Abstract
Laser powder bed fusion (LPBF) is an increasingly popular approach for additive manufacturing (AM) of metals. However, parts produced by LPBF are prone to residual stresses, deformations, and other defects linked to nonuniform temperature distribution during the process. Several works have highlighted the important role (laser) scanning strategies, including laser power, scan speed, scan pattern and scan sequence, play in achieving uniform temperature distribution in LPBF. However, scan sequence continues to be determined offline based on trial-and-error or heuristics, which are neither optimal nor generalizable. To address these weaknesses, we present a framework for intelligent online scan sequence optimization to achieve uniform temperature distribution in LPBF. The framework involves the use of physics-based models for online optimization of scan sequence, while data acquired from in-situ thermal sensors provide correction or calibration of the models. The proposed framework depends on having: (1) LPBF machines capable of adjusting scan sequence in real-time; and (2) accurate and computationally efficient models and optimization approaches that can be efficiently executed online. The first challenge is addressed via a commercially available open-architecture LPBF machine. As a preliminary step towards tackling the second challenge, an analytical model is explored for determining the optimal sequence for scanning patterns in LPBF. The model is found to be deficient but provides useful insights into future work in this direction.
Laser-metal interaction dynamics during additive manufacturing resolved by detection of thermally-induced electron emission
