Collaborative Research: Towards Intelligent Scan Sequence Generation to Reduce Local Overheating, Distortion, and Residual Stress in LPBF Additive Manufacturing

Laser powder bed fusion (LPBF) is increasingly being used to produce metallic parts in a variety of high-value industries, such as the aerospace, biomedical, and automotive industries. However, LPBF manufactured parts are prone to shape distortion and excessive heat or stress build up due to uneven temperatures across the part during the printing process, leading to cracks or other defects.

Prior research has shown that scan sequence (i.e., the order in which geometric features on the part are scanned by the laser) can help with homogenizing temperature distribution across a part, thus reducing distortion, overheating and excessive stress. However, scan sequence is currently determined based on trial-and-error or hands-on learning, leading to inconsistent and suboptimal results.

This project supports a scientific investigation into an approach for optimally determining scan sequence using printing process models. The knowledge created through this investigation will enable 3D printing of complex metallic parts with fewer failed or defective prints, thus improving the economic viability of lLPBF. The research will enrich an outreach program that actively engages middle school students in Detroit and inspires them to pursue careers in STEM fields.

The main objective of the project is to mathematically, numerically, and experimentally uncover the relationships between optimal scan sequences, temperature distribution, distortion, and residual stress in laser powder bed fusion using physics-based and data-driven thermal or thermomechanical models. The impacts of optimal scan sequences on microstructure and other part quality metrics will also be investigated.

These objectives will be achieved by: (1) incorporating advanced thermal effects into the determination of optimal scan sequences using data-driven models; (2) numerically investigating when optimal scan sequences generated using only thermal models do not adequately reduce distortion or residual stress; and (3) introducing thermomechanical effects into the determination of optimal scan sequences in cases where thermal models alone are deficient. The methods will be validated experimentally.

Translation of knowledge from this project to application may accelerate broader industry adoption of additive manufacturing.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.