This article by Jason Hehman was originally published on CDInsights in 2023 and has been updated and republished here to reflect the latest insights and developments.
Industry 4.0 – the emerging future of smart manufacturing – combines software, automation, and Internet of Things (IoT) devices to support faster, smoother, and more cost-effective operations. Getting there, though, depends on tools that provide accurate, data-driven insights about floor-level machinery. Also critical: a low-risk way to continually optimize performance and equipment health.
Enter digital twins, a cornerstone of digital twin technology. They use IoT data to replicate physical machinery (like a robot or conveyor belt) in a virtual environment. And they’re the key to smart manufacturing.
Here, I’ll explain how digital twins work and how the manufacturing industry can use them to improve efficiency and protect the bottom line, utilizing a methodology that leverages big data for precise analysis.
How Real-Time IoT Data Powers Digital Twins
True digital twins are more than static 3D simulations. They rely on real-time data that continuously flows between IoT-monitored equipment and engineers. Here’s what the manufacturing process looks like:
IoT sensors collect real-time data about equipment. This could include data analytics about a machine’s position, electrical currents, rotational speed, noise emission, energy consumption, temperature, etc.
The data feeds into the manufacturer’s cloud computing system or industrial internet. Depending on the sensor hardware, data transmission can occur via WiFi, Bluetooth, cellular, or wide access network (WAN).
Engineers use cloud data to create a virtual model of the monitored equipment. From here, engineers can manipulate this digital twin without actually being on the facility floor. If any changes need to occur to the physical equipment, they can remotely communicate that to nearby workers.
As IoT sensors continue to improve their connectivity with cutting-edge technology (like 5G), they’ll be able to relay larger volumes of data at higher speeds than ever. That means manufacturers can maximize the accuracy and relevancy of every digital twin.
Digital Twins Enable Smarter Decision Making
One of the biggest advantages to a digital twin is the ability to track a machine’s operation in real time outside of the manufacturing environment. And with the right software, artificial intelligence and machine learning can recommend adjustments that could extend a machine’s lifetime or improve its performance.
Those two use cases – predictive maintenance and performance optimization – can pave the way for smart manufacturing that protects the bottom line. To illustrate, I’ll walk you through a scenario for each, functioning as a case study.
Use Case #1: Predictive Maintenance
Imagine an auto manufacturer that’s struggling to keep its automated conveyor belts from breaking down on the production line without warning. Engineers can use a series of digital twins to monitor the full conveyor belt system. Thanks to real-time data, they can pinpoint the moment a foreign object gets caught in a machine or a motor starts slowing down – and alert a worker on the floor before the system comes to a halt.
What’s more, AI can use equipment data analytics to notify engineers when a motor has worn down past a certain threshold. It can even suggest an optimal maintenance schedule by comparing real-time and historical data.
The bottom line: with the help of digital twins, manufacturers can slash equipment downtime and keep revenue flowing.
Use Case #2: Performance Optimization
Picture a food and beverage manufacturer that wants to improve the efficiency of its packaging robots. With digital twins, engineers can track each robot’s performance and identify specific components that could be slowing them down.
Then, they can virtually test individual tweaks (like a motor upgrade) and simulate their effectiveness in custom production scenarios. They can even adjust environmental factors, like heat or humidity, to gauge the upgraded robot’s structural resilience. Once the engineers have identified the right changes, they can relay next steps to the right team.
The benefit: digital twins let manufacturers explore ways to create new efficiencies without disrupting equipment on the floor.
Digital Twins as a Product Development Sandbox for Intelligent Manufacturing
So far, we’ve looked at ways digital twins can help manufacturers optimize equipment within their facilities. But they’re also powerful tools for intelligent manufacturing—intelligently improving devices in production.
Think of digital twins here as a product development sandbox: engineers can model a near-infinite number of product changes and operational scenarios in a matter of seconds. Most importantly, these changes happen without the need for costly physical rework.
The simulation and testing process is similar to the ones we’ve already explored. The main difference: each model relies on a knowledge base of IoT data about prototypes versus a true real-time stream. Even so, engineers get a highly accurate prototype simulation that they can virtually manipulate to boost its performance. It’s a low-risk way to test, fail, learn, and create better products.
Don’t Wait to Invest in Digital Twins
Digital twins are a core enabler for Industry 4.0, acting as pivotal components in smart manufacturing systems and the evolution towards the smart factory model.
Many firms are already watching this space closely. The proof: investments in digital twin technology, which plays a crucial role in product lifecycle management and virtual reality simulations, are set to reach $73.5 billion by 2027.
These technologies not only replicate physical assets in the real-world but also ensure seamless synchronization between the virtual and physical realms, enhancing operational efficiencies across the board.
It’s clear that manufacturers adopting digital twins will gain a significant advantage in this next phase of industrial innovation, propelling them towards more agile, responsive, and intelligent manufacturing environments. Invest now or risk falling behind in this rapidly evolving landscape.
