Digital twins are nothing but the 3D digital replica of a physical thing. They have been in existence since the days computer-aided design became mainstream during the 1990s. However, they remained standalone replicas for the next 20 years until augmented reality (AR) became prominent in the gaming and entertainment industries.
As TechNewsWorld notes, AR—often referred to as mixed reality—is an immersive and "interactive experience of a real-world environment where computer-generated perceptual information enhances real-world objects." The technology expands our physical world by adding a digital layer and generating the AR. We can view AR on many devices, such as mobile phones and iPads, with a camera capturing sounding images in real time and superimposing the augmented portions to enhance the object or environment surrounding it. AR glasses incorporate the field of view and project images onto glasses providing the composite view. Digital twins with AR unite the physical and virtual worlds and provide a dynamic digital representation for us to interact with. AR is gaining momentum in higher education and monitoring hazardous environments such as oil rigs remotely.
With advances in Internet of things (IoT) sensors, specialized video processing in real time, support of machine learning (ML) and deep neural networks all rendered into slightly bigger eyewear, there is an opportunity for enterprises to leverage this technology. This article explores how data, ML and cloud can further augment and add value to enterprises leveraging AR.
Industry Examples
Following through on a new product such as an e-bike, from early design to final product launch, including detailed design, road testing, and road riding by the potential customer, all in AR and offering guided repairs to customers
- Oil rigs floating in the Arctic being remotely monitored from Houston using IoT sensor-enabled digital twins
- Off-floor monitoring of continuous-process manufacturing such as for soaps and shampoos
- Guided assembly of complex components such as airplane engines
- Remote-guided repairing of connected devices such as ATMs, coffee machines, and vending machines
- Remote roadside assistance for basic diagnosis for broken-down cars
Leveraging the Data They Generate in the Enterprise
One of the significant differences between the previous generation of digital twins and AR-enabled ones is the amount of data they generate from the sensors. The increased number of sensors emitting frequent data provides a more-immersive experience and offers vast operational data, and it can be precious to the enterprise. Therefore, we need to ensure we capture that data in a cloud-scale data lake not throw it away after the first real-time use in AR glasses.
We can analyze these data in real time or offline and leverage it for many uses, such as scheduling preventive maintenance to replace a part's wear and tear before it happens. In the field service examples, telemetry data can offer significant insights into how the end users operate the product and train them better. We can use the usage data from the field and feedback into the product design. The more sophisticated advanced process controls can interact with the physical twin with the same gesture controls from AR. They can also take operational feedback from ML algorithms and feed it into the physical twin (wherever possible today), such as resetting the factory line to the next batch of products. It can adjust the process line to support the next product variant's need, from can to bottle or skipping individual wrapping of teabags with just bulk pack.