Despite technological and medical advances, amputations continue to increase. Amputees face significant challenges when acquiring and using prosthetic devices, challenges which are made worse as their emotional needs, aspirations, mobility, prosthesis requirements and problems change over time. These challenges require custom solutions for each individual amputee, a fact that current amputee centered prosthesis services tend to ignore. The work reported in this paper contributes an AI based Prosthesis Development Service Framework to cater for the current and evolving needs of amputees.

Shorter product lifecycles are also leading to even shorter planning times for the development of production systems. In most companies, the restructuring is carried out within a few weeks during the annual holidays. Digital tools such as simulations or the digital twin are used to avoid delaying the restructuring during this time. However, the introduction of a 3D model of the factory is often the first point of failure for many companies. This article proposes a six-step process model that enables the transition from 2D to 3D design. The process model was evaluated in a research project.

Additive manufacturing (AM) became a key technology in the development of innovative products. Advancements have been made to improve economic feasibility. However, ecological sustainability is still an open issue of AM. To improve sustainability, it is crucial to track, visualize, and evaluate emissions along the lifecycle. This paper presents a novel Digital Twin based approach enabling prediction of the product carbon footprint (PCF) and prescriptive measures to improve sustainability. By improving part and process design, a significant PCF reduction was achieved.

Digital Twins are perceived differently between and within industry and academia regarding applications and potentials. For this reason, a round table was formed based on the Digital Twin Workshop of the Design Conference 2022. One of the results of this round table is this contribution, which deals with a survey within the industry. The survey captured the understanding of the different roles in the creation and use of Digital Twins, the requirements and hurdles as well as the perception of methodological support. In addition, factors that influence the perception were identified.

Digital Twins (DTs) are intended to be utilized for a wide range of applications, promising benefits like visualization, monitoring, simulation and control of a physical system. Not only the development of a DT for a production facility is a time-consuming task, but also to keep the virtual counterpart up to date in the use phase. In this work, the implementation of an industrial-scale DT of an automotive supplier production site based on a Discrete-Event Simulation (DES) model with self-optimization capabilities for easier maintainability and increased simulation accuracy is presented.

Sustainability is not a new trend, but a mandatory measure for responsible and environmentally conscious use of resources. The digital transformation offers new potential in engineering and competitive advantages for companies through innovative technologies like the digital twin. Based on digital twins, products can be optimized, and new business models can be developed. Long-term added value is generated for manufacturing companies and customers. This paper explores the benefits of digital twins in the context of sustainability. Current challenges and use cases of digital twins are analysed.

This study emphasizes the importance of automatic synthetic data generation in data-driven applications, especially in the development of a 3D computer vision system for engineering contexts such as brownfield factory projects, where no data is readily available. Key points: (1) A successful integration of a synthetic data generator with the S3DIS dataset, leading to a significant enhancement in object detection of previous classes and enabling recognition of new ones; (2) A proposal for a CAD-based configurator for efficient and customizable scene reconstruction from LiDAR scanner point clouds.

This research proposes a virtual environment (VE) for co-designing in early childhood education and care settings using a social VR platform with 3D-scanned childcare rooms. Co-design workshops were analyzed focusing on perceived presence and experience and workshop outcomes. The results indicate a high level of presence in the VE, with unique advantages like facilitating 3D prototyping. However, challenges such as unbalanced prototyping tools distribution were also noted. The study highlights the potential of VEs with 3D scanned rooms in co-design.

The Digital Thread is a system that connects different phases of the product lifecycle and the related data across one or more companies in the supply chain. This work aims to develop a graph data model of the Digital Thread, in the context of the vision of polyglot persistence, that interconnects the different phases of the lifecycle and their corresponding data models, processes, and IT systems. This work proposes a Digital Thread Graph that integrates a Digital Model and a derived Digital Twin, using object and relation attributes for view creation and filtering while minimizing redundancy.

This article proposes a framework of linked software agents that continuously interact with an underlying knowledge graph to automatically assess the impacts of potential flooding events. It builds on the idea of connected digital twins based on the World Avatar dynamic knowledge graph to create a semantically rich asset of data, knowledge, and computational capabilities accessible to humans, applications, and artificial intelligence. We develop three new ontologies to describe and link environmental measurements and their respective reporting stations, flood events, and their potential impact on population and built infrastructure as well as the built environment of a city itself. These coupled ontologies are deployed to dynamically instantiate near real-time data from multiple fragmented sources into the World Avatar. Sequences of autonomous agents connected via the derived information framework automatically assess consequences of newly instantiated data, such as newly raised flood warnings, and cascade respective updates through the graph to ensure up-to-date insights into the number of people and building stock value at risk. Although we showcase the strength of this technology in the context of flooding, our findings suggest that this system-of-systems approach is a promising solution to build holistic digital twins for various other contexts and use cases to support truly interoperable and smart cities.

This paper proposes a virtual reality-based dual-mode teleoperation architecture to assist human operators in remotely operating robotic manipulation systems in a safe and flexible way. The architecture, implemented via a finite state machine, enables the operator to switch between two operational modes: the Approach mode, where the operator indirectly controls the robotic system by specifying its target configuration via the immersive virtual reality (VR) interface, and the Telemanip mode, where the operator directly controls the robot end-effector motion via input devices. The two independent control modes have been tested along the task of reaching a glass on a table by a sample population of 18 participants. Two working groups have been considered to distinguish users with previous experience with VR technologies from the novices. The results of the user study presented in this work show the potential of the proposed architecture in terms of usability, both physical and mental workload, and user satisfaction. Finally, a statistical analysis showed no significant differences along these three metrics between the two considered groups demonstrating ease of use of the proposed architecture by both people with and with no previous experience in VR.

Sensed data from high-value engineering systems is being increasingly exploited to optimise their operation and maintenance. In aerospace, returning all measured data to a central repository is prohibitively expensive, often causing useful, high-value data to be discarded. The ability to detect, prioritise and return useful data on asset and in real-time is vital to move toward more sustainable maintenance activities.

We present a data-driven solution for on-line detection and prioritisation of anomalous data that is centrally processed and used to update individualised digital twins (DT) distributed onto remote machines. The DT is embodied as a convolutional neural network (CNN) optimised for real-time execution on a resource constrained gas turbine monitoring computer. The CNN generates a state prediction with uncertainty, which is used as a metric to select informative data for transfer to a remote fleet monitoring system. The received data is screened for faults before updating the weights on the CNN, which are synchronised between real and virtual asset.

Results show the successful detection of a known in-flight engine fault and the collection of data related to high novelty pre-cursor events that were previously unrecognised. We demonstrate that data related to novel operation are also identified for transfer to the fleet monitoring system, allowing model improvement by retraining. In addition to these industrial dataset results, reproducible examples are provided for a public domain NASA dataset.

The data prioritisation solution is capable of running in real-time on production-standard low-power embedded hardware and is deployed on the Rolls-Royce Pearl 15 engines.

In order to clarify and visualize the real state of the structural performances of ships in operation and establish a more optimal, data-driven framework for ship design, construction and operation, an industry-academia joint R&D project on the digital twin for ship structures (DTSS) was conducted in Japan. This paper presents the major achievements of the project. The DTSS aims to grasp the stress responses over the whole ship structure in waves by data assimilation that merges hull monitoring and numerical simulation. Three data assimilation methods, namely, the wave spectrum method, Kalman filter method, and inverse finite element method were used, and their effectiveness was examined through model and full-scale ship measurements. Methods for predicting short-term extreme responses and long-term cumulative fatigue damage were developed for navigation and maintenance support using statistical approaches. In comparison with conventional approaches, response predictions were significantly improved by DTSS using real response data in encountered waves. Utilization scenarios for DTSS in the maritime industry were presented from the viewpoints of navigation support, maintenance support, rule improvement, and product value improvement, together with future research needs for implementation in the maritime industry.

We present the design and deployment of a capsule endoscope via external electromagnets for locomotion in large volumes alongside its digital twin implementation based on interval type-2 fuzzy logic systems (IT2-FLSs). To perform locomotion, we developed an external mechanism comprising five external electromagnets on a two-dimensional translational platform that is to be placed underneath the patients’ bed and integrated multiple Neodymium magnets into the capsule. The interaction between the central bottom external electromagnet and the internal magnet forms a fixed body frame at the capsule center, allowing rotation. The interaction between the external electromagnets and the two internal magnets results in rotation. The elevation of the capsule is accomplished due to the interaction between the upper external electromagnet and the internal magnets. Through simulations, we model the capsule rotation as a function of torque and drive voltages. We validated the proposed locomotion approach experimentally and observed that the results are highly nonlinear and uncertain. Thus, we define a regression problem in which IT2-FLSs, capable of representing nonlinearity and uncertainty, are learned. To verify the proposed locomotion approach and test the IT2-FLS, we leverage our experimental effort to a stomach phantom and finally to an ex vivo bovine stomach. The experimental results validate the locomotion capability and show that the IT2-FLS can capture uncertainties while resulting in satisfactory prediction performance. To showcase the benefit in a clinical scenario, we present a digital twin implementation of the proposed approach in a virtual environment that can link physical and virtual worlds in real time.

A digital twin is the mapping of a physical twin between hybrid spaces. The lifecycle of digital and physical twins occurs through the concepts of foetal, child, and adult twins. This technology can be used to assist clients and designers with real-time data. The use of digital twin technology in architectural design can be realised at various stages, from design to operation. Designers will be able to gain knowledge of the past, present, and future using this technology. This will reveal possible design scenarios. In this study, a hypothetical scenario is designed, in which designers build a building while already having a digital twin template. To do this, Building Information Modelling (BIM) is used as a reference model for digital twins, along with the fidelity levels of digital twins and the level of detail-development of BIM. When designers want to design a new project related to their predecessors, they already use the same type of digital twin-building portfolio they can use for their new design. A digital twin will help optimise the new process. Therefore, the digital twin of a building with a similar building type can be used to extract relevant data for the design process.

In order to have a sustainable disassembly process, a successful decision-making based on reliable and up-to-date information should be made while taking into consideration sustainability indicators. In this context, The aim of this paper is to introduce a decision support system based on knowledge based and digital twin in order to help stakeholders to choose the most sustainable disassembly scenario .In this research, firstly, we presented the state of art of disassembly process, digital twin, knowledge based system and the merging of knowledge based system and digital twin for disassembly. Secondly, we presented the knowledge based digital twin (KBDTw) system framework for a sustainable disassembly process. Thirdly, a case study is presented about the use of KBDTw in the end-of-life of internet boxes. Finally, a conclusion and future work are conducted.

Digital Twins are one of the most trending topics. However, there are still open issues in the development of Digital Twins. One of these impediments is formulating a clear and valid value proposition of a Digital Twin. Therefore, this paper presents a novel business modelling approach for Digital Twins. Based on a literature review and an academic case study, different business modelling approaches and techniques were systematically compared, selected and adjusted in order to develop a new framework suitable for describing the value of Digital Twins. It consists of 10 steps – (1) describe a basic development concept of the DT, (2) identify the customer segments, needs and pain points, (3) derive a value proposition (4) identify revenue streams and values created, (5) identify key activities, (6) resources needed, and (7) necessary partners. (8) Adapt the organization to fit the business model and (9) evaluate the cost structure of the project. In the end, document and communicate the new business model (10). Each step is supported by auxiliary methods, tools and procedures. The approach was applied to a scientific case study. In an initial evaluation the overall applicability and usefulness of the approach were confirmed.

Early detection and correction of defects are critical in additive manufacturing (AM) to avoid build failures. In this paper, we present a multisensor fusion-based digital twin for in-situ quality monitoring and defect correction in a robotic laser-directed energy deposition process. Multisensor fusion sources consist of an acoustic sensor, an infrared thermal camera, a coaxial vision camera, and a laser line scanner. The key novelty and contribution of this work are to develop a spatiotemporal data fusion method that synchronizes and registers the multisensor features within the part's 3D volume. The fused dataset can be used to predict location-specific quality using machine learning. On-the-fly identification of regions requiring material addition or removal is feasible. Robot toolpath and auto-tuned process parameters are generated for defect correction. In contrast to traditional single-sensor-based monitoring, multisensor fusion allows for a more in-depth understanding of underlying process physics, such as pore formation and laser-material interactions. The proposed methods pave the way for self-adaptation AM with higher efficiency, less waste, and cleaner production.

Using the case study of a smart green blue roof pilot project in Greater Manchester, the authors have coded and analysed a series of semi structured interviews. We present our findings which are specifically focused on how 'smart' was understood by the multiple stakeholders involved in the project and how product complexity is managed when digital technologies are integrated into nature based solutions that are becoming more popularised in the construction sector. This integration of digital twin and sensor technologies with physical drainage products to create the next generation of smart green blue infrastructure, presents numerous challenges for organisations in the construction sector. We conclude that the need for smart systems of systems in this sector necessitates organisational change and new methods of knowledge transfer across organisations who work together to deliver the holistic physical and digital services to the client.