In the digitization age, there are several evolving technologies like Cloud computing, Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Digital Twin (DT), and many more, which are developed and implemented in product development and design. Among all these emerging technologies, DT is one of the most versatile technologies utilized in many industries, specifically in the manufacturing industry, to monitor the execution, optimize the growth, simulate the output, and predict the probable errors.
Also, DT plays many roles in the product development lifecycle, from manufacturing to designing, using, delivering, and end-of-life. DT can also provide an efficient solution for future product development, design, and innovation, with the developing demands of specific products and the utilization of Industry 4.0.
Digital Twin Defined
A DT can be defined as the virtual twin of the characteristics of a system in its operating environment. That twin system can be a manufacturing process, a product, or the complete supply chain presented by an accumulation of digital models. Those models operate and respond to several stimuli, data presenting the external environment. Digital twins accumulate several model types and process data from several sources. That helps them to provide a better estimation of a real object than old simulation techniques.
Invention of Digital Twin
The idea behind digital twins was coined by David Gelernter in 1991 in his book ‘Mirror Worlds,’ and Michael Grieves of the Florida Institute of Technology applied the idea to manufacturing.
Grieves had shifted to Michigan University by 2022 when he introduced the DT concept formally at the Society of Manufacturing conference in Troy, Michigan.
However, John Vickers of NASA first accepted the idea of the digital twin, which was utilized to generate digital simulations of space capsules and create them for testing.
The concept of DT was embraced hugely in 2017 when Gartner named the concept as one of the top 10 dynamic technology trends. After that, the digital twin idea has been implemented in various industrial processes and applications.
Digital Twins Today
According to many studies and investigations, it is found that nearly 75% of firms have adopted digital-twin technology and achieved medium levels of complication. However, there is an important deviation between sectors.
With a digital twin, the aerospace, automotive, and defense industries experience more advanced innovation. In contrast, other industries like infrastructure and aerospace industries are more likely to be processing their primary DT concepts.
Applications in Smart Product Development
Digital twins provide an array of applications in innovative product development, transforming old practices and exploring new possibilities. Some important fields where digital twins are making a strong impact include:
DT helps engineers and designers make virtual prototypes of their products, helping them discover several design iterations, test functionalities, and find potential problems before physical production starts. This regularizes the product development process, minimizes time-to-market, and reduces expenses regarding iterative prototyping.
Manufacturers can conduct virtual simulations and tests to examine the durability, presentation, and product safety under different conditions. By duplicating real-world conditions in a digital environment, organizations can determine flaws in design, optimize performance measures, and ensure compliance with regulative standards.
DT helps predictive maintenance techniques by continuously monitoring the health and condition of physical assets in real time. Organizations can predict maintenance requirements, determine failures or anomalies, and schedule repairs by analyzing data from sensors integrated into devices, vehicles, or machines. This reduces downtimes, increases asset lifespan, and improves operational efficacy.
- Supply Chain Optimization:
Digital twins ease the optimization of supply chain operations and end-to-end visibility. Firms can analyze factors like production schedules, inventory levels, transportation routes, and demand forecasts by making digital twins of supply chain networks. This helps enhance resource allocation, decision-making, and responsiveness to market dynamics.
- Challenges and Future Outlook
While the capability of digital twins is huge, their broad adoption still faces some challenges, which include security concerns, data privacy, interoperability problems, and the requirement for skillful talent to develop and handle digital twin ecosystems. However, since technology continues to emerge and mature, these issues are predicted to be fixed, paving the way for wider adoption and innovation in smart product development.
Conclusion
Digital twins revolutionized the way products are built, designed, and handled. Organizations can accelerate innovation, enhance product quality, and get competitive advantage by utilizing the power of digital twins. Since firms have adopted this revolutionary technology, digital twins have become vital tools for realizing the vision of sustainable and smart product development.
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