The future of manufacturing is here.
Digital twins are the next big thing in manufacturing, and it’s time you get on board with this new technology. A digital twin is a physical object model that can predict how the real world product will perform under different conditions, says Jack Barrett, account executive at Microsol Resources.
Like any trend, it’s never too late to join. But once the use of digital twins becomes commonplace, learning about them won’t have the same value that implementing them earlier on has. So, it would be wise to act fast.
A brief history of digital twinning
Digital twinning is creating a virtual replica of a physical object. You can do this for individual components or an entire manufacturing plant. The first digital twins existed in the early 1990s when researchers at NASA used them to model the aerodynamics of aircraft wings.
Since then, digital twinning has expanded far beyond NASA’s original application. For example, digital twins improve the design of engines and simulate various manufacturing processes. Architects use them to model entire buildings, while power industries utilise them to replicate transmission systems.
Applications in manufacturing – The digital replica
A digital replica, or digital twin, is an exact copy of a physical object or system created in a computer system. In other words, the term “digital twin” usually refers to the virtual representation of a real world object or process.
A digital twin can facilitate monitoring, analysis, optimisation, and control of a product, component or system. Designers and manufacturers can create twins for a single process, a large scale system, a small object or a physically large project.
The use of digital twins has increased recently, especially in the manufacturing sector. Manufacturers use digital twins to create virtual representations of both their products and processes. These tools make it easier for them to track and analyse data to improve product quality, adjust timeframes, and reduce costs.
Some of the critical applications of digital twins in manufacturing include the following.
Digital twins can create virtual representations of products before building them, allowing manufacturers to test new designs and make changes.
The manufacturing industry uses digital twins to improve efficiency and reduce costs by monitoring data from multiple sources. These sources include sensors, cameras, and other devices.
Also, digital twins help identify problems with products before they become serious, helping improve customer service levels and profit.
Supply chain and asset management
Manufacturers use digital replicas in their supply chains to help them be more efficient and effective. In other words, they can prevent issues with schedules, delays, and the general organisation of supplies.
Digital twins can track products throughout their life cycle. This process includes monitoring developments as they are being manufactured, shipped, and sold to customers.
Manufacturers use this information to improve customer service levels by identifying and resolving any product issues during their lifecycle.
Manufacturers can track all the crucial data associated with their assets by creating virtual representations of them. That includes performance history, usage patterns, and even maintenance information.
Engineering tools for development of digital twins
There are several engineering tools that you can use to develop digital twins. Some of the most popular tools include CAD (computer-aided design), simulation, and data management. Each of these tools has its strengths and weaknesses, and it is vital to select the right tool for the job.
CAD software creates digital models of physical objects. For example, you can use it to create individual parts or assemble models and build complete product designs. Also, CAD software is the best tool for creating detailed models with precise measurements.
Simulation software models the behavior of physical systems. It can simulate the performance of products under various conditions, and it can also test new designs. Simulation software is essential for verifying that products meet all safety and performance requirements.
Data management tools collect, organise, and analyse manufacturing processes and products. They can help identify trends and patterns in data, leading to improved efficiency and quality. Also, data management tools are handy for detecting patterns in large datasets.
There are other engineering tools that you can use to develop digital twins, including metrology and scanning. Metrology tools measure physical shapes and dimensions.
Also, they can help create reference models for developing 3D CAD models of parts and assemblies. Scanning tools use lasers, cameras, or other devices to capture snapshots of real world objects in 3D. By combining the output of metrology and scanning tools, you can build detailed digital models of real-world objects with precise measurements.
Predictive data analysis will provide a competitive advantage
Manufacturing companies are under pressure to reduce costs and increase efficiency. To stay competitive, they need to adopt new technologies that will give them a competitive advantage. Predictive data analysis is one of those technologies.
Predictive data analysis involves using historical data to predict future outcomes. You can do this by analysing patterns in the data or using machine learning algorithms.
The benefits of predictive data analysis are evident in the manufacturing industry. For example, Ford Motor Company used predictive analytics to improve its production process.
As a result, Ford could optimise their process before it failed by predicting when a part would break down. This reduced the number of warranty claims and revenue losses that would have occurred if Ford had not used predictive data analysis.
Digital twins will play an increasingly important role in manufacturing as technology evolves. Digital twins can help companies improve product quality, reduce waste, and optimise operations.
As the technology continues to develop, you can expect to see even more benefits from using digital twins in manufacturing.
Jack Barrett is an Account Executive for Microsol Resources focused on engineering and construction firms in New York. Jack has a solid background in solution selling and provides a unique insight into the AEC industry and the technology trends. He has worked with AEC firms in assisting them with the software and services that provide Microsol Resources customers with the power to solve their business, design, and environmental challenges.
The author is Jack Barrett, account executive at Microsol Resources.
Follow us and Comment on Twitter @TheEE_io