Smart digital twins and artificial intelligence: a technology partnership
Smart digital twins are like creating an identical "twin" in the virtual world for real industrial equipment, production lines, and even entire factories. This "twin" is not just a simple resemblance. It can obtain real-time operating data such as temperature, vibration, and pressure of the equipment through various sensors installed on the equipment, as well as order information and material consumption during the production process. It is like installing "eyes" and "ears" on the equipment, transmitting everything it sees and hears to the virtual model, so that the virtual model can accurately simulate the state, behavior, and performance of the equipment in reality. Whether it is every movement of the robotic arm or the operating details inside the complex machine, it can be perfectly presented in the virtual world.
Data collection and “organization”
To make the "twin" of the smart digital twin real and reliable, the first step is to collect a large amount of data. In the factory, various sensors need to be installed, just like arranging dense "little detectives" for the equipment, which are responsible for collecting various information about the operation of the equipment. This information is of various types and formats, just like letters in different languages, which need to be processed initially. At this time, edge computing comes on the scene. It is like a small data processing station, which simply "sorts" the data near the data source, removes some duplicate and erroneous information, and then transmits the processed data to the digital twin platform to "fuse" it with the virtual model.For example, in an automobile manufacturing plant, sensors on production line equipment such as stamping, welding, and painting transmit equipment operation data to the digital twin model in real time, so that the virtual model can always know the status of the equipment in reality and provide an accurate data basis for subsequent analysis.
Equipping “twins” with “intelligent brains”
In order to make industrial automation systems smarter, artificial intelligence algorithms should be embedded in digital twin models. Deep learning algorithms are "experts" in predicting faults. They are like an experienced old doctor who remembers the various "symptoms" before a device fails by learning the device's past operating data and previous failure cases. When the digital twin model detects an abnormal change trend in the device's operating parameters, the deep learning algorithm can predict in advance when the device may fail and give maintenance suggestions, such as reminding workers which parts to replace and when it is best to repair it.
The reinforcement learning algorithm focuses on optimizing production scheduling. Like a smart production scheduler, it aims to improve production efficiency and reduce energy consumption. In the virtual environment built by the digital twin, it constantly tries different production scheduling strategies to see which solution can make the production line run faster and better, and finally find the optimal production plan. For example, in chemical production, the reinforcement learning algorithm will flexibly adjust production arrangements and reduce production costs based on factors such as the supply of raw materials and the operating status of equipment.