Designing products for a circular economy with digital twins

Designing products for a circular economy with digital twins

Designing products for a circular economy with digital twins

VP of Production, Technology and Innovation at jabil† Over 20 years of experience helping global teams implement advanced manufacturing.

As digital transformations take place, the ability to discover, develop and deploy a product in the virtual world before entering the physical world holds a far-reaching promise. The use of digital twins – virtual representations of people, places and things – is increasingly emerging as one of the greatest and most impactful achievements of the digital age.

Not only can digital twins benefit from significant advances in machine learning, analytics, artificial intelligence (AI) and augmented reality/virtual reality (AR/VR), digital twins may well save the planet by significantly reducing our global carbon footprint. reduction through a circular economy and major progress in the field of sustainability.

The growth of digital twinsDuring the Covid-19 pandemic, digital twins made it easier and faster to collect and correlate patient data for more personalized medical treatments, while accelerating the production of life-saving vaccines. Siemens partnered with BioNTech SE to design and validate a manufacturing 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 large project. company.

While the pandemic has accelerated the advancement of digital twin technology, a growing level of comfort in collecting and sharing personal data has also created new opportunities to scale digital twins on multiple fronts. Proponents of medical digital twins promote the benefits of treating current medical problems and predicting and mitigating potential future health problems based on risk models and simulations.

In addition to supporting the delivery of personalized care, the ability to simulate different production line configurations or equipment predictive maintenance programs can help bring new medical device treatment systems to market faster, more cost-effectively and with less risk.

Coming up with more efficient, sustainable methods and models for developing, testing and validating product designs applies to every industry sector, propelling the entire market. As a result, the global digital twin market is expected to grow at a CAGR of 40.2% from 2022 to 2028.

Another key growth driver for digital twins can be found in the evolving world of flexible hybrid electronics (FHE). These lightweight, pliable, yet powerful embedded electronics are critical to digital twins and 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 tracks and devices on plastic film substrates, which are stretchable and malleable, with the high performance of unobtrusive semiconductor chips. As such, they offer creative options for integrating electronics into everything from products that record data about a person’s physiological state to embedded systems that perform continuous monitoring or ‘structural health checks’ on airplanes, trains, automobiles, roads, bridges and buildings. .

According to the Alan Turing Institute, next-generation digital twins advance the field of aviation by supporting research in aerodynamics, applied mathematics and computational science. The results are significant improvements in the development of electric propulsion, more fuel-efficient aircraft for commercial travel and the exploration of urban air taxis. In all these cases, the use of lightweight FHE is critical to producing more accurate digital twins that allow engineers to identify potential problems and adapt product designs before flight risks arise.

In many ways, FHE has become a great toolkit for innovation as it allows us to collect, connect and correlate steady streams of data to make more informed decisions. With each iteration, the digital twin becomes a closer, more accurate representation of the physical entity.

However, this cannot be achieved without the digital twin combining rich data generated during product design, product manufacturing and product performance. It is a closed system where one data source inputs and improves the next digital twin iteration and the next digital twin iteration. Eventually, some work will come around to determine the most accurate digital twin.

A miracle of modeling

To realize the full potential of FHE-compatible digital twins, let’s take a look at 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, but any change can come with costly risks.

For example, a contamination in a cleanroom can lead to the scrapping of tens of millions of dollars worth of bare semiconductor chips and packaged semiconductor chips. Therefore, every part of the production process (front-end and back-end production) must be carefully made risk-free.

Enter the digital twin, which can march through virtually any other scenario without the time, cost and energy associated with physically working semiconductor manufacturing processes. Radically changing the power requirements of an existing semiconductor plant is made possible by using a digital twin to model each proposed configuration change in the virtual world before performing physical operations.

The miracle of modeling in reducing the carbon footprint cannot be overstated: it opens up new possibilities for the development and production of a diverse range of semiconductor chips with eternal durability and the circular economy in mind.

In a recent article on the McKinsey & Company site, the authors provided 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 the time it takes to develop advanced products by up to 40%. 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 ambition is becoming a reality.


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