ADVANCED MATERIALS & PROCESSES | JULY/AUGUST 2024 72 3D PRINTSHOP PREVENTING AND CORRECTING DEFECTS IN REAL TIME Keyhole formations that appear during powder bed fusion are difficult to spot during the process, but researchers from Johns Hopkins Applied Physics Laboratory (APL) developed a method to identify and repair them before they fully form. Powder bed fusion uses lasers to melt metal powders and solidify them into complex geometries, however, when the lasers deposit too much energy too quickly into the melted metal or melt pool, tiny bubbles of vapor form and become trapped as the metal cools, weakening a part’s structural integrity. The researchers hypothesized that these keyhole defects were occurring during transitional states. If they could pause the depositing laser just before the anomaly began to form, then the molten metal could cool long enough to settle and close the vapor depression, preventing bubble formation. “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,” says Steve Storck, project manager and chief scientist for manufacturing technologies. “To eliminate keyhole defects, we need to be able to detect and prevent them in real time, but this all happens exceptionally fast,” adds Storck. The team ran a simulation using computational fluid dynamics to determine that response times faster than 10 to 20 microseconds were required to identify a thermal disruption, augment the process, and let the molten pool cool slightly without a defect forming. Collaborating with other Johns Hopkins teams, they created new highspeed sensors, a control framework that communicates between the sensor and laser, and a high-speed field programmable gate array to build a system with the ability to respond in a mere 952 nanoseconds—faster than the blink of an eye. The team plans to incorporate artificial intelligence into the process to speed up the feedback loop and more accurately indicate where and how the defects are forming. jhu.edu. BONE-LIKE MATERIAL CREATED IN THE LAB Researchers at the University of Illinois Urbana-Champaign have used machine learning, optimization, 3D printing, and stress experiments to create a material that mimics bone. The group started with a materials database and used a virtual growth stimulator and machine learning algorithms to generate a virtual material, learning the relationship between its structure and physical properties. “What separates this work from past studies is that we took things a step further by developing a computational optimization algorithm to maximize both the architecture and stress distribution we can control,” says Shelly Zhang, civil and environmental engineering professor. In the lab, Zhang’s team used 3D printing to fabricate a full-scale resin prototype of the new bio-inspired material and attached it to a synthetic model of a fractured human femur. “Having a tangible model allowed us to run real-world measurements, test its efficacy, and confirm that it is possible to grow a synthetic material in a way analogous to how biological systems are built,” Zhang says. “We envision this work helping to build materials that will stimulate bone repair by providing optimized support and protection from external forces.” cee.illinois.edu. A model view of a keyhole defect forming. The slide farthest to the right shows vapor trapped within the cooling metal. Courtesy of Johns Hopkins APL. Researchers show their 3D-printed resin prototype, here attached to a synthetic model of a fractured human femur. Courtesy of Fred Zwicky.
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