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Johns Hopkins detects AM defects within nanoseconds

The rapid response system, which can act within 10 to 20 millionths of a second, is based on technology adapted from missile defense systems

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Additive manufacturing stands to revolutionize the industrial landscape. However, according to Johns Hopkins, a major challenge with the technology is the occurrence of defects during the fabrication process, which can compromise the strength and reliability of the components. Researchers at the Johns Hopkins Applied Physics Laboratory (APL) are addressing this issue by developing advanced sensors capable of detecting these defects in real-time, during the manufacturing process itself.

The APL team, led by Vince Pagán and Morgan Trexler, has made significant strides in tackling defects that arise during powder bed fusion. One common problem during this process is the formation of keyhole defects – tiny vapor bubbles trapped within the solidifying metal, weakening its structural integrity. These defects occur when the laser imparts excessive energy too quickly – creating instabilities in the melted metal.

Drawing analogies from nature, like identifying submerged rocks in rivers by observing surface disruptions, the APL team developed a method to detect potential defects by monitoring thermal and spectral anomalies during the manufacturing process. They hypothesized that by pausing the laser briefly when these anomalies are detected, the metal can cool sufficiently to prevent the formation of vapor bubbles.

Johns Hopkins detects AM defects within nanoseconds - thanks to technology adapted from missile defense systems.
A model view of a keyhole defect forming. The slide that is farthest to the right shows vapor trapped within the cooling metal. APL is aiming to prevent defects like this through the development of novel sensing methods that detect the anomalies before they fully form. Credit: Johns Hopkins APL.

The breakthrough came from developing custom sensors that could respond in microseconds – crucially fast given that in AM, materials solidify thousands of times faster than in traditional processes. These sensors, developed in collaboration with Mark Foster from Johns Hopkins University and his team, are equipped with photodiodes at multiple wavelengths and have enhanced sampling frequencies. This setup allows for the high-resolution capture of data on the dynamics of the melt pool – crucial for the early detection of defects.

In practice, the sensors are integrated into a control framework that communicates directly with the laser – instructing it to shut off momentarily if excessive heat is detected. This rapid response system, which can act within 10 to 20 millionths of a second, is based on technology adapted from missile defense systems. The agility of these systems in responding to data inputs is key to their success in preventing defects.

The APL team successfully demonstrated the system’s ability to respond in less than one microsecond – significantly faster than the physical processes it is monitoring. This capability allows the system to preemptively address potential defects, ensuring the integrity of the final product.

Looking forward, the team plans to incorporate artificial intelligence into the system to enhance the speed and accuracy of the feedback loop, potentially enabling real-time adjustments during the manufacturing process. This development promises to not only improve the reliability of additive manufacturing in producing defect-free components but also sets the stage for a broader adoption of this technology across various critical sectors.

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Edward Wakefield

Edward is a freelance writer and additive manufacturing enthusiast looking to make AM more accessible and understandable.

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