Engineers and manufacturers have for years used various tools such as CAD/CAM software to model products or systems they design or manufacture, but more recently new technologies are making these models come alive through simulation.
Immersive disease state simulations allow doctors and HCPs to experience what their patient may feel like.
What is a Digital Twin?
Digital twins are easy to grasp: they’re software models that replicate an object, process, organization or individual in the real world. Their complexity depends on how closely they reflect its original form – all the way down to physical properties like materials.
Sensors allow a digital twin to track its physical counterpart over time, providing useful data on performance and failure predictions, optimizing processes, and helping companies realize business outcomes.
BMW employs digital twins to monitor and optimize its factories’ workflows for material distribution, robot applications and other factory floor challenges. This enables the company to test new products or production scenarios before being implemented on the plant floor, thus shortening design cycle times and increasing manufacturing efficiency.
Industry examples include digital twins of products, which combine CAD models with operational data to enable rapid validation and adjustments, eliminating multiple prototypes while shortening product development time while quickly responding to customer feedback. GE Digital’s solution, for instance, enables industrial companies to construct digital twins of components (such as pumps or compressors), systems of assets (like an entire power plant), or even massive containers carrying most world commerce.
Digital twin technology is rapidly being adopted across industries to increase revenues, enhance training programs, reduce machinery downtime, foster better remote collaboration, train autonomous devices and more. Passive Logic, for instance, has developed an AI platform that engineers and autonomously operates IoT components of buildings right down to their physical structure; using machine learning it also learns improvements on how the building operates over time.
What are the Benefits of a Digital Twin?
Digital twins offer numerous benefits across the product lifecycle. These technologies are particularly beneficial in complex or dynamic environments that could benefit from real-time optimization, such as manufacturers using them to validate new product performance prior to entering production, saving both resources and time – not to mention potentially costly mistakes and more informed design decisions!
Engineers can utilize digital twins to monitor product performance in its intended environment and gain insights that could enable them to optimize its original design. For instance, sensors in an airplane engine provide feedback about wear-and-tear conditions which allows engineers to redesign components before any problems arise, improving quality while decreasing flight delays.
Digital twins also present another important advantage in terms of employee development and training: they allow companies to train employees without subjecting them to potentially hazardous working environments. For instance, remote employees could use one to familiarize themselves with a factory floor layout before working with actual machinery.
Digital twins can help companies develop more flexible and efficient business models. For instance, manufacturers can use their digital twin to simulate warehouse or store layout for improved customer distribution and foot traffic patterns, helping reduce inventory costs while simultaneously increasing productivity and customer satisfaction.
How Can a Digital Twin Help You?
Digital Twin technology serves as a virtual bridge between physical products or systems and the digital realm. Sensors installed into physical products or systems collect real-time data that is then fed into a cloud-based system where it is then analyzed against business and contextual information to provide actionable insight that can transform your business.
Digital Twins can dramatically cut development time and costs. Engineers can simulate how a product will behave under various environmental scenarios (even impossible ones) before physically producing it, making changes before manufacturing begins. Furthermore, engineers can use virtual environments for testing prototypes of products which saves resources by eliminating the need to construct and tear down real versions of it.
Digital twins can assist with maintenance operations by tracking machine performance and anticipating its demise and pinpointing its cause, helping prevent unscheduled downtime and costly repairs as well as meeting production goals and customer service goals.
To fulfill its functions effectively, digital twins require an excellent data infrastructure that includes engineering capabilities to organize data before it’s stored, analyzed and modeled within their digital twin. Furthermore, compute power is also required when twinning more complex entities like aircraft engines or manufacturing plants.
What is the Future of Simulations?
Digital twins hold immense promise to revolutionize how businesses do business by speeding up holistic understanding, effective decision-making and efficient action. They are particularly advantageous to companies managing complex physical assets or processes or producing highly technical products; helping people better comprehend equipment or systems more, identify failure risks or inefficiency sources as well as maintenance concerns more easily, identify opportunities for process efficiencies or streamlining opportunities, as well as creating awareness around potential failures or maintenance concerns.
Systematically capturing, storing, processing and delivering data at the required level requires substantial upfront investments and ongoing maintenance costs; they typically are deployed for processes and products with multiple moving parts that necessitate multiple sensors to capture accurate measurements.
Simulation models are particularly helpful when it comes to evaluating the impacts of planned change projects such as new procedures, equipment or machinery being introduced; or when trying out changes before making them in reality. They allow one to save costs and avoid mistakes that can otherwise prove expensive.
Digital twins offer businesses another key use case in data-driven descriptive, diagnostic and predictive analytics. Accurate model inferences using historical and real-time data can quickly reveal any environmental factors or quality issues impacting target parameters or create what-if scenarios using trained models to determine an ideal intervention strategy.