Imagine being able to create an exact digital replica of a manufacturing operation, machine, or product and then use it to optimize and improve the physical counterpart in real time. Sounds like something out of a science fiction movie, right? Well, this technology exists, and it’s called a digital twin.
Digital twins are revolutionizing the manufacturing industry by providing immense visibility into the production process. By creating virtual replicas of physical assets, manufacturers can test different scenarios, predict failures, and make adjustments in real time. The result? Increased efficiency, reduced costs, and improved product quality.
In this blog post, we’ll explain the meaning of digital twins in manufacturing, exploring their benefits and the exciting future they offer for the industry. So, strap in and get ready to learn how this cutting-edge technology is transforming the manufacturing industry.
What are Digital Twins in Manufacturing?
Digital twins in manufacturing refer to virtual replicas of physical machines, products, or processes. It replicates the entire production process, which includes machinery, equipment, and raw materials, and provides the dynamics of how an object functions in the real world.
A digital twin can also be the design model for a future asset, which can be updated and maintained to reflect the as-built state of the asset during the construction and operational phases of its lifecycle.
“Digital twins are a dynamic software model of a physical thing or system (Gartner)”
Manufacturers use digital twins to simulate different scenarios and test potential improvements, which can help reduce costs, improve efficiency, and increase productivity. By analyzing data from the digital twin, manufacturers can make informed decisions about changes to the physical object or process.
For example, a digital twin of a machine tool could be used to simulate different machining strategies, allowing manufacturers to identify the most efficient approach before making changes to the physical machine. Similarly, a digital twin of a production line could be used to identify bottlenecks and optimize the flow of materials and products.
Overall, digital twins are a powerful tool for manufacturers looking to improve their operations, reduce costs, and increase productivity in a rapidly changing digital landscape.
Digital Twin Market Highlights
Benefits of Digital Twins in Manufacturing
Digital twin technologies offer numerous advantages, and a few of them are listed below:
Improved reliability of equipment and production lines : Digital twins enable real-time monitoring and analysis of equipment and production processes, allowing for early detection of faults or anomalies. This proactive approach helps in preventing breakdowns, minimizing unplanned downtime, and ensuring reliable operation.
Enhanced productivity : Digital twins provide insights into process optimization and efficiency improvements. By analyzing data from the twin and simulating different scenarios, manufacturers can identify bottlenecks, optimize workflows, and make informed decisions to enhance overall productivity.
Enhanced Overall Equipment Effectiveness (OEE) by reducing downtime and improving performance : Digital twins enable predictive maintenance by monitoring equipment health in real-time. By identifying potential issues in advance, manufacturers can schedule maintenance activities during planned downtime, minimizing disruptions and maximizing equipment uptime, leading to improved OEE.
Lower maintenance costs : With digital twins, maintenance activities can be optimized based on real-time data, leading to a reduction in unplanned maintenance and unnecessary preventive maintenance. This approach helps to lower maintenance costs by ensuring that maintenance tasks are performed when required, avoiding unnecessary expenses.
Better workforce training : Digital twins can be used as virtual training environments where employees can learn and practice operating machinery and equipment in a safe and controlled setting. This enables better training and upskilling of the workforce, leading to improved operational efficiency and reduced errors.
Improved supply chain management : Digital twins provide visibility into the entire supply chain, enabling better planning, coordination, and optimization. Manufacturers can simulate different supply chain scenarios, identify potential bottlenecks or risks, and make informed decisions to optimize inventory levels, reduce lead times, and enhance overall supply chain performance.
Save valuable time : Digital twins allow for faster and more efficient troubleshooting and problem-solving. By simulating different scenarios and analyzing real-time data, manufacturers can quickly identify the root causes of issues and implement corrective actions, saving valuable time in diagnosing and resolving problems.
What industry use digital twin for manufacturing?
Digital twins are widely adopted in various industries for manufacturing purposes, offering immense value and benefits. Some of the key industries that utilize digital twinning in manufacturing include:
Automotive Industry : Digital twins are used for virtual prototyping, simulation, and optimization of production processes in automotive manufacturing, improving assembly lines and production efficiency.
Aerospace and Defense : Digital twins enhance manufacturing and maintenance of complex aircraft components, providing real-time monitoring, predictive maintenance, and optimization of production workflows for safety, reliability, and cost-effectiveness.
Electronics and Semiconductor : Digital twins optimize production processes for electronic components, devices, and integrated circuits, improving yield, reducing defects, and streamlining manufacturing operations.
Energy and Utilities : Digital twins optimize operations of power plants, oil refineries, and renewable energy installations through real-time monitoring, predictive maintenance, and simulation, ensuring improved efficiency, reliability, and safety.
Pharmaceutical and Healthcare : Digital twins optimize drug development, formulation, and production processes in the pharmaceutical industry, aiding in batch process optimization, critical parameter monitoring, and regulatory compliance.
Consumer Goods : Digital twins are used in the consumer goods industry for product design, quality control, and process optimization, delivering better products and improving operational efficiency in manufacturing.e
Digital Twins in Manufacturing Examples
As said, digital twins are virtual replicas of physical assets, systems, and processes that can be used in various industries, including manufacturing. Here are some examples of digital twins in manufacturing:
Bridgestone’s Digital Twin Technology Drives an Innovative Business Model and Improves Performance
Bridgestone, the global leader in tire and rubber manufacturing, is transforming to become a leader in mobility solutions. The company regularly uses digital twin technology to understand the impact of various factors, such as driving style, speed, to improve tire life and road conditions on performance and durability. With these insights, the company can advise its operators on preventing breakdowns and increasing the wheels’ lifespan.
Bridgestone has reaped several benefits from using digital twin technology in its tire manufacturing processes. These benefits include:
Improved Tire Design : By using digital twins to simulate tire designs, Bridgestone can optimize the design for better performance and longevity, resulting in higher-quality products.
Reduced Development Time : Bridgestone can design and test new tires more quickly, reducing the time it takes to bring new products to market. According to Bridgestone, this approach cuts development time by 50%.
Enhanced Production Efficiency : Digital twins help Bridgestone optimize its tire manufacturing processes, reducing downtime and improving production efficiency.
Better Predictive Maintenance : By monitoring equipment data in real time, the digital twin can predict equipment failures before they occur, reducing downtime and maintenance costs.
Increased Sustainability : Digital twins can identify areas where waste can be reduced, leading to a more sustainable manufacturing process.
Power Generation Used Digital Twins to Predict the Performance of Gas Turbines
Siemens acquired Rolls-Royce’s energy gas turbine and compressor business and based on the acquired asset, introduced the SGT-A65 aero-derivative gas turbine. This posed new challenges for production and maintenance, including unforeseen issues. Siemens’ Excel-based forecasting tools were insufficient due to the high volume of data and lack of clear results. The business required a more powerful method to predict performance, forecast KPIs, and evaluate investment options. Siemens wanted to visualize the entire production and maintenance process, including critical supply-chain logistics. They needed the ability to run multiple scenarios to communicate investment options and improve decision-making.
DecisionLab and Siemens presented ATOM, a digital twin solution to overcome challenges.
ATOM uses real-time data from various sources to create a comprehensive model of engine parameters, performance metrics, maintenance operations, and logistics processes throughout the entire turbine lifecycle.
Some of the benefits of using digital twins for gas turbines include:
Simulate different scenarios and present their outcomes in a visual format.
ATOM assists in making informed investment decisions.
Captures and predicts system KPIs
Uses Cases of Digital Twins for Manufacturing
Digital twins have numerous use cases in manufacturing, such as:
Improving System Designs
Manufacturing teams use digital twins to plan and test new production lines. This means, before creating a physical system, potential issues, and optimization areas can be identified, resulting in a significant saving of both time and money. Similarly, the planning of warehouse designs can be done through digital twins in a more effective way. The utilization of digital twin visualization techniques has made issues much more conspicuous, thereby enhancing team communication and efficiency.
Testing New Products
Gone are the days when frontline workers had to undergo a tedious trial-and-error process to test new or updated products within an existing system. The advent of digital twins has made it possible for manufacturers to test out updated configurations with a significantly reduced risk of incurring costly miscalculations. The simulation of multiple scenarios has become faster, easier, and more efficient than physical testing.
Equipment Monitoring and Preventive Maintenance
Digital twins of machines offer a precise virtual equivalent in real-time, providing maintenance staff with a detailed breakdown of equipment performance and health. With the help of AI, digital twins can identify potential failures before they occur, enabling manufacturers to take proactive measures.
Field technicians can use augmented reality smart glasses to view accurate models overlaid on the physical machines on the factory floor. This provides precise specifications and a visual representation that the technician can rely on to carry out their work.
Digital twins can be used to train employees and simulate real-world scenarios, allowing them to gain experience and knowledge without the risk of physical harm or damage. For example, by creating a digital twin of a production line, employees can learn how to operate equipment, troubleshoot problems, and improve efficiency. This can improve safety, reduce training costs, and enhance workers’ productivity.
To ensure top-notch quality and reduce rework, it’s crucial to continuously monitor and respond to data during production. The digital twin can model every aspect of the production process to pinpoint any inconsistencies or identify areas where better materials or processes could be utilized.
How do you build Digital Twins in Manufacturing?
For creating a virtual replica, digital twins require a real-time flow of data from physical objects, systems, or processes that are scanned in the real world. This data is then fed into IoT systems and used to develop computational analytical models. The resulting models are displayed through 3D representations and AR techniques to simulate real-world conditions and visualize outcomes on any device, including smartphones, computers, and AR/VR devices. AR overlays the digital twin onto a real-world view, allowing frontline workers to see how the virtual model would interact with the physical world. This can be especially useful for testing and prototyping, as well as for training purposes.
Usually, digital twin deployment begins on a small scale by focusing on the performance of a single component within a product, but it eventually grows and changes over time. Yet this occurs in two different ways. Firstly, a few digital twins are combined to provide a comprehensive view of the asset, process, or system. Secondly, more advanced capabilities are added to the existing digital twin to predict the future performance of its physical counterpart.
Tool Needed to Develop Digital Twins in Manufacturing
Developing and manufacturing digital twin requires the use of various tools and technologies that enable the creation, simulation, and monitoring of virtual replicas. Some of the key tools needed for developing digital twins include:
Simulation Software : Tools like Ansys Twin Builder, Siemens Simcenter, and Dassault Systemes’ SIMULIA provide powerful simulation capabilities for creating virtual replicas and simulating the behavior of physical systems in digital twins.
IoT Sensors and Devices : Various sensor manufacturers offer IoT devices, such as temperature sensors from Texas Instruments, pressure sensors from Honeywell, and vibration sensors from TE Connectivity. These sensors collect real-time data from physical assets and transmit it to the digital twin.
Cloud Computing : Cloud platforms like Microsoft Azure, Amazon Web Services (AWS), and Google Cloud provide scalable infrastructure and services for storing, processing, and analyzing data generated by digital twins. They offer resources like virtual machines, data storage, and analytics tools.
Data Analytics and Machine Learning : Tools such as MATLAB, Python’s pandas and scikit-learn libraries, and Microsoft Azure Machine Learning enable data analysis and machine learning techniques to extract insights from digital twin data, detect patterns, and predict future behavior.
Visualization and User Interfaces : Visualization tools like Tableau, Power BI, and Plotly help create interactive dashboards and visual representations of digital twin data. User interface frameworks like React and Angular can be used to develop intuitive interfaces for interacting with the digital twin model.
Integration and Connectivity : Integration tools such as MuleSoft, Microsoft Azure Logic Apps, and Apache Kafka enable seamless connectivity and data exchange between the digital twin and other systems or databases, ensuring interoperability and synchronization.
Unlock The Power of Digital Twin Technology with Plutomen Assist
As manufacturing becomes increasingly complex and competitive, the use of AR and digital twin technology can give manufacturers a competitive advantage in the marketplace. AR and Digital Twin technologies play a crucial role in smart manufacturing. By visualizing Digital Twin data through AR technology, it is possible to create a more intuitive and comprehensive integration of the physical and virtual aspects of Digital Twin.
Digital twins in manufacturing can help to enhance their performance and ensure the safety of their operations by enabling operator training and advanced testing of equipment and processes through dynamic simulation. And for all this, Plutomen Assist is a one-stop solution for the manufacturing industry.
From maintenance and repair to training and simulation, we can help you integrate these technologies and ultimately improve decision-making and reduce costs.
Plutomen Assist is the most dynamic Augmented Reality Training software, which enables industry experts to develop their own training model. At Plutomen, each 3D model is highly detailed, which helps in better knowledge transfer and comprehension. Moreover, by simply scanning the barcode or QR code on a machine, businesses can quickly and easily access training materials and guides linked to specific machines or parts. This innovative solution enables frontline workers to easily access critical training materials even in complex and emergency situations.
Plutomen’s approach has always been consistent – providing the most effective solution that is agile, flexible, and scalable. It doesn’t matter what industry you’re in or what product or system you want to model; Plutomen can handle it all.
So, are you ready to take your manufacturing operations to the next level with the power of Augmented Reality and digital twin technology? Don’t wait any longer – book a meeting with one of our experts today to learn more about how our solutions can help your business optimize production, reduce downtime, and improve efficiency. Get an AR solution with us and revolutionize the way you do business!
A digital twin in the context of manufacturing is a virtual replica of a physical machine, product, or process that enables real-time monitoring, simulation, and optimization to improve performance and efficiency.
Various manufacturing processes can benefit from digital twins, including automotive assembly lines, aerospace component manufacturing, electronics production, power plant operations, pharmaceutical manufacturing, and consumer goods production.
A digital twin helps in optimizing production and improving efficiency by providing insights into process optimization, identifying bottlenecks, simulating different scenarios, enabling predictive maintenance, enhancing supply chain management, and facilitating workforce training.
Technologies used to create and maintain digital twins include simulation software (such as Ansys Twin Builder and Siemens Simcenter), IoT sensors and devices, cloud computing platforms (like Microsoft Azure and AWS), data analytics and machine learning tools (such as MATLAB and Python libraries), visualization tools (like Tableau and Power BI), and integration and connectivity tools (such as MuleSoft and Apache Kafka).
Digital twins are not limited to large manufacturing companies. Smaller businesses can also benefit from them by leveraging cloud-based solutions and utilizing affordable IoT sensors and simulation software. The scalability and flexibility of digital twin technologies make them accessible to businesses of all sizes.
Chirag brings with him 15+ Years of experience in Digital Transformation, and IT Leadership. At Plutomen, he holds deep experience in business with a track record of customer-centric approaches helping them build business transformation.
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