The creation of a digital representation of products is a by-product of digital manufacturing and the digital thread, the essence of which is to maintain and re-use digital information developed during the design and manufacturing of a product throughout the life of that product. That digital product information is often called a digital twin.
The so-called digital twin is an accumulation of data that is established during the design and manufacturing process but continues to grow through the life of the product. Once the product is sold and put into service in the field, its life history including condition data, sensor readings, operating history records, as-built and as-maintained (as-is) configuration states, serialized part inventory, software versions, and more provide service and maintenance functions with a complete picture of the product.
The digital twin allows the service function to analyze the product’s current status and performance for scheduling preventive and predictive maintenance activities including calibration and tooling management. In conjunction with a maintenance management software system, digital twin information can be used to manage repair parts inventories and have the parts most likely to be needed at the right place (on the repair truck or on-site at the equipment location) when the service techs arrive to complete the repair, upgrade, or maintenance. With enough examples in the database, engineering can assess performance of a particular family of equipment and its component parts for product improvement studies.
The basic idea behind the digital twin is not new. Ever since CAD/CAM started building digital models of product physical characteristics and attributes, there has been a sense that this data could prove useful downstream, especially after delivery, if performance and service information could be combined with the original design data to enhance support and feed future design decisions. In some markets, like aerospace and defense, configuration history (provenance) and service records are a primary concern and could benefit from having the data from the full lifecycle combined into one single product digital twin. But technology was an impediment to accomplishing this objective – it wasn’t easy to pass this data between systems and make use of it with different software on different platforms.
Those technology issues have pretty much disappeared in recent years. In addition, the Industrial Internet of Things (IIoT) is bringing a veritable ocean of data from installed sensors monitoring the usage, performance, and quality that can be added to the digital twin, making it an increasingly accurate and complete view of the product as it exists in the field.
NASA played a role in proving the need for and utility of the digital twin. Spacecraft are generally inaccessible so gathering sensor information through telemetry is just about the only way they are able to monitor performance and complete any tuning or adjustments that may be required. When manned missions encounter problems, simulators and digital twin databases can help pinpoint the problem, devise possible fixes, and test out repair actions on the ground before asking the astronauts perform risky repairs in space.
The digital twin has proven to be very useful and valuable for all kinds of machinery and equipment products by improving maintenance operations, providing better support for equipment in the field, caving money, reducing breakdowns and extending equipment durability. As IIoT data proliferates, the digital twin will become more complete, more detailed, and even more useful in getting the most out of equipment and maintenance investments while spurring improved product design and support.