US Army researchers at the CCDC Army Research Laboratory, Aberdeen Proving Ground, Maryland, USA, have reportedly discovered a way to monitor the performance of additively manufactured parts using predictive maintenance.
A new study published in the International Journal of Advanced Manufacturing Technology, titledIn-Situ Fatigue Monitoring Investigation of Additively Manufactured Maraging Steel, showed that the army could detect and monitor the wear of metal additively manufactured maraging steel through sensor measurement. These types of measurements are expected to help soldiers maintain readiness, because they help predict when parts will degrade or fail and need replacement.
“3D printed parts display certain attributes, due to the manufacturing process itself, which, unchecked, may cause these parts to degrade in manners not observed in traditionally-machined parts,” explained Dr Jaret C Riddick, Director of the Vehicle Technology Directorate at the US Army’s Combat Capabilities Development Command’s Army Research Laboratory.
“Because of this, it’s commonly understood that the use of these parts, in current cases, is meant to be a stop-gap to fill a critical need, just as we have seen with 3D printing during the COVID-19 response,” he continued.
He explained that the laboratory’s study points to a scientific discovery that ensures readiness in increasingly contested environments where the immediate need for replacement parts places constraints on the time it takes to deliver them from far away. In these cases, soldiers would opt for a stop-gap to continue the mission rather having to abort the mission.
The study was led by a team of researchers from the laboratory, the National Institute of Standards and Technology, CCDC Aviation and Missile Center and Johns Hopkins University, who likened cues from the material’s performance to a vehicle odometer reading that signals a need for an oil change.
“The strain or eddy current sensor would supply a measurement and let you know the part needs replaced,” stated Dr Todd C Henry, a mechanical engineer at the laboratory, who co-authored the study. Henry hopes to develop a tool for measuring the unique performance of each AM part, acknowledging that each is different via sensor measurement.
“If I took a batch of paper clips and started bending them back and forth they’ll break from fatigue damage at different intervals depending on the internal imperfections associated with the steel,” he explained. “Every real-world material and structure has imperfections that make it unique in terms of performance, so if the batch of paper clips take 21–30 cycles to break, what we would do today is after fifteen cycles throw the batch of paperclips away to be safe.”
The imperfections in AM parts are typically attributed to voids and geometric variance between the computer model and the print. The sensor technology Henry has been developing offers a way to track individual parts, predict failure points and replace them before they break.
“In order to create a high-trust situation, you take little risk, such as throwing the paper clip away after fifteen cycles even though the lowest lifetime in your test batch was twenty-one,” he noted. “If you try and take more risk and put the throw away limit at twenty-two cycles, then the paperclip may break on someone sometime but you will save money.”
The research team conducted an experimental validation set for assessing the real-time fatigue behaviour of metal additively manufactured maraging steel structures. Army researchers are now applying these findings to new studies on the AM of stainless steel parts and using machine-learning techniques, instead of sensors, to characterise the lifespan of parts.
“With 3D printing, you might not be able to replace a part with the exact same material,” Henry commented. “There is a cost and time benefit with 3D printing that perhaps warrants using it anyway. Imagine a situation where you always chose the strongest material but there was another material that was cheaper and easier to get; however, you need to prove that this other material can be depended on.”
“This study is as much about understanding the specific performance of a 3D-printed material as it is about understanding our ability to monitor and detect performance and 3D-printed material degradation,” he concluded.
The paper’s co-authors are Dr Francis Phillips and Dr Dan Cole, CCDC Army Research Laboratory; Dr Ed Garboczi, National Institute of Standards and Technology; Dr Robert Haynes, Combat Capabilities Development Command Aviation and Missile Center and Dr Terrence Johnson, Johns Hopkins University.