AI uncovers a novel method to enhance titanium alloys and accelerate production.

AI is transforming titanium alloy manufacturing, making it faster, stronger, and more precise. Traditionally, producing high-performance titanium components—used in spacecraft, submarines, and medical devices—was slow and resource-intensive. Even with advanced 3D printing, extensive testing was required to fine-tune manufacturing conditions.

A team from Johns Hopkins Applied Physics Laboratory (APL) and the Whiting School of Engineering is changing that by integrating AI-driven techniques. Their approach accelerates production while enhancing material strength, a breakthrough with implications for aerospace, defense, and medical industries.

“The nation urgently needs faster manufacturing to meet evolving challenges,” said Morgan Trexler, program manager for Extreme and Multifunctional Materials at APL. “We’re advancing laser-based additive manufacturing to develop mission-ready materials at speed.”

AI Unveils New Possibilities for Titanium 3D Printing in Additive Manufacturing

Published in Additive Manufacturing, the team’s findings focus on Ti-6Al-4V, a widely used titanium alloy prized for its strength and low weight. Using AI-driven models, researchers mapped out previously unexplored conditions for laser powder bed fusion, a 3D-printing method. Their results challenge long-held assumptions, revealing a broader processing window for creating dense, high-quality titanium with customizable properties.

“For years, we thought certain processing settings were ‘off-limits’ because they compromised quality,” said Brendan Croom, a senior materials scientist at APL. “AI allowed us to explore a much wider range, leading to faster printing while maintaining—or even improving—strength and flexibility.”

These advancements have major implications for industries relying on high-performance titanium. Stronger, lighter, and faster-to-produce components could revolutionize shipbuilding, aviation, and medical device manufacturing. The work also aligns with broader efforts to refine additive manufacturing for space and defense applications.

At the Whiting School of Engineering, researchers like Somnath Ghosh are integrating AI-driven simulations to predict how 3D-printed materials will perform under extreme conditions. Ghosh co-leads a NASA Space Technology Research Institute focused on using advanced computational models to speed up material qualification and certification. This effort complements APL’s mission to accelerate titanium manufacturing.

A Major Leap Forward

APL has long worked to refine additive manufacturing. When Steve Storck, chief scientist for manufacturing technologies at APL, joined in 2015, he saw its limitations. “One of the biggest barriers for the Department of Defense was material availability,” he said. “Titanium was one of the few optimized for 3D printing, but we needed to expand the range and improve processing parameters.”

APL developed a rapid material optimization framework, leading to a 2020 patent. By 2021, the team published a study on how defects affect mechanical properties. That groundwork enabled their latest breakthrough: using machine learning to explore an unprecedented range of processing conditions—something impractical with traditional trial-and-error methods.

Their AI-driven approach revealed a high-density processing regime once dismissed due to concerns about material instability. By making targeted adjustments, they unlocked new ways to process Ti-6Al-4V, surpassing previous performance limits.

“We’re not just making small improvements,” Storck said. “We’re discovering entirely new processing methods that push performance beyond what was thought possible.”

AI Unlocks Hidden Patterns

Material properties depend on processing factors like laser power, scan speed, and track spacing. Traditionally, finding the right combination was a slow process of manual adjustments. Instead, the team used Bayesian optimization, a machine learning technique that predicts the best next experiment based on prior data.

By analyzing early test results and refining predictions with each iteration, AI quickly identified optimal processing conditions. This allowed researchers to virtually test thousands of configurations before selecting a few for physical trials. The results overturned long-standing beliefs about which laser settings produce the best material properties.

“This isn’t just about faster production,” Croom said. “It’s about balancing strength, flexibility, and efficiency. AI helps us explore processing regions we wouldn’t have considered.”

Storck emphasized that their approach customizes materials for specific needs rather than relying on one-size-fits-all solutions. “Whether it’s a submarine operating in the Arctic or a flight component enduring extreme conditions, we can now optimize materials for each unique challenge while maintaining peak performance.”

Expanding the machine learning model to predict even more complex behaviors is a key goal. While the team’s initial work focused on density, strength, and flexibility, they aim to model factors like fatigue resistance and corrosion.

“This research demonstrates AI’s power in data-driven manufacturing,” Croom said. “It used to take years to understand how new materials perform in real-world environments. Now, we can gain that knowledge in weeks and use it to manufacture superior alloys rapidly.”

The success of this AI-driven approach opens doors for broader applications. While the study focused on titanium, the same methods apply to other metals and manufacturing techniques, including alloys designed specifically for 3D printing.

The Future of Manufacturing

One promising area for future research is in situ monitoring—real-time tracking and adjustments during the manufacturing process. Storck envisions a future where metal 3D printing is as seamless as home 3D printing: “We see a paradigm shift where additive manufacturing systems self-adjust as they print, ensuring perfect quality without extensive post-processing.”

By harnessing AI and high-throughput testing, this breakthrough is setting the stage for the next generation of high-performance materials, with far-reaching benefits across industries.

Read Original Article: TechXplore

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