AI framework overcomes the strength-ductility dilemma in additive-manufactured titanium alloys

February 25, 2025

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AI framework overcomes the strength-ductility dilemma in additive-manufactured titanium alloys

The KAIST analysis group led by Professor Seungchul Lee from the Division of Mechanical Engineering, in collaboration with Professor Hyoung Seop Kim's group at POSTECH, have efficiently overcome the power–ductility dilemma of Ti-6Al-4V alloys utilizing synthetic intelligence, enabling the manufacturing of excessive power, excessive ductility metallic merchandise.

The AI developed by the group precisely predicts mechanical properties primarily based on numerous 3D printing course of parameters whereas additionally offering uncertainty data, and it makes use of each to advocate course of parameters that maintain excessive promise for 3D printing. The research is printed in Nature Communications.

Amongst numerous 3D printing applied sciences, laser powder mattress fusion is an modern technique for manufacturing Ti-6Al-4V alloys, famend for his or her excessive power and bio-compatibility. Nonetheless, this alloy made through 3D printing has historically confronted challenges in concurrently attaining excessive power and excessive ductility.

Though there have been makes an attempt to deal with this challenge by adjusting each the printing course of parameters and warmth remedy circumstances, the huge variety of doable combos made it troublesome to discover all of them by way of experiments and simulations alone.

The lively studying framework developed by the group shortly explores a variety of 3D printing course of parameters and warmth remedy circumstances to advocate these anticipated to enhance each power and ductility of the alloy. These suggestions are primarily based on the AI mannequin's predictions of final tensile power and complete elongation together with related uncertainty data for every set of course of parameters and warmth remedy circumstances.

The advisable circumstances are then validated by performing 3D printing and tensile exams to acquire the true mechanical property values. These new knowledge are included into additional AI mannequin coaching, and thru iterative exploration, the optimum course of parameters and warmth remedy circumstances for producing high-performance alloys had been decided in solely 5 iterations.

With these optimized circumstances, the 3D printed Ti-6Al-4V alloy achieved an final tensile power of 1190 MPa and a complete elongation of 16.5%, efficiently overcoming the power–ductility dilemma.

Professor Seungchul Lee mentioned, "On this research, by optimizing the 3D printing course of parameters and warmth remedy circumstances, we had been capable of develop a high-strength, high-ductility Ti-6Al-4V alloy with minimal experimentation trials. In comparison with earlier research, we produced an alloy with the same final tensile power however larger complete elongation, in addition to that with the same elongation however better final tensile power.

"Moreover, if our method is utilized not solely to mechanical properties but in addition to different properties resembling thermal conductivity and thermal enlargement, we anticipate that it’s going to allow environment friendly exploration of 3D printing course of parameters and warmth remedy circumstances."

Extra data: Jeong Ah Lee et al, Lively studying framework to optimize course of parameters for additive-manufactured Ti-6Al-4V with excessive power and ductility, Nature Communications (2025). DOI: 10.1038/s41467-025-56267-1

Journal data: Nature Communications Supplied by The Korea Superior Institute of Science and Expertise (KAIST) Quotation: AI framework overcomes the strength-ductility dilemma in additive-manufactured titanium alloys (2025, February 25) retrieved 27 February 2025 from https://techxplore.com/information/2025-02-ai-framework-strength-ductility-dilemma.html This doc is topic to copyright. Aside from any truthful dealing for the aim of personal research or analysis, no half could also be reproduced with out the written permission. The content material is offered for data functions solely.

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