Industrial
Harnessing the power of the industrial metaverse
By WeiZheng Lee, director of micro products, Molex E
very new technology development paves the way for the next advance. Personal Computers (PCs) became the platform for the internet and global connectivity, the internet and cloud technology laid the foundation for e-commerce and social media. It’s often difficult to tell what new technology will offer the next leapfrog opportunity.
Though still in early stages of development, the metaverse is viewed by many industry experts as just such a launch pad. Gartner Research defines the metaverse as, “A collective virtual shared space, created by the convergence of virtually enhanced physical and digital reality. It is persistent, providing enhanced immersive experiences, as well as being device independent and accessible through any type of device, from tablets to head- mounted displays”.
The metaverse trend is also taking hold in unique ways throughout the industrial sector. The industrial metaverse is largely viewed as the first manifestation of the metaverse. The recent report “Unleashing the Power of the Industrial Metaverse” highlights how powerful data analysis, modelling and predictive analytics bring an opportunity to improve industrial manufacturing and design processes in terms of both costs and efficiency.
Elements of the industrial metaverse
The proliferation of smart devices, the development of artificial intelligence (AI) applications and machine learning (ML) algorithms, as well as the emergence of augmented reality (AR) and virtual reality (VR) have created a platform for the industrial metaverse.
n IIoT
The emergence of the cloud, which brings robust and speedy connectivity, has served as a precursor to several new
14 April 2023
types of smart devices which serve the industrial space. Virtually any device can now transmit data, often providing several data points, including geolocation, physical measurements such as temperature or pressure, and device health status. IoT Analytics reported that the global number of connected IoT devices used 14.4 billion active endpoints in 2022, and the firm predicts that number will exceed 27 billion by 2025. Consider the opportunities represented by all this data being available in real-time in the metaverse for modelling, analysis, and prediction. The possibilities are endless!
n Digital twins
Digital representations of real-world entities or systems, called digital twins, are utilized by engineers to simulate operations and performance. Using a software-based model, digital twins bring in physical measurements from the real world and
Components in Electronics
compare them with online predictions under different operating conditions. This provides engineers a powerful means of evaluating the performance of that system as they make various design modifications and other changes in the metaverse before making any physical changes to the product. Using digital twins to design new systems reduces development time and the need for extensive prototype testing.
n AR and VR
AR and VR are technologies that bridge the gap between the digital and physical worlds. A VR headset immerses the wearer in a 3D digital environment where they can visualize components or systems as they would in the physical world. This enables engineers to foresee ergonomic problems in production lines and improve work processes or product designs for more efficient manufacturing. The technology can also make user manuals available at
the work site, enabling remote experts to work with onsite technicians as they examine the physical equipment or system. Together these technology pillars create the platform for the industrial metaverse.
The priority of quality connections As impressive as the industrial metaverse is, it is highly dependent on physical devices and connectivity. Trillions of IoT connections are made physically and digitally, and both are essential for business continuity. The quality of software and components is critical, particularly in the electrical connectors that enable the networks, which ensure that data is both securely maintained and moving with the required speed. AI applications and ML algorithms depend on vast amounts of information for analysis and model building, like large data files containing video, audio and images. The quality of these file types is
www.cieonline.co.uk
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62
