Oak Ridge National Laboratory researchers have developed Peregrine, an AI software package for powder bed 3D printers that assesses the quality of parts in real time.
Peregrine is said to support the advanced manufacturing ‘digital thread’ being developed at ORNL that collects and analyses data through every step of the manufacturing process, from design to feedstock selection to the print build to material testing.
“Capturing that information creates a digital ‘clone’ for each part, providing a trove of data from the raw material to the operational component,” said Vincent Paquit, who leads advanced manufacturing data analytics research as part of ORNL’s Imaging, Signals and Machine Learning group. “We then use that data to qualify the part and to inform future builds across multiple part geometries and with multiple materials, achieving new levels of automation and manufacturing quality assurance.”
The digital thread supports the factory of the future in which custom parts are conceived using CAD and then produced by self-correcting 3D printers via an advanced communications network, with less cost, time, energy and materials. According to ORNL, the concept requires a process control method to ensure that every part rolling off printers is ready to install in applications like cars, airplanes, and energy facilities.
To devise a control method for surface-visible defects that would work on multiple printer models, ORNL researchers created a novel convolutional neural network, which is a computer vision technique that mimics the human brain in quickly analysing images captured from cameras installed on the printers. The Peregrine software uses a custom algorithm that processes pixel values of images, taking into account the composition of edges, lines, corners and textures. If Peregrine detects an anomaly that may affect the quality of the part, it automatically alerts operators so adjustments can be made.
The software is well suited to powder bed printers, which are popular for the production of metal parts. However, during the printing process, problems such as uneven distribution of the powder or binding agent, spatters, insufficient heat, and some porosities can result in defects.
“One of the fundamental challenges for additive manufacturing is that you’re caring about things that occur on length-scales of tens of microns and happening in microseconds, and caring about that for days or even weeks of build time,” said ORNL’s Luke Scime, principal investigator for Peregrine. “Because a flaw can form at any one of those points at any one of those times, it becomes a challenge to understand the process and to qualify a part.”
Peregrine is being tested on multiple printers at ORNL and is part of the Transformational Challenge Reactor (TCR) Demonstration Program that is pursuing the world’s first additively manufactured nuclear reactor.
“For TCR in particular, you could have a scenario in which the regulator will want detailed data on how a part was manufactured, and we can provide specs with the database built using Peregrine,” Scime said.
“Establishing correlations between these signatures collected during manufacturing and performance during operation will be the most data-rich and informed process for qualifying critical nuclear reactor components,” said Kurt Terrani, TCR program director. “The fact that it may be accomplished during manufacturing to eliminate the long and costly conventional qualification process is the other obvious benefit.”
ORNL researchers stress that by making the Peregrine software machine-agnostic printer manufacturers can save development time while offering an improved product to industry. Peregrine produces a common image database that can be transferred to each new machine to train new neural networks quickly, and it runs on a single high-powered laptop or desktop. Standard cameras were used in the research, ranging in most cases from 4 to 20 megapixels and installed so they produce images of the print bed at each layer. The software has been tested successfully on seven powder bed printers at ORNL so far, including electron beam melting, laser powder bed, and binder jetting, as detailed in Additive Manufacturing.
“Anything we can do to help operators and designers know what works and what doesn’t helps with the confidence that the part will be okay for use,” Scime said. “When you have a 3D map of every pixel where the network thinks there is an anomaly and what it thinks the problem is, it opens up a whole world of understanding of the build process.”