A new artificial intelligence system pinpoints the origin of 3D printed parts down to the specific machine that made them. The technology could allow manufacturers to monitor their suppliers and manage their supply chains, detecting early problems and verifying that suppliers are following agreed upon processes.
A team of researchers led by Bill King, a professor of mechanical science and engineering at the University of Illinois Urbana-Champaign, has discovered that parts made by additive manufacturing, also known as 3D printing, carry a unique signature from the specific machine that fabricated them. This inspired the development of an AI system which detects the signature, or "fingerprint," from a photograph of the part and identifies its origin.
"We are still amazed that this works: we can print the same part design on two identical machines –same model, same process settings, same material – and each machine leaves a unique fingerprint that the AI model can trace back to the machine," King said. "It's possible to determine exactly where and how something was made. You don't have to take your supplier's word on anything."
The results of this study were recently published in the Nature partner journal Advanced Manufacturing.
The technology has major implications for supplier management and quality control, according to King. When a manufacturer contracts a supplier to produce parts for a product, the supplier typically agrees to adhere to a specific set of machines, processes, and factory procedures and not to make any changes without permission. However, this provision is difficult to enforce. Suppliers often make changes without notice, from the fabrication process to the materials used. They are normally benign, but they can also cause major issues in the final product.
"Modern supply chains are based on trust," King said. "There's due diligence in the form of audits and site tours at the start of the relationship. But, for most companies, it's not feasible to continuously monitor their suppliers. Changes to the manufacturing process can go unnoticed for a long time, and you don't find out until a bad batch of products is made. Everyone who works in manufacturing has a story about a supplier that changed something without permission and caused a serious problem."
While studying the repeatability of 3D printers, King's research group noticed that the tolerances of part dimensions were correlated with individual machines. This inspired the researchers to examine photographs of the parts. It turned out that it is possible to determine the specific machine made the part, the fabrication process, and the materials used – the production "fingerprint."
"These manufacturing fingerprints have been hiding in plain sight," King said. "There are thousands of 3D printers in the world, and tens of millions of 3D printed parts used in airplanes, automobiles, medical devices, consumer products, and a host of other applications. Each one of these parts has a unique signature that can be detected using AI."
King's research group developed an AI model to identify production fingerprints from photographs taken with smartphone cameras. The AI model was developed on a large data set, comprising photographs of 9,192 parts made on 21 machines from six companies and with four different fabrication processes. When calibrating their model, the researchers found that a fingerprint could be obtained with 98% accuracy from just 1 square millimeter of the part's surface.
"Our results suggest that the AI model can make accurate predictions when trained with as few as 10 parts," King said. "Using just a few samples from a supplier, it's possible to verify everything that they deliver after."
King believes that this technology has the potential to overhaul supply chain management. With it, manufacturers can detect problems at early stages of production, and they save the time and resources needed to pinpoint the origins of errors. The technology could also be used to track the origins of illicit goods.
Miles Bimrose, Davis McGregor, Charlie Wood and Sameh Tawfick also contributed to this work.
The study, "Additive Manufacturing Source Identification from Photographs using Deep Learning," is available online. DOI: 10.1038/s44334-025-00031-2
