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Digital twin (DT) models are increasingly being used to improve the performance of complex manufacturing systems. In this context, DTs automatically enabling anomaly detection, such as increase in orders, and bottleneck identification, such as shortage of products, can significantly enhance decision-making to mitigate the consequences of the identified bottlenecks. The existing literature has mainly focused on implementing top-down approaches for analysing the bottlenecks without considering the emergent behaviour of micro-level agents, including inventory levels and human resources, and their impact on the macro-level system’s performance. In order to handle the aforementioned challenges, this paper extends the current literature by proposing a novel DT integrated in a multi-agent cyber physical system (CPS) for detecting anomalies in sensor data, while identifying and removing bottlenecks that emerge during the operation of complex manufacturing systems. An extended 5 C CPS architecture, using multi-agent approach, is implemented to allow DT integration. The agent-based simulation technique enables capturing the probabilistic variability, and aggregate parallelism and dynamism of parallel dynamic interactions within the DT-CPS. A new single agent at the exo-level of the multi-level agent-based modelling structure, called the ‘monitoring agent’, is introduced in this research. The agent detects anomalies and identify bottlenecks through communicating with other agents in different levels automatically. The DT-CPS provides feedback automatically to the physical space to remove and mitigate the identified bottlenecks. The proposed DT based multi-agent CPS has been tested successfully on a real case study in a cryogenic warehouse shop-floor from the cell and gene therapy industry. The performance of the studied cryogenic warehouse is continuously measured using real-time sensor data. The analyses of the results show that the proposed DT-CPS improves the utilisation rates of human resources, on average, by 30% supporting decision making and control in complex manufacturing systems. Innovate UK: 104515. Engineering and Physical Sciences Research Council (EPSRC): EP/R032718/1

Objective. This study aims to explore the potential of high-resolution brain functional connectivity based on electroencephalogram, a non-invasive low-cost technique, to be translated into a long-overdue biomarker and a diagnostic method for Alzheimer’s disease (AD). Approach. The paper proposes a novel ultra-high-resolution time-frequency nonlinear cross-spectrum method to construct a promising biomarker of AD pathophysiology. Specifically, using the peak frequency estimated from a revised Hilbert–Huang transformation (RHHT) cross-spectrum as a biomarker, the support vector machine classifier is used to distinguish AD from healthy controls (HCs). Main results. With the combinations of the proposed biomarker and machine learning, we achieved a promising accuracy of 89%. The proposed method performs better than the wavelet cross-spectrum and other functional connectivity measures in the temporal or frequency domain, particularly in the Full, Delta and Alpha bands. Besides, a novel visualisation approach developed from topography is introduced to represent the brain functional connectivity, with which the difference between AD and HCs can be clearly displayed. The interconnections between posterior and other brain regions are obviously affected in AD. Significance. Those findings imply that the proposed RHHT approach could better track dynamic and nonlinear functional connectivity information, paving the way for the development of a novel diagnostic approach.

Product-Service System (PSS) models offer an integrated service solution to create value for businesses. In the high-value manufacturing sector, value creation for maintaining market competitiveness and improving customer satisfaction is a challenging task. Designing an effective PSS solution depends on integrated service, and product requirements and constraints. Thereby, PSS contract decisions can be significantly influenced by customers’ requirements, and also product and service features. However, existing literature primarily focuses on the impact of service requirements on the PSS contract decisions. Moreover, the existing insights for PSS customization mainly consider hysteretic customer requirements rather than forecasting the requirements under product and service uncertainties. In this paper, an agent-based cost-benefit analysis simulation model is implemented for the PSS contract decisions context. Moreover, a sensitivity analysis is conducted on service costs. Additionally, the effect of product remaining life on service contract decisions is analyzed. The simulation model considers stochastic uncertainty to study PSS contracts customization. The presented model supports PSS customization process by providing a quantitative tool that measures contracts’ profitability as early as the requirement elicitation phase. Furthermore, the bottom-up nature of the model, and the integration of probabilistic uncertainties enhance the flexibility of PSS customization. A case study of PSS contract decision in the machine tool industry is considered for assessing the validity of the presented model. Studies on different forms of service uncertainty highlight that the product failure rate has the most influence on the profitability of a service contract. Moreover, the impact of product age on profitability in an availability-based contract is more significant compared to a spare-parts contract.

Digital twins (DTs) offer the potential for improved understanding of current and future manufacturing processes. This can only be achieved by DTs consistently and accurately representing the real processes. However, the robustness and resilience of the DT itself remain an issue. Accordingly, this paper offers an approach to deal with uncertainty and disruptions, as the DT detects these effectively and self-adapts as needed to maintain representativeness. The paper proposes an intelligent agent-based architecture to improve the robustness (including accuracy of representativeness) and resilience (including timely update) of the DT. The approach is demonstrated on a case of cryogenic secondary manufacturing.

Complex manufacturing and supply chain systems consist of concurrent labour-intensive processes and procedures with repetitive time-consuming tasks and multiple quality checks. These features may pose challenges for the efficient operation and management, while manual tasks may significantly increase human errors or near misses, having impact on the propagation of effects and parallel interactions within these systems. In order to handle the aforementioned challenges, a digital twin (DT) integrated in a multi-agent cyber-physical manufacturing system (CPMS) with the help of RFID technology is proposed. The proposed reference architecture tends to improve the trackability and traceability of complex manufacturing processes. In this research work, the interactions occurring both within a single complex manufacturing system and between multiple sites within a supply chain are considered. For the implementation of the integrated DT-CPMS, a simulation model employing the agent-based modelling technique is developed. A case study from a cryogenic supply chain in the UK is also selected to show the application and validity of the proposed digital solution. The results prove that the DT-CPMS architecture can improve system’s performance in terms of human, equipment and space utilisations.

Aircraft manufacturing industries often evolve in the ecosystem of complex designs and manufacturing processes associated with large volume of information generated along the lifecycle. Digital Twin (DT) technology has the potential of leveraging such information to provide useful insights benefiting the overall business in many ways. Information Management (IM) for DT is still an ongoing challenge for many industries, thus leaving a considerable research gap. In this paper, an IM framework for DT in the aircraft manufacturing sector is proposed. The key phases and elements of IM are discussed on which the framework is constructed. The potential application of the framework along aircraft lifecycle is further discussed. The framework not only provides an effective approach to managing information but also opens new research prospects in DT domain.

Digital twin (DT) technology allows the user to monitor the asset, specifically over the operation and service phase of the life cycle, which is the longest-lasting phase for complex engineering assets. This paper aims to present a thematic review of DTs in terms of the technology used, applications, and limitations specifically in the context of maintenance. This review includes a systematic literature review of 59 articles on semantic digital twins in the maintenance context. Key performance indicators and explanations of the main concepts constituting the DT have been presented. This article contains a description of the evolution of DTs together with their characterisation for maintenance purposes. It provides an ontological approach to develop DT and improve the maintenance management leading to the creation of a structured DT or a Cognitive Twin (CT). Moreover, it points out that using a top-level ontology approach should be the starting point for the creation of CT. Enabling the creation of the digital framework that will break down silos, ensuring a perfect integration in a network of twins’ scenario. Engineering and Physical Sciences Research Council (EPSRC): EP/R032718/1

In this paper, a new recursive structure based on the convolution model of discrete cosine transform (DCT) for designing of a finite impulse response (FIR) digital filter is proposed. In our derivation, we start with the convolution model of DCT-II to use its Z-transform for the proposed filter structure perspective. Moreover, using the same algorithm, a filter base implementation of the inverse DCT (IDCT) for image reconstruction is developed. The computational time experiments of the proposed DCT/IDCT filter(s) demonstrate that the proposed filters achieve faster elapsed CPU time compared to the direct recursive structures and recursive algorithms for the DCT/IDCT with Arbitrary Length. Experimental results on clinical ultrasound images and comparisons with classical Wiener filter, non-local mean (NLM) filter and total variation (TV) algorithms are used to validate the improvements of the proposed approaches in both noise reduction and reconstruction performance for ultrasound images.

The prosperity of aircraft transportation together with revolutionary promotion of composite components used in commercial aircraft pose enormous challenges to the aircraft composite assembly and repair (especially uncertainties associated with polymer process parameters control, barely visible defects and non-destructive inspection capability to inspect zero-thickness defects). Therefore, an industry solution owning merits on reliability and repeatability of assembly process is at high demand. Augmented Reality (AR), a human computer interaction technology, possesses its exclusive superiority on its capability of inflicting digital mock-up into physical environment. The above property of AR provides colossal opportunities to be utilised into industrial applications to contribute the realisation of automated, efficient, streamlined and reliable process and assembly. The current ongoing research aims at developing an AR System integrated into aircraft composite bonded assembly and repair as a guidance tool to instruct technicians’ repairing operation, mitigate human errors, and reduce duration of repair and assembly. Upon the accomplishment of the System, the researchers would aim to investigate the incorporation of machine learning and deep network algorithms to enable and significantly improve the interactions between the multitude of process parameters involved in the composites assembly control procedures, solely relying upon the AR geometric data. This will ultimately lead to dramatic reduction of sensors in aircraft assembly, mitigation of in-process analysis time, reduction of process and post-process inspection time, and a higher quality assembly. Stepped scarf composite repair embedded with soft composite patches was selected as the archetype to be brought into effect though hard patches were partially examined as well. The AR System has focused on composite patches assembly and vacuum bagging process to address the predicament of miscellaneous steps and fibre directions.

With the growth of the product-service system (PSS) in recent years, how to better manage the service data to improve the informed decision-making capability has become an on-going aim among the Through-life Engineering Services (TES) firms. This scenario has led managers, more and more, to turn their attention to the quality of data and information created, gathered and used within the company. Encouraged by this background, a service data quality framework has been developed aiming to provide companies with a set of methods and tools to prioritise relevant service data and assess its quality levels. The process involves four main steps that go through: (1) Mapping out important data for Through-life Support available within the company and its internal and external flows; (2) Application of a multiple criteria decision-making technique to prioritise the relevant data set considering its costs for being collected and maintained, business impact, frequency of use and ease of obtainability; (3) Quality assessment of the prioritised dataset, based on a capability maturity model; (4) Defining strategies to address data quality issues. Validation on an industrial case study demonstrates potential benefits of the process and further work opportunities.

Digital Twin (DT) is a ‘living’ entity that offers potential with monitoring and improving functionality of interconnected complex engineering systems (CESs). However, lack of approaches for adaptively connecting the existing brownfield systems and their data limits the use of DTs. This paper develops a new DT design framework that uses ontologies to enable co-evolution with the CES by capturing data in terms of variety, velocity, and volume across the asset life-cycle. The framework has been tested successfully on a helicopter gearbox demonstrator and a mobile robotic system across their life cycles, illustrating DT adaptiveness without the data architecture needing to be modified.

Industrial Product-Service Systems (IPS2) have been increasingly researched and practiced in a variety of fields and sectors. Nevertheless, such solutions are not as significantly implemented as expected by the CIRP keynote in 2010: it is not a dominant business of manufacturing companies today despite their economic and environmental advantages. One reason for this delay is assumed to be that the initial efforts were mainly on business and service when design was an afterthought. The promising digital technologies that have become easily implementable in practice will in the next years facilitate design and implementation of IPS(2 )smartly to satisfy users and contribute to sustainability. This keynote analyses literature and practice in the last decade, aiming to propose the main characteristics of IPS2 of the future and design processes adequate for IPS2. The design process of both production systems for sustainability and high-value systems for sustainable development goals may need to be supported differently than today. The list of recommendations for future research on IPS2 design is provided at the end of the paper. (C) 2022 CIRP. Published by Elsevier Ltd. All rights reserved. Accepted version uploaded on author´s home page with CC-BY-licens: https://tomohikosakao.wixsite.com/home/work-as-a-professor-in-a-university

Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional mechanism. Many recent studies on electroencephalography (EEG) have been focusing on modeling and estimating brain connectivity due to increasing evidence that it can help better understand various brain neurological conditions. However, there is a lack of a comprehensive updated review on studies of EEG‐based brain connectivity, particularly on visualization options and associated machine learning applications, aiming to translate those techniques into useful clinical tools. This article reviews EEG‐based functional and effective connectivity studies undertaken over the last few years, in terms of estimation, visualization, and applications associated with machine learning classifiers. Methods are explored and discussed from various dimensions, such as either linear or nonlinear, parametric or nonparametric, time‐based, and frequency‐based or time‐frequency‐based. Then it is followed by a novel review of brain connectivity visualization methods, grouped by Heat Map, data statistics, and Head Map, aiming to explore the variation of connectivity across different brain regions. Finally, the current challenges of related research and a roadmap for future related research are presented., This article reviews EEG‐based functional and effective connectivity studies undertaken over the last few years, in terms of estimation, visualization, and applications associated with machine learning classifiers. Methods are explored and discussed from various dimensions, such as either linear or nonlinear, parametric, or nonparametric, time‐based, frequency‐based or time‐frequency‐based. Then it is followed by a novel review of brain connectivity visualization methods, grouped by Heat Map, data statistics and Head Map, aiming to explore the variation of connectivity across different brain regions. Finally, the current challenges of related research and a roadmap for future related research are presented.

In Industry 4.0, integrated data management is an important challenge due to heterogeneity and the lack of structure of numerous existing data sources. A relevant research gap involves human knowledge integration, especially in maintenance operations. Augmented Reality (AR) can bridge this gap, but it requires improved augmented content to enable effective and efficient knowledge capture. This paper proposes dynamic authoring and hybrid recommender methods for accurate AR-based reporting. These methods aim to provide maintainers with augmented data input formats and recommended datasets for enhancing the efficiency and effectiveness of their reporting tasks. The proposed contributions have been validated through experiments and surveys in two failure diagnosis reporting scenarios. Experimental results indicated that the proposed reporting solution can reduce reporting errors by 50% and reporting time by 20% compared to alternative recommender and AR tools. Besides, survey results suggested that testers perceived the proposed reporting solution as more effective and satisfactory for reporting tasks than alternative tools. Thus, proving that the proposed methods can improve the effectiveness and efficiency of diagnosis reporting applications. Finally, this paper proposes future works towards a framework for automatic adaptive authoring in AR knowledge transfer and capture applications for human knowledge integration in the context of Industry 4.0.

Industry 5.0 is based on the idea of merging sustainable development goals and digitalization provisions from the fourth industrial revolution through human-centric solutions, bio-inspired technologies, and cyber safe data transmission. Industries are yet the most significant drivers of the integrated sustainability development (economic, environmental, and social) over the design, manufacturing, operation and disposal of products and services. This research investigates Industry 5.0 indicators that are required to achieve sustainable reliability centered maintenance (RCM) for high-value equipment. The research work examines the feasibility of a correlation between sustainable indicators between operation and maintenance phases using fuzzy logic. The fuzzy approach is implemented to measure the impact of RCM technical indicators on sustainability. A broader recommendation to improve sustainable RCM is presented through an academic survey. Cranfield University