Introduction

Imagine a giant metal part being manufactured on the shop floor of an engine manufacturing company. The initial structure of this part has been made using the casting process, and it is ready for milling, drilling, and welding. This will give it a final shape as per the CAD model specifications. The part is being manufactured as a replacement for a part in a ship engine, which is unexpectedly bound to fail in some time. En route to Europe, the cargo ship carries large amounts of medical, food, and other raw materials. These supplies will be delivered regularly for the next few weeks. If this cargo ship gets delayed, it may lead to immense supply chain crises for many local industries, affecting the daily lives of millions of people.

However, this will not happen, thanks to the Digital Twin model of the ship engine. It allows continuous monitoring and prediction of any unexpected failures. The engine is monitored by multiple cameras across various locations. Vibration and temperature sensors, gyroscopes, and accelerometers transmit live feed and data related to all the engine parameters to the twin model. This data is then mapped to parameters similar to the engine’s simulated model. The mapping helps the digital twin model understand the life of each of the specific engine parts and update the failure time. Based on the failure time range (the time to replace the part) decided by the operations team, the twin will automatically trigger an order for a replacement part to the procurement team. Soon after, the automated purchase order will be released to the parts supplier. The replacement part will be delivered to the ship’s next scheduled stop. The maintenance team reaches the port and replaces the failed part in the engine without downtime.

The above process involves the confluence of data flowing across the business functions, leading to efficient operations. Let us get into more details of digital twin in manufacturing and how this can be executed.

Digital twin as a concept

The ideation of Digital Twin started back in the 1960s. The terminology may have changed over the years, but the concept has remained the same since its inception. NASA created a digital twin model of each of their actual mission spacecraft to help them study and simulate under various conditions. Think of it as an online platform for testing, creating, and altering real objects without engaging with them in the real world. A digital twin allows users to investigate solutions for product lifecycle extension, manufacturing and process improvements, and product development and prototype testing. In such cases, a digital twin can virtually represent a problem so that a solution can be devised and tested in the program rather than in the real world. There are four types of digital twins, each contributing to the overall aggregate’s development. The figure below defines the four types of twins based on the above example.

Key considerations for designing a digital twin

The digital twin design process can be broken down into three broad types, which show the different times when the process can be used:

• Digital Twin Prototype (DTP): This is undertaken before a physical product is created. Usually, this consists of various simulation projects to test the hypotheses and shortlist the one with the best outcomes.
• Digital Twin Instance (DTI): DTI is created once a product is manufactured to test different usage scenarios. Various test cases are identified to determine if the system is running as expected in different scenarios.
• Digital Twin Aggregate (DTA): This gathers DTI information to determine a product’s capabilities, run prognostics, and test operating parameters.

• Design Process: Designing each of the above (DTP, DTI, and DTA) requires an agile approach. Outputs and learnings from each phase are linked and enable us to modify our input and approach at any time.

How to start your Digital Twin journey?

Now that we have understood the basic design process of a twin, it is time to understand the implementation journey. Every digital solution journey starts with defining a business case, which holds true in this case, too. The figure below shows the high-level approach to embark on this journey.

The potential of digital twin in manufacturing and other industries is vast, and as an industry, we must tackle challenges to create precise, dependable, and sustainable solutions. The journey can be complex, requiring a structured approach to maximize value and ROI. LTIMindtree consulting’s jumpstart approach, powered by our accelerators and frameworks can help you achieve your digital twin goals.

References

• Emergence of Digital Twins – Is this the march of reason?, Shoumen Palit Austin Datta, Researchgate: Journal of Innovation Management, 2017, (PDF) Emergence of Digital Twins – Is this the march of reason? (researchgate.net)
• What is Digital Twin Technology — Use Cases, Solutions, and Examples, Gramener, Medium.com, 2021, https://gramener.medium.com/digital-twin-technology-f2312d64ca36
• Digital twins: The art of the possible in product development and beyond, com, 2022, https://www.mckinsey.com/capabilities/operations/our-insights/digital-twins-the-art-of-the-possible-in-product-development-and-beyond
• The Rise Of Digital Twins: Accelerating Enterprises Virtually, Sayantan Dasgupta, Forbes.com, 2021, https://www.forbes.com/sites/forbesbusinessdevelopmentcouncil/2021/12/07/the-rise-of-digital-twins-accelerating-enterprises-virtually/
• What Is Digital Twin Technology?, Colin McMahon, ptc.com, 2022, https://www.ptc.com/en/blogs/corporate/what-is-digital-twin-technology
• What is a digital twin?,com, https://www.ibm.com/topics/what-is-a-digital-twin
• What is digital twin technology and how does it work? twi-global.com, https://www.twi-global.com/technical-knowledge/faqs/what-is-digital-twin

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