Digital twins are like virtual copies, designed to help manufacturing organisations to work better. By analysing these virtual models, companies can prevent costly mistakes, improve production speed, and even introduce innovations that were not previously considered.
Digital twins can be virtual representations of physical objects, systems, or processes that act as a bridge between the digital and physical worlds. The real-time data acquired from sensors and machines allows digital twins to offer insights for optimizing operations, enhancing efficiency, and making informed decisions.
When used in manufacturing, digital twins can offer an array of benefits. For example, digital twins help engineers or designers anticipate equipment issues and resolve them before they escalate into costly downtime. This results in increased asset lifespan, reduced maintenance costs, and enhanced productivity.
This type of technology can also facilitate informed decision-making through simulations and scenario analyses, allowing engineers to test and validate strategies in a risk-free, virtual environment – leading to better strategic planning, faster problem-solving, and the ability to adapt swiftly to changing factory or market conditions.
In addition to their operational advantages, digital twins foster innovation and drive technological advancements. By providing a platform for continuous experimentation and prototyping, they expedite the development of new products and services, potentially shortening time-to-market.
It also stands that digital twins enable manufacturers to align their sustainability goals with real-world outcomes. They can help optimize resource consumption, energy usage, and waste generation in factories, contributing to eco-friendly practices and reduced environmental impact.
What are the types of digital twins?
Francesco Rivalta, engineer at Hubs, has provided Digital Journal with the latest innovation in digital twins technology.
Digital twins are categorized into various types to effectively capture the diverse range of applications and complexities they address. Grouping digital twins allows manufacturers to tailor their implementation to specific needs, such as optimizing product design or monitoring complex manufacturing processes.
Each type corresponds to a specific focus, allowing factories to deploy digital twins that align with their unique objectives.
The most common types of digital twins are:
Product digital twins
Product digital twins represent physical products and assets. They are used throughout the product lifecycle, from design and development to manufacturing, operation, and maintenance. Product digital twins help optimize machine or part design, simulate performance, monitor usage, and enable predictive maintenance.
Process digital twins
Using digital twins, we can simulate and play out manufacturing processes and other complex workflows. They highlight bottlenecks, enhance production, and up the efficiency game. They’ve also proven to be quite useful in managing supply chain operations — a challenge many businesses have grappled with (as outlined in this report).
System digital twins
System digital twins represent entire systems or environments, such as factories and industrial complexes, enabling holistic monitoring, analysis, and optimization of interconnected components or processes.
Asset digital twins
Asset digital twins focus on individual physical assets, such as machinery, equipment, or infrastructure. They facilitate remote monitoring, predictive maintenance, and performance optimization.
As digital twins technology continues to evolve, the technology will be integrated into a range of systems, from autonomous robotics to intelligent supply chains.
