Layer-to-Layer Melt Pool Control in Laser Powder Bed Fusion

被引:0
|
作者
Liao-McPherson, Dominic [1 ]
Balta, Efe C. [2 ,3 ]
Afrasiabi, Mohamadreza [4 ,5 ]
Rupenyan, Alisa [6 ]
Bambach, Markus [5 ]
Lygeros, John [7 ]
机构
[1] Univ British Columbia, Dept Mech Engn, Vancouver, BC V6T 1Z4, Canada
[2] Swiss Fed Inst Technol, Control & Automat Grp, inspire AG, CH-8092 Zurich, Switzerland
[3] Swiss Fed Inst Technol, Automat Control Lab, CH-8092 Zurich, Switzerland
[4] Swiss Fed Inst Technol, Computat Mfg Grp, inspire AG, CH-8092 Zurich, Switzerland
[5] Swiss Fed Inst Technol, Adv Mfg Lab, CH-8092 Zurich, Switzerland
[6] ZHAW Zurich Univ Appl Sci, ZHAW Ctr Artificial Intelligence, CH-8400 Zurich, Switzerland
[7] Swiss Fed Inst Technol, Automatic Control Lab IfA, CH-8092 Zurich, Switzerland
基金
瑞士国家科学基金会;
关键词
Process control; Powders; Nonhomogeneous media; Mathematical models; Geometry; Numerical models; Power lasers; Physics; Optimization; Laser modes; Additive manufacturing (AM); control; laser powder bed fusion (LPBF); melt pool; simulation; ADDITIVE MANUFACTURING PROCESSES; ITERATIVE LEARNING CONTROL; SIMULATION; MODEL;
D O I
10.1109/TCST.2024.3464118
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Additive manufacturing (AM) processes are flexible and efficient technologies for producing complex geometries. However, ensuring reliability and repeatability is challenging due to the complex physics and various sources of uncertainty in the process. In this work, we investigate closed-loop control of the melt pool dimensions in a 2-D laser powder bed fusion (LPBF) process. We propose a trajectory optimization-based layer-to- layer (L2L) controller based on a linear parameter-varying (LPV) model that adjusts the laser power input to the next layer to track a desired melt pool depth and validate our controller by placing it in closed-loop high-fidelity multilayer smoothed particle hydrodynamics simulator of the 2-D LPBF process. Detailed numerical case studies demonstrate successful regulation of the melt pool depth on brick and overhang geometries and provide first of its kind results on the effectiveness of L2L input optimization for the LPBF process as well as detailed insight into the physics of the controlled process. Computational complexity and process performance results illustrate the method's effectiveness and provide an outlook for its implementation onto real systems.
引用
收藏
页码:207 / 218
页数:12
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