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Key AI Technologies in Manufacturing: A Comparative Analysis of Digital Twin vs. Domain Twin
In recent years, the rise of Industry 4.0, smart manufacturing, AI applications, and digital transformation has made the concept of the “Digital Twin” increasingly popular in the manufacturing sector. However, as companies begin integrating AI, they encounter several challenges, including insufficient data, talent shortages, and implementation bottlenecks. In response, a new concept has started to gain attention: the “Domain Twin.”
Although the names of these two concepts are similar, their meanings are entirely different. Digital Twin addresses “visible physical problems,” while Domain Twin complements “invisible experiential knowledge.” Only by complementing each other’s strengths and weaknesses can manufacturing move from data-driven to intelligence-driven. This article explores the definitions, differences, and applications of Digital Twin and Domain Twin to help companies make informed decisions in their smart transformation strategies.
What is Digital Twin? A Virtual Replica of Equipment Data
A Digital Twin is a virtual replica of a physical device, system, or process. By connecting sensors and real-time data, it can simulate the state, behavior, and performance of its physical counterpart, helping businesses with monitoring, predictive maintenance, and process optimization.
Core features of Digital Twin include:
- Creating a data-driven model synchronized with physical assets
- Real-time simulation of the operation of equipment or systems
- Commonly used in predictive maintenance, operational status monitoring, and energy efficiency analysis
- Focused on simulating and monitoring specific machines, processes, or physical equipment
According to the Ministry of Economic Affairs, a globally renowned automobile brand implemented Digital Twin technology and, through integration across various stages from product development to mass production, was able to simulate quality, resource allocation, and process stability in advance, reducing time and cost risks. They also integrated AR for staff training, significantly improving assembly efficiency, accuracy, and on-site safety.
Thus, Digital Twin uses virtual replication and data simulation to help companies better understand equipment conditions, predict risks, and improve overall production and training efficiency. However, while Digital Twin can fully simulate equipment and processes, it cannot capture the experience, judgment logic, and tacit knowledge of seasoned workers, which is where Domain Twin comes into play.
What is Domain Twin? The Key Technology for AI to Mimic Expert Decision-Making
Domain Twin is a different concept that addresses the “human intelligence layer” missing in Digital Twin. It models professional knowledge and industry logic comprehensively, allowing AI to “learn” and reuse human experience. Using a No-Code approach, it can be rapidly applied in different but similar manufacturing scenarios.
In manufacturing, the experience and skills of senior workers are often the result of decades of accumulated wisdom. However, these valuable insights are frequently lost due to retirements or personnel changes. Profet AI’s Domain Twin is designed to solve this issue by digitizing and upgrading the expertise of senior workers in machine calibration, formula optimization, and problem-solving, transforming it into a long-lasting, valuable asset for the business.
Unlike typical AI models, Domain Twin integrates with AutoML (Automated Machine Learning) and AILM (AI Lifecycle Management) platforms to tightly link departments and processes such as R&D, production, and after-sales. This ensures fast end-to-end integration. More importantly, Domain Twin enables key data related to R&D, production, dispatch, testing, etc., to remain internal, safeguarding the company’s core technologies.
Core features of Domain Twin include:
- Digitizing the knowledge and experience of senior workers into reusable AI model logic
- No code required, allowing users to directly operate model templates for predictive analysis
- Designed to address common repetitive issues in manufacturing, such as quality forecasting and defect classification
- Helping businesses lower AI adoption thresholds, improving modeling efficiency and standardization
For example, after implementing Profet AI’s Domain Twin technology in their PCB production line, a company successfully simulated process parameters like gold and nickel plating in real time. They used AI models to predict the probability of defects and recommend optimal formulas, reducing trial production costs and error rates.
Additionally, through the integration of virtual and real simulations and built-in knowledge modules, they reduced the learning curve for new employees by 40% and accelerated implementation by 50%, creating a more flexible smart manufacturing process.
Comparing Digital Twin and Domain Twin
If Digital Twin is the “shadow” of the factory, Domain Twin is the “brain” of the engineers, because it understands logic, processes, and judgment. It can teach AI to mimic these experiences. Therefore, the focus of Domain Twin lies in virtually replicating industry knowledge and logic, enabling AI to learn and apply this knowledge quickly in various scenarios.
Profet AI’s Vision: Empowering Businesses with AI-Driven Smart Decision-Making
In summary, both Digital Twin and Domain Twin have their own strengths: the former focuses on the virtual simulation of equipment and processes, while the latter infuses human experience and professional judgment. The emergence of Domain Twin fills the gaps left by Digital Twin, making it an essential part of the manufacturing industry’s journey toward smart transformation. Only by complementing each other can these two technologies help the industry overcome transformation bottlenecks and achieve continuous optimization and growth.
At Profet AI, we believe that AI should not be the privilege of a select few experts, but a tool that every business can leverage. Through our Domain Twin solution, companies can quickly transform internal knowledge into repeatable and optimizable smart decision models, truly realizing Knowledge as a Service.