A research team led by Professor Seungchul Lee from the Korea Advanced Institute of Science and Technology (KAIST), the nations first research-oriented graduate school in science and technology, and by Professor Hyoung Seop Kim from POSTECH, which had 900 researchers in 2024, utilized artificial intelligence to address the strength-ductility trade-off in Ti-6Al-4V alloy. By predicting mechanical properties and offering uncertainty data, the approach enabled the production of metal products with superior strength and ductility.In this study, by optimizing the 3D printing process parameters and heat treatment conditions, we were able to develop a high-strength, high-ductility Ti-6Al-4V alloy with minimal experimentation trials. Compared to previous studies, we produced an alloy with a similar ultimate tensile strength but higher total elongation, as well as that with a similar elongation but greater ultimate tensile strength, said Professor Lee.The study, published in Nature Communications, was funded by the National Research Foundation of Korea through a grant from the Korean government and by the Nano & Material Technology Development Program, supported by the Ministry of Science and ICT.Ph.D. Student Jaejung Park and Professor Seungchul Lee of KAIST, and Professor Hyoung Seop Kim of POSTECH, and M.S.Ph.D. Student Jeong Ah Lee of POSTECH. Photo via: KAIST.Study on Laser Powder Bed Fusion: New DiscoveriesLaser powder bed fusion is used to produce Ti-6Al-4V alloys, which are known for their strength and biocompatibility. However, creating 3D printed versions with both high strength and ductility has been challenging due to the wide range of potential process combinations.To address this, the research team developed an active learning framework that explores various 3D printing parameters and heat treatment conditions. The AI model predicts tensile strength and elongation while also evaluating the uncertainty of these predictions. The models recommendations are then validated through 3D printing and tensile testing to obtain further data on material properties.This new data is incorporated into additional AI model training. After five iterations of refinement, the optimal process parameters and heat treatment conditions were identified. The final 3D printed Ti-6Al-4V alloy achieved a tensile strength of 1190 MPa and an elongation of 16.5%, successfully overcoming the strength-ductility challenge.Professor Lee explained that applying the approach to other properties, like thermal conductivity and thermal expansion, would allow for more efficient exploration of 3D printing process parameters and heat treatment conditions.Active Learning Framework. Image via: KAIST.Artificial Intelligence in Additive ManufacturingArtificial intelligence is driving significant advancements in additive manufacturing. One such effort comes from 1000 Kelvin, a company focused on AI-driven additive manufacturing solutions, which introduced AMAIZE 2.0. This solution marks a major improvement in the metal laser powder bed fusion (LPBF) process, with the goal of boosting reliability, efficiency, and scalability for manufacturers in different industries.In another development, Dyndrite, a company specializing in 3D printing software, introduced the SMART Python script, which automates adherence to ASTM/ISO standards in additive manufacturing. This script integrates with Dyndrites LPBF Pro software to generate 3D printing layouts that align with industry standards, resulting in up to 99% labor savings and reduced human errors. Tasks that once took more than a week can now be finished in just a few minutes.What 3D printing trends should you watch out for in 2025?How is the future of 3D printing shaping up?To stay up to date with the latest 3D printing news, dont forget to subscribe to the 3D Printing Industry newsletter or follow us onLinkedin.While youre here, why not subscribe to our YouTube channel? Featuring discussion, debriefs, video shorts, and webinar replays.Featured image shows Ph.D. Student Jaejung Park and Professor Seungchul Lee of KAIST, and Professor Hyoung Seop Kim of POSTECH, and M.S.Ph.D. Student Jeong Ah Lee of POSTECH. Photo via: KAIST