified as being “lack of a systematic and comprehensive reference model”, “real-time data integration”, “the complexity and uncertainty nature of real-time data” and “real-time data visualisation”. An integrative framework is finally proposed by examining the interactive relationship amongst the enablers, the applications and the challenges.

The findings could guide facility managers/engineers to fairly understand the enablers, applications and challenges when DTs are being implemented to improve smart building performance and achieve user satisfaction at the facility management stage.

This study contributes to the knowledge body on DTs by extending the scope of the existing studies to identify the enablers and applications of DTs for smart buildings at the facility management stage and the specific challenges.

The possibility of integrating building information in an augmented reality (AR) environment provides an effective solution to all phases of a building's lifecycle. This paper explores the integration of building information modelling (BIM) and AR to effectively visualise building information models in an AR environment and evaluates the currently available AR tools.

A BIM model of a selected office room was created and superimposed to the actual physical space using two different AR devices and four different AR applications. The superimposing techniques, accuracy and the level of information that can be visualised were then investigated by performing a walk-through analysis.

From the investigation, it can be concluded that model positioning can be inaccurate depending on the superimposing method used and the AR device. Moreover, using the currently available techniques, only static building information can be superimposed and visualised in AR, showing a need to integrate data from Internet of Things (IoT) sensors into the current BIM-AR processes to allow visualisation of accurate and high-quality operational building information.

A practical process and method for visualising and superimposing BIM models in an AR environment have been described. Recommendations to improve superimposing accuracy are provided. The assessment of type, quality and level of detail that can be visualised indicates the areas that need improvement to increase the effectiveness of building information's visualisation in AR.

Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.

A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.

Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.

In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.

This paper aims to explore challenges and opportunities of shifting from physical to virtual employment support delivery prompted by the Covid-19 pandemic. It investigates associated changes in the nature and balance of support and implications for beneficiary engagement with programmes and job search.

The study draws on longitudinal interviews conducted with beneficiaries and delivery providers from a neighbourhood-based employment support initiative in an English region with a strong manufacturing heritage between 2019 and 2021. The initiative established prior to the Covid-19 pandemic involved a strong physical presence locally but switched to virtual delivery during Covid-19 lockdowns.

Moving long-term to an entirely virtual model would likely benefit some beneficiaries closer to or already in employment. Conversely, others, particularly lone parents, those further from employment, some older people and those without computer/Internet access and/or digital skills are likely to struggle to navigate virtual systems. The study emphasises the importance of blending the benefits of virtual delivery with aspects of place-based physical support.

Previous studies of neighbourhood-based employment policies indicate the benefits of localised face-to-face support for transforming communities. These were conducted prior to the Covid-19 pandemic and the more widespread growth of virtual employment support. This study fills a gap regarding understanding the challenges and opportunities for different groups of beneficiaries when opportunities for physical encounters decline abruptly and support moves virtually.

Despite the growing attention on the relevance of improved building management systems with cognition in recent years in the architecture, engineering, construction and operation (AECO) community, no review has been conducted to understand the human-environment interaction features of cyber-physical systems (CPS) and digital twins (DTs) in developing the concept of a cognitive building (CB). Thus, this paper aims to review existing studies on CPS and DTs for CB to propose a comprehensive system architecture that considers human-environment interactions.

Scientometric analysis and content analysis were adopted for this study.

The scientometric analysis of 1,042 journal papers showed the major themes of CPS/DTs for CB, and these can be categorized into three key technologies to realize CB in the AECO community: CPS, DTs and cognitive computing (CC). Content analysis of 44 relevant publications in the built environment assisted in understanding and evidently confirming the claim of this study on the integration of CPS and DTs for CB in construction by also involving the CC. It is found and confirmed that CB can be realized with CPS and DTs along with the CC. A CB system architecture (CBSA) is proposed from the three key technologies considering the human-environment interactions in the loop. The study discovered the potential applications of the CBSA across the building lifecycle phases, including the design, construction and operations and maintenance, with the potential promise of endowing resilience, intelligence, greater efficiency and self-adaptiveness. Based on the findings of the review, four research directions are proposed: human-environment interactions, CB for sustainable building performance, CB concept for modular buildings and moving beyond CB.

This study stands out for comprehensively surveying the intellectual core and the landscape of the general body of knowledge on CPS/DTs for CB in the built environment. It makes a distinctive contribution to knowledge as it does not only propose CBSA by integrating CPS and DTs along with CC but also suggests some potential practical applications. These may require expert judgments and real case examples to enhance reproducibility and validation.

This paper aims to present a reverse engineering (RE) approach for three-dimensional (3D) model reconstruction and fast prototyping (FP) of broken chess pieces.

A case study involving a broken chess piece was selected to demonstrate the effectiveness of the proposed unconventional RE approach. Initially, a laser 3D scanner was used to acquire a (non-uniform rational B-spline) surface model of the object, which was then processed to develop a parametric computer aided design (CAD) model combined with geometric design and tolerancing (GD&T) technique for evaluation and then for FP of the part using a computer numerical controlled (CNC) machine.

The effectiveness of the proposed approach for reconstruction and FP of rotational parts was ascertained through a sample part. The study demonstrates non-contact data acquisition technologies such as 3D laser scanners together with RE systems can support to capture the entire part geometry that was broken/worn and developed quickly through the application of computer aided manufacturing principles and a CNC machine. The results indicate that design communication, customer involvement and FP can be efficiently accomplished by means of an integrated RE workflow combined with rapid product development tools and techniques.

This research established a RE approach for the acquisition of broken/worn part data and the development of parametric CAD models. Then, the developed 3D CAD model was inspected for accuracy by means of the GD&T approach and rapidly developed using a CNC machine. Further, the proposed RE led FP approach can provide solutions to similar industrial situations wherein agility in the product design and development process is necessary to produce physical samples and functional replacement parts for aging systems in a short turnaround time.

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

The digital warehouse management system is an emergence that forms a critical part of the transformation of economic structure in Industry 4.0. In the present business scenario, the warehouse management system encounters a messy layout, poor damage control, unsatisfactory order management, lack of visibility and lack of technological interventions. Digital twin (DT) based warehouse system shows the ontology and knowledge graphs for competitive advantage by consolidating and transferring goods directly from an inbound supplier to an outbound customer on short notice and with no or limited storage. There remains a lack of clarity on how the DT can be implemented successfully in warehouse management.

The current literature remains largely unstructured and scattered due to a lack of a systematic approach to integrating the research implications and analysis. This paper probes the conceptualization of the DT with the help of theoretical analysis using the systematic literature analysis method.

The study explores essential concepts such as interoperability and integrability in implementing DT. Further, it analyzes the role of a supply chain control tower (SCCT) in modern supply chain management. A research framework is proposed for practitioners and academicians by incorporating the opportunities and challenges associated with DT implementation. The research findings are mainly threefold: Conceptualization of DT, Featuring SCCT and Exploration of cross-computer platform interfaces, scalability and maintenance strategies.

This study is among the first to analyze and review DT applications in warehouse management. Moreover, the study proposes a theoretical toolbox for the practitioners to successfully implement the DT in warehouse DT-based warehouse management system: A theoretical toolbox for future research and applications.

The purpose of this study is to numerically and experimentally investigate the natural convection heat transfer in flat plates and plates with square, trapezoidal and triangular corrugations.

This work is an extension of the previous studies by Verderio et al. (2021a, 2021b, 2021c, 2021d, 2022a). An experimental apparatus was built to measure the plates’ temperatures during the natural convection cooling process. Several physical parameters were evaluated through the experimental methodology. Free and open-source computational tools were used to simulate the experimental conditions and to quantitatively and qualitatively evaluate the thermal plume characteristics over the plates.

The numerical results were experimentally validated with reasonable accuracy in the range of studied RaLP for the different plates. Empirical correlations of Nu¯LPexp=f(RaLP), h¯conv=f(RaLP) and Nu¯LPexp⋅(A/AP)=f(RaLP), with good accuracy and statistical representativeness, were obtained for the studied geometries. The convective thermal efficiency of corrugated plates (Δη), as a function of RaLP, was also experimentally studied quantitatively. In agreement with the findings of Oosthuizen and Garrett (2001), the experimental and numerical results proved that the increase in the heat exchange area of the corrugations has a greater influence on the convective exchange and the thermal efficiency than the disturbances caused in the flow (which reduce h¯conv). The plate with trapezoidal corrugations presented the highest convective thermal efficiency, followed by the plates with square and triangular corrugations. It was also proved that the thermal efficiency of corrugated plates increases with RaLP.

The results demonstrate that corrugated surfaces have greater thermal efficiency than flat plates in heating and/or cooling systems by natural convection. This way, corrugated plates can reduce the dependence on auxiliary forced convection systems, with application in technological areas and Industry 4.0.

The empirical correlations obtained for the corrected Nusselt number and thermal efficiency for the corrugated plate geometries studied are original and unpublished, as well as the experimental validation of the developed three-dimensional numerical code.