Manufacturing parts using titanium alloys is complex and expensive, requires a lot of patience and users make only slow progress. Even modern technologies such as metal 3D printing can only help to a limited extent. Currently, trial and error is the main approach utilized in order to gradually identify the optimum manufacturing conditions. However, titanium parts are of great value in the aerospace, defense and maritime sectors. It is therefore important to speed up the production of these parts in order to meet demand more quickly and reduce costs. That is why researchers at the Johns Hopkins Applied Physics Laboratory (APL) and the Whiting School of Engineering have been searching for a solution. As such, they have developed a new technology that enables fast, stable and precise processing of titanium parts for additive manufacturing using AI.
In recent APL research, AI has been used in many different ways in various fields and examined for its potential, but also to a certain extent for its risks and side effects. The study “Machine learning enabled discovery of new L-PBF processing domains for Ti-6Al-4V,” which was published in Additive Manufacturing in December 2024, covers the opportunities for AI in process control and optimization. As the title of the study suggests, the research team focused on the titanium alloy Ti-6AI-4V, which is valued in numerous industries for its high strength and low weight. The aim of the research was to create optimal conditions for faster processing of the alloy and thus obtain precise, strong end parts.
Brendan Croom, a senior materials scientist at Johns Hopkins APL, in the lab (photo credits: Johns Hopkins APL/Ed Whitman)
AI Goes Beyond the Processing Limits of Titanium Alloys
As with all materials, processing conditions have an influence on the material properties. Laser power, scanning speed, etc. can determine how the material hardens and whether it is ultimately solid, flexible or brittle. The correct configuration of the process parameters is therefore largely responsible for the quality of the end parts. This is only possible as well through constant trial and error and adjustment.
To shorten this long procedure and save resources, researchers at APL and the Whiting School of Engineering have developed AI-driven models that identify unknown conditions in LPBF 3D printing. The AI is designed to find hidden patterns and suggest a promising approach for the follow-up attempt based on previous data. “This isn’t just about manufacturing parts more quickly,” says Brendan Croom, a senior materials scientist at APL. “It’s about striking the right balance among strength, flexibility and efficiency. AI is helping us explore processing regions we wouldn’t have considered on our own.”
According to the study, the AI was able to predict the best processing conditions based on the data analysis, which were first tested virtually and then implemented in the laboratory. The results show that the established boundaries need to be rethought and that AI offers completely new possibilities for processing and ultimately for application:
“For years, we assumed that certain processing parameters were ‘off-limits’ for all materials because they would result in poor-quality end product,” explained Croom. “But by using AI to explore the full range of possibilities, we discovered new processing regions that allow for faster printing while maintaining — or even improving — material strength and ductility, the ability to stretch or deform without breaking. Now, engineers can select the optimal processing settings based on their specific needs.”
The results of the study are promising in any case – especially for those industries that rely on high-performance titanium parts and benefit significantly from an increase in processing efficiency. These include aviation, aerospace, the defense sector and shipbuilding. Although only the aforementioned titanium alloy was tested as part of the study, the approach could also be of interest for processing other materials, including various AM alloys.
The research team will now refine its approach. By optimizing the machine learning model, it may be possible to predict even more complex material behavior. In addition, the team will also focus on other material properties, including density, strength, ductility, flammability resistance and corrosion. Nevertheless, the initial results can be interpreted as a success. “This work has clearly demonstrated the power of AI, high-throughput testing and data-driven manufacturing,” says Croom. You can find the full study HERE.
What do you think of the use of AI to push the limitations of titanium alloys in LPBF? Let us know in a comment below or on our LinkedIn, Facebook, and Twitter pages! Don’t forget to sign up for our free weekly Newsletter here, the latest 3D printing news straight to your inbox! You can also find all our videos on our YouTube channel.
*Cover Photo Credits: SciTechDaily.com