Designing Products For A Circular Economy With Digital Twins

Innovation Designing Products For A Circular Economy With Digital Twins Dan Gamota Forbes Councils Member Forbes Technology Council COUNCIL POST Expertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based) Jun 14, 2022, 10:15am EDT | Share to Facebook Share to Twitter Share to Linkedin VP of manufacturing, technology and innovation at Jabil .

Over 20 years of experience helping global teams deploy cutting-edge manufacturing. getty As digital transformations go, the ability to discover, develop and deploy a product in the virtual world before stepping into the physical one holds far-reaching promise. Increasingly, the use of digital twins—virtual representations of people, places and things—is emerging as one of the biggest and most impactful results of the digital age.

Not only can digital twins take advantage of major advancements in machine learning, analytics, artificial intelligence (AI) and augmented reality/virtual reality (AR/VR), digital twins might just save the planet by significantly slashing our global carbon footprint by empowering a circular economy and major advancements in sustainability. The Growth Of Digital Twins uring the Covid-19 pandemic, digital twins made it easier and faster to collect and correlate patient data for more personalized medical treatments while speeding up the production of life-saving vaccines. Siemens worked with BioNTech SE to design and validate a production facility in record time, enabling the company to launch its Covid-19 vaccine in five months, less than half the time it would normally take for such a major undertaking.

While the pandemic served as an accelerant for advancing digital-twin technology, a growing comfort level with collecting and sharing personal data also has created new opportunities to scale digital twins on multiple fronts. Advocates of medical digital twins promote the benefits of treating current medical issues as well as predicting and mitigating potential future health issues based on risk models and simulations. MORE FOR YOU Google Issues Warning For 2 Billion Chrome Users Forget The MacBook Pro, Apple Has Bigger Plans Google Discounts Pixel 6, Nest & Pixel Buds In Limited-Time Sale Event Beyond supporting the delivery of personalized care, the ability to simulate different production-line configurations or equipment-predictive maintenance programs can help bring novel medical-device treatment systems to market faster, more cost-effectively and with less risk.

Devising more efficient, sustainable methods and models for developing, testing and validating product designs applies to every industry sector, which is propelling the entire market forward. As a result, the global digital-twin market is forecasted to grow at a CAGR of 40. 2% from 2022 to 2028 .

Another major growth driver for digital twins can be found in the evolving world of flexible hybrid electronics (FHE). As crucial enablers of digital twins, these lightweight, pliable, yet powerful embedded electronics can be integrated into all types of products to collect massive amounts of data for real-time monitoring, modeling and simulation. Flexible hybrid electronics are everywhere.

FHE creates a new category of electronics by combining the flexibility and low cost of printed traces and devices on plastic film substrates, which are stretchable and conformable, with the high performance of low-profile semiconductor chips. As such, they offer creative options for integrating electronics into everything from products that capture data on an individual’s physiological condition to embedded systems that perform continuous monitoring or “structural health checks” on planes, trains, automobiles, roads, bridges and buildings. According to the Alan Turing Institute , next-generation digital twins are advancing the field of aeronautics by supporting research in aerodynamics, applied mathematics and computational science.

The results are significant improvements in the development of electric propulsion, more fuel-efficient aircrafts for commercial travel and the exploration of urban aerial taxis. In all these cases, the use of lightweight FHE is critical in producing more accurate digital twins that empower engineers to identify potential issues and modify product designs before any flight risks emerge. In many ways, FHE has become a wonderful toolkit for innovation as it lets us collect, connect and correlate steady streams of data to make better-informed decisions.

With each iteration, the digital twin becomes a closer, more accurate representation of the physical entity. This cannot be achieved, however, without the digital twin combining rich data generated during product design, product manufacturing and product operation performance. It is a closed-loop system where one source of data feeds into and enhances the next digital twin iteration and the next digital twin iteration.

Eventually, what goes around comes around to establish the most accurate digital twin. A Marvel Of Modeling To realize the full potential of FHE-enabled digital twins, let’s consider the world of semiconductor chip fabrication. Major research and development efforts are focused on improving both the materials and processes involved in making semiconductor chips, yet any change can pose costly risks.

For instance, a contaminant in a clean room can cause the scrapping of tens of millions of dollars in bare semiconductor chips and packaged semiconductor chips, which is why every part of the manufacturing process (front-end and back-end production) must be carefully de-risked. Enter the digital twin, which can virtually march through every different scenario without the time, cost and energy demands associated with physically operating semiconductor manufacturing processes. Radically altering the energy requirements of an existing semiconductor fab becomes feasible using a digital twin to model each proposed configuration change in the virtual world prior to performing physical operations.

The marvel of modeling in reducing carbon footprints cannot be overstated: It opens new possibilities for developing and manufacturing a diverse array of semiconductor chips with perpetual sustainability and the circular economy in mind. In a recent article posted on the McKinsey & Company site , the authors offered some powerful examples of digital twins in action. In one case, an aerospace and defense company used digital twins to replicate systems in complex mission scenarios to reduce by up to 40% the time required to develop advanced products.

In another example, an electric vehicle (EV) manufacturer uses live data from more than 80 sensors to track energy consumption under different driving regimes and in varying weather conditions. In manufacturing, the concept of designing for a circular future is often part of an overarching sustainability strategy to reduce waste and increase reuse while achieving the highest levels of product quality, reliability and excellence. Thanks to digital twins and critical enablers, such as FHE, this aspiration is becoming a reality.

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