“We’re not just making incremental improvements,” says Steve Storck, Ph.D., chief scientist for additive manufacturing at APL. “We discovered processing conditions that pushed performance beyond what was thought possible. We’re finding entirely new ways to process these materials, unlocking capabilities that weren’t previously considered.”

Storck and his colleagues are focusing their R&D efforts on Ti-6Al-4V, a titanium alloy known for its high strength and low weight. They leveraged AI-driven models to map out previously unexplored manufacturing conditions for laser powder bed fusion, a popular method of metal 3D printing.

Their findings challenge long-held assumptions about process limits, revealing a broader processing window for producing dense, high-quality titanium with customizable mechanical properties.

“Producing Ti64 parts often requires significant material removal from a billet, resulting in a high buy-to-fly ratio, meaning much of the raw material is wasted and not used in the final product,” explains Storck. “While additive manufacturing provides a promising alternative to reduce material waste and the associated costs, it can be slower. [Our] recent work demonstrates one method to reduce the production time for near-net-shape-parts, making the process more efficient.”

The research revealed a high-density processing regime previously dismissed due to concerns about material instability. With targeted adjustments, the team unlocked new ways to process Ti-6Al-4V, which can be used for additive manufacturing applications.

“Laser powder bed fusion is a process in which a high-powered laser fuses layers of metal powder to produce dense, high-strength parts with complex geometries,” says Storck. “This technique is useful for applications requiring precision and performance.

“Titanium’s properties can be affected by the way the material is processed,” Storck points out. “Laser power, scan speed and spacing between laser tracks determine how the material solidifies—whether it becomes strong and flexible or brittle and flawed. Traditionally, finding the right combination required slow trial-and-error testing.”

When Storck began studying additive manufacturing a decade ago, materials availability was a challenge. “Each design required a specific material, but robust processing conditions didn’t exist for most of them,” he explains. “Titanium was one of the few that met Department of Defense needs and had been optimized to match or exceed traditional manufacturing performance. We knew we had to expand the range of materials and refine processing parameters to fully unlock additive manufacturing’s potential.”

Stock and his colleagues focused on defect control and material performance. Building on that groundwork, they used machine learning technology to explore a wide range of processing parameters.

“We’re leveraging AI tools to optimize the manufacturing process and improve materials performance,” says Storck. “Our findings hold promise for industries that rely on high-performance titanium parts. For instance, the ability to manufacture stronger, lighter components at greater speeds could improve efficiency in aviation, medical devices and shipbuilding.

“We envision a paradigm shift where future additive manufacturing systems can adjust as they print, ensuring perfect quality without the need for extensive post-processing, and parts that can be born qualified,” adds Storck.

For more information on titanium, read these articles:

• Aerospace Assembler Forms Titanium Parts.
• Hot Forming Titanium.
• Additive Manufacturing Helps Boom Supersonic Produce Complex Parts.