3D printing has significant potential to boost manufacturing, but defects in additively manufactured parts prevent widespread adoption. Johns Hopkins APL experts are addressing this problem by developing sensors capable of detecting and preventing these defects before they occur.
“Additive manufacturing allows you to create so many different structures that are optimized for specific applications,” said Vince Pagán, an experimental optics scientist and project manager at the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland. “But its Achilles’ heel is that the process generates defects that can cause parts to weaken and fail, and you can’t have that when they’re used in critical applications like national defense, biotechnology and aerospace.”
To solve this problem, experts at APL are developing sensors that can detect defects as they occur. This research is supported by the Hopkins Extreme Materials Institute, the Army Research Laboratory and the Office of Naval Research.
A common problem in additive manufacturing is the formation of keyhole defects during the powder bed fusion process. This can trap small vapor bubbles in the molten metal if the laser delivers too much energy too quickly, compromising structural integrity.
“If we can identify defect formation while still in the melt state, then we have the opportunity to repair these imperfections before they result in performance-limiting flaws,” said Morgan Trexler, who leads APL’s Science of Extreme and Multifunctional Materials program. “We are working to make manufacturing processes more intelligent, which will inherently lead to more rapid manufacturing and trusted components.”
“We can identify rocks below the surface of rivers from space, not because we can actually see them directly, but because we can see rapids where the water flow is disrupted,” explained Steve Storck, project manager and chief scientist for manufacturing technologies in APL’s Research and Exploratory Development Department. “Similarly, if a pore is about to form in a part, then the thermal flow around it will be disrupted, which indicates a defect in the formation process. If we can measure that temperature and spectral anomalies accurately and rapidly, we should be able to tell if something is forming in, underneath or adjacent to the active melting location.”
Through simulations using computational fluid dynamics, Li Ma, senior engineer and expert in additive manufacturing process modeling, was able to determine that response times of less than 20 microseconds are required to detect thermal disturbances and adjust the process. This made it possible to switch off the laser in time to prevent the formation of defects.
The researchers developed a high-speed sensor that records spectral and temperature data in high spatial and temporal resolution. This enabled real-time control of the laser within microseconds.
“To eliminate keyhole defects, we need to be able to detect and prevent them in real time, but this all happens exceptionally fast,” said Storck. “In the additive manufacturing process, solidification happens about one to three thousand times faster than during traditional processes, which means conventional sensing and control methods would not work. This drove us to develop custom methods.”
The team plans to integrate artificial intelligence into the process to speed up the feedback loop and improve defect detection. This should enable the production of components that are ready for use directly from the printing process.
Through these advances in defect detection and process control, additive manufacturing promises to produce more reliable, high-quality components faster and more cost-effectively.
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