In recent years, multiple technologies have emerged that are instrumental in driving the advancement of smart manufacturing and the Industrial IoT. These include Big Data, advanced analytics, artificial intelligence (AI), machine-learning (ML), operational intelligence, advanced robotics, cyber-physical systems, next-generation material science, and generative design for additive manufacturing.
Whilst these technologies are positively impacting the manufacturing sector, it is safe to say that the application of IoT, connected assets and the digital twin are having the most significant and immediate impact on how company’s implement Smart Manufacturing.
What is the Digital Twin?
A digital twin in manufacturing is a virtual copy of the as-designed, as-built, and as-maintained physical product; augmented by real-time process data and analytics based on accurate configurations of the physical product, production systems, or equipment.
How Are Digital Twins Used in Manufacturing?
One of the initial areas of focus for implementation of the digital twins has been asset lifecycle management (ALM). Maintaining assets in the field has traditionally been a time-consuming and costly task, though very critical to equipment and system uptime. Today, maintenance technicians can leverage technologies like augmented reality (AR) where they can access virtual engineering models and overlay these models over the physical equipment on which they are performing maintenance. Using specialised AR goggles or glasses, they use the most accurate and up-to-date engineering, ensuring that the correct maintenance and performance specifications are undertaken efficiently.
The digital twin is also being used by:
Manufacturing teams to plan out and test new production lines, meaning they can find potential problems and areas to optimize before they create the physical system, saving time and money. In the same vein, warehouse designs can be planned more easily and effectively with digital twins. Digital twin visualization techniques can make problems much more visible. They also help improve communication within the whole team.
New product testing teams, in the past, teams had to go through a lengthy trial and error process to test manufacturing a new or updated product in an existing system. With digital twins, manufacturers are able to test updated configurations while lowering the risk of costly miscalculations. Simulating many different scenarios is faster and easier than physical testing.
Maintenance teams to monitor and undertake preventative maintenance - manufacturing teams have long been collecting vital information about their machinery, such as humidity, motion, vibration, etc. with IoT connected devices and digital twins, this information can be incorporated into a comprehensive view of a system, complete with real-time data. Outliers, spikes in usage, or unexpected behaviours become easier to notice earlier on. If a problem begins to develop for a component, teams will be aware of it before it has the chance to halt production or become a hazard.
Why Digital Twins, AI, and IOT Are Essential for Manufacturing
The manufacturing sector is usually at the heart of most economies, Currently the manufacturing sector accounts for 15.0% of European GDP1 and provides about 33 million jobs. Manufacturing generates almost two-thirds of total factor growth in the EU as well as accounting for over two-thirds of EU exports, making it vital that manufacturers optimise their processes as much as possible and keep up with the latest digital advancements.
Digital twins, AI, and IoT are three technological components that work together to improve manufacturing at every level of business operations. By using these technologies together, manufacturers can dramatically increase productivity and reduce downtime, maximise the life of machinery, detect potential problems before they occur and take corrective action quickly.
The benefits of using digital twins
Accelerated risk assessment and production time
With the help of a digital twin, companies can test and validate a product before it even exists in the real world. By creating a replica of the planned production process, a digital twin enables engineers to identify any process failures before the product goes into production. Engineers can disrupt the system to synthesize unexpected scenarios, examine the system’s reaction, and identify corresponding mitigation strategies. This new capability improves risk assessment, accelerates the development of new products, and enhances the production line’s reliability.
Predictive maintenance
Since a digital twin system’s IoT sensors generate “big” data in real-time, businesses can analyse that data to proactively identify any problems within the system. This ability enables businesses to more accurately schedule predictive maintenance, and so improve production line efficiency and lower maintenance costs.
Real-time remote monitoring
It is often very difficult or even impossible to get a real-time, in-depth view of a large physical system. However, a digital twin can be accessed anywhere/anytime thus enabling users to monitor and control the physical systems performance remotel
Better team collaboration
Process automation and 24×7 access to system information allows technicians to focus more on inter-team collaboration, which leads to improved productivity and operational efficiency.
Better financial decision-making
A virtual representation of a physical object has the ability to integrate financial data, such as the cost of materials and labour. The availability of a large amount of real-time data and advanced analytics enables businesses to make better and faster decisions about whether or not adjustments to a manufacturing value chain are financially sound.
when we start connecting IoT endpoints, devices and physical assets to data sensing and gathering systems, the data extracted can be turned into valuable insights and ultimately optimise and automate processes. Consequently, the potential for digital twinning to positively impact business outcomes are almost endless. Digital twins are possible for all kinds of physical products from microchips to luxury cars. One industry that has trail blazed the use of the technology is the Formula 1 racing industry, crucial, race-winning insight has been gained from a digital twin running exactly the same race as the physical car, taking into account factors such as engine performance, road conditions, weather, and temperature.
For manufacturers, digital twins are being used to boost efficiency and productivity by enabling companies to monitor the construction of plants, manage assets, and to test final prtwins are enormous, especially when it comes to prototyping. With conventional product development, physical prototypes tend not to be built until very late in the process. Having twinned a device, a digital prototype can be used to run simulations in virtual reality that can be modified at any time at minimal cost through the entirety of the production process. This means that manufacturers are then able to not only reduce development time and costs, but to also move into the area of being able to predict failure scenarios and potential downtime—an insight that provides a significant and valuable step forward to increasing efficiencies in product development.
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