parallax background

Typical Recent Project Results

SMART MANUFACTURING PROJECT RESULTS

Digital Twin Technology in Smart Manufacturing

Digital Twin technology is an emerging field that enables the creation of digital representations of real-world entities (e.g., a car, a building, a bridge, or a jet engine) and keeps track of the state and behaviour of those entities during their entire lifespan. A Digital Twin (DT) is a tool to help engineers and operators understand not only how products are performing before they are constructed but also how they will perform in the future once operational. To achieve this, they use sensors connected to the physical asset which collect data that can be mapped onto the digital (virtual) model. Analysing the data from the connected sensors combined with other sources of information, e.g., product characteristics, materials, performance data, emission and pollution levels, etc, allows product designers to make these predictions. With the creation of the Digital Twin, companies may realize significant value in the areas of speed to market with a new product, improved operations, reduced defects, and emerging new business models to drive revenue.

ServTech personnel developed a framework comprising machine-processable knowledge structures that convey manufacturing knowledge. These turn conventional products into self-describing products by storing, linking, combining, and analyzing the raw data collected for a product throughout its lifecycle and the processes that produce it. They represent, manage, inter-link and process product data and information (both its content and context), product portfolios and product families, manufacturing assets (personnel, plant machinery and facilities, production line equipment), production schedules, and, in general, help meet the requirements (functional, performance, quality, cost, time, etc.) of an entire manufacturing plant or network. This information can be put within a broader operational context, providing the basis for a complete digital footprint of the entire manufacturing process - spanning product, production, and performance - so that product designers and engineers can make informed choices about materials and processes using 3D visualization tools during the design stages of a digital product and immediately see the impact that it would have on a physical version of the product. This capability of digital products can be extended across multiple factories.

Design of Customised/Differentiated Products

Until recently, the process of product development - a series of steps that includes the conceptualization and design of a newly created or newly rebranded product, e.g., automotive engine parts or aircraft engine parts, auxiliary equipment, etc - has been painfully iterative and rigid. Analyses of industrial product development projects have shown that only 52% of the originally allocated requirements appear in the final released version of the product. Traditionally, product analysts and engineers would receive customer requirements, create a set of design concepts, experiment with product structure and materials, test designs to determine how they held up in realistic conditions, and tweak them until they got the design that met specifications. This very often results in a large part of the design simply unravelling, necessitating new analyses and production experiments.

To alleviate these problems ServTech staff have developed a novel Product-oriented Configuration Language (PoCL) for customizing digital products by combining the power of the Digital Twin approach with support for customized products, improved processes and empowering human operators. PoCL is a user-friendly graphical language that supports product conceptualization by creating Digital Twin replicas of different types of complex customized innovative products by supporting collaboration between product analysts and customers while checking the consistency and satisfaction of customer requirements in-line with the customization process. This language employs easily combinable graphical representations of 3D product shapes and combines product quality characteristics with product design artefacts producing a series of interconnected Digital Twin knowledge structures that can be used for eventual product construction.

Design of Customised/Differentiated Products

Industry 4.0 sets the foundations for completely connected factories that are characterized by the digitization and interconnection of supply chains, production equipment and lines, and the application of the latest advanced digital technologies to manufacturing activities. Industry 4.0 can be perceived as the rapid transformation of industry, where the virtual world of digital technology, the physical world of machines, and the Internet meet. Paired with powerful tools, such as visualization, scenario analysis, and predictive learning algorithms, Industry 4.0 is fundamentally changing how manufacturers operate. Yet most companies are limited by their tactics thus far: investing in a collection of point solutions that work well for individual processes but do not talk to each other or integrate data. As a result, stakeholders often have little, if any, visibility into other processes, which limits their ability to react or adjust their activities and many aspects of the production process, including design, production, and supply, remain widely fragmented.

ServTech staff developed an approach for manufacturing companies to integrate the entire end-to-end supply chain so that they can collaborate and run the majority of processes and decisions through autonomous planning whereby demand can be automatically factored into all processes and decisions along the chain, production planning and scheduling. ServTech designed a federated IT platform supporting the Industry 4.0 shift from linear, sequential supply chain operations to an interconnected, open system of supply operations which lays the foundation for how manufacturers and suppliers can collaborate to produce a range of innovative digital products. Digital Twins are used in a collaborative manufacturing network during the development of a product or when planning production. They make the development process more efficient, improve quality and help to share information between stakeholders. By combining Digital Twins of a product and the production line, new production processes can be virtually tested and optimized before any physical work can start. In addition, when Digital Twin information is shared with partners, they are better able to optimize and align their processes.

Inter-connected Digital Manufacturing Ecosystem

Industry 4.0 sets the foundations for completely connected factories that are characterized by the digitization and interconnection of supply chains, production equipment and lines, and the application of the latest advanced digital technologies to manufacturing activities. Industry 4.0 can be perceived as the rapid transformation of industry, where the virtual world of digital technology, the physical world of machines, and the Internet meet. Paired with powerful tools, such as visualization, scenario analysis, and predictive learning algorithms, Industry 4.0 is fundamentally changing how manufacturers operate. Yet most companies are limited by their tactics thus far: investing in a collection of point solutions that work well for individual processes but do not talk to each other or integrate data. As a result, stakeholders often have little, if any, visibility into other processes, which limits their ability to react or adjust their activities and many aspects of the production process, including design, production, and supply, remain widely fragmented.

ServTech staff developed an approach for manufacturing companies to integrate the entire end-to-end supply chain so that they can collaborate and run the majority of processes and decisions through autonomous planning whereby demand can be automatically factored into all processes and decisions along the chain, production planning and scheduling. ServTech designed a federated IT platform supporting the Industry 4.0 shift from linear, sequential supply chain operations to an interconnected, open system of supply operations which lays the foundation for how manufacturers and suppliers can collaborate to produce a range of innovative digital products. Digital Twins are used in a collaborative manufacturing network during the development of a product or when planning production. They make the development process more efficient, improve quality and help to share information between stakeholders. By combining Digital Twins of a product and the production line, new production processes can be virtually tested and optimized before any physical work can start. In addition, when Digital Twin information is shared with partners, they are better able to optimize and align their processes.

SMART HEALTHCARE PROJECT RESULTS

Digital Twin Technology in Smart Healthcare

One of the areas that can benefit from the application of Digital Twin technology in healthcare is chronic disease management, such as cardiovascular diseases, diabetes, chronic respiratory diseases, arthritis, and Alzheimer’s, which account for almost three-quarters of all deaths worldwide. A real challenge to delivering optimal care for chronic conditions lies in the need to transition from an episodic to a continuous monitoring cycle of the patient’s physiological data and the need to develop collaborative relationships with patients. This can be achieved by sustaining vigilant monitoring of health indicators to optimize treatment and an operational shift that gives patients greater control and personalization over how they manage their condition.

ServTech staff and partners employ the DT paradigm for personalized chronic disease prevention by recording, aligning and analyzing masses of an individual’s health data using a Personal Medical Digital Twin to help medical professionals to better predict a specific patient’s (long-term) response, including side effects, recommending a personalized treatment plan, providing therapy guidance, and preventing deterioration. The data that will provide the information used to represent an individual’s Personal Medical Digital Twin is gathered from a plethora of health sources, including medical sources with patient-specific data, electronic health records (EHRs), vital signs, a patient’s medical history (diagnosis and prescriptions), medical and clinical data, symptoms, medical tests, medication, psychological and psychosocial data, nutritional data, patient-generated data using sensors, and so on. These data are used by various entities, including hospitals, Community Health Centers, laboratories, physicians, and health plans, and are represented in different formats. A Personal Medical Digital Twin can then pull together and codify such rich data and can also continuously pull real-time sensor (IoT) data to create accurate snapshots of the physical patient’s current state. This information can be integrated with historical data and predictive analytics to inform healthcare professionals - who may be remotely located - of potential issues and suggest solutions vetted and approved by healthcare professionals.

Improving the Quality of Life of Cancer Patients via the use of a Smart Platform & Digital Twins

Cancer immunotherapy has revolutionized the field of oncology over the last two decades. By relying on the stimulation of the immune system to recognize and attack cancer cells, immunotherapy has a different mode of action than existing anti-cancer treatments, which makes it an important therapeutic strategy in the arsenal of anti-cancer drugs. Although immunotherapies are successful in employing the immune system to attack cancer cells, the activation of the immune system also causes toxicities that are unique to this type of anti-cancer treatment. Given that these adverse events are expected to be the result of the immune system attacking healthy cells, they are often referred to as immune-related adverse events (irAEs). Treatment with immunotherapy is expected to have a psychosocial benefit for the patient. In comparison to conventional therapies, various trials reported smaller impairments in health-related Quality of Life scales, longer time until deterioration of Quality of Life, and better control of cancer symptoms after immunotherapy. However, since irAEs can have a late onset and be long-lasting, both short- and long-term deterioration of Quality of Life may still occur.

ServTech staff and partners aim to take on those challenges by studying patients’ irAEs and Quality of Life and its determinants in a multi-hospital setting, and to incorporate the collected data and study outcomes into a Smart Digital Platform employing personalized Digital Twins specific for immunotherapy patients. This Smart Platform will use AI tools to share and exchange trusted and secure medical data - with innovative AI-driven personalized risk prediction, prevention and intervention models that facilitate preventive care. It will enable better provision of information to various stakeholders including patients and health care professionals and will provide information on a patient’s risk profile for irAE development or Quality of Life deterioration, increase awareness, outline strategies, and offer guidance on the recommended management of immune-related adverse events in patients treated with immune checkpoint inhibitor therapy. Overall, with the support of Personalised Medical Digital Twins, the Smart Platform would act as an intelligent personal “health assistant” to:

  • provide a personalized experience in which patients can ask questions and learn how to better manage their own health;
  • understand disease progression in real-time and characterize the status and health condition of each individual;
  • deliver active self-management support, personalized predictions and recommendations to strengthen prevention and improve outcomes for individuals in collaboration with healthcare professionals.

OTHER APPLICATION AREAS

The DT approach applied in smart manufacturing and smart healthcare is generic and can also be applied in other areas. Currently, ServTech staff is experimenting with the application of DT technology in:

  • Smart Cities
  • Smart Agriculture, and eventually
  • Green Energy & Climate Change.