Feedforward control of thermal history in laser powder bed fusion: Toward physics-based optimization of processing parameters

被引:37
作者
Riensche, Alex [1 ,2 ]
Bevans, Benjamin D. [1 ,2 ]
Smoqi, Ziyad [2 ]
Yavari, Reza [2 ]
Krishnan, Ajay [3 ]
Gilligan, Josie [4 ]
Piercy, Nicholas [2 ]
Cole, Kevin [2 ]
Rao, Prahalada [1 ,2 ]
机构
[1] Virginia Tech, Grad Dept Ind & Syst Engn, Blacksburg, VA 24061 USA
[2] Univ Nebraska, Mech & Mat Engn, Lincoln, NE USA
[3] Edison Welding Inst, Addit Mfg, Columbus, OH USA
[4] Lincoln High Sch, Lincoln, NE USA
基金
美国国家科学基金会;
关键词
Feedforward process control; Laser powder bed fusion; Thermal history simulations; Graph theory; Physics -based parameter optimization; ENERGY DENSITY; TEMPERATURE; STRENGTH; STRATEGY; NEEDS;
D O I
10.1016/j.matdes.2022.111351
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
We developed and applied a model-driven feedforward control approach to mitigate thermal-induced flaw formation in laser powder bed fusion (LPBF) additive manufacturing process. The key idea was to avert heat buildup in a LPBF part before it is printed by adapting process parameters layer-by-layer based on insights from a physics-based thermal simulation model. The motivation being to replace cumbersome empirical build-and-test parameter optimization with a physics-guided strategy. The approach consisted of three steps: prediction, analysis, and correction. First, the temperature distribution of a part was predicted rapidly using a graph theory-based computational thermal model. Second, the model-derived thermal trends were analyzed to isolate layers of potential heat buildup. Third, heat buildup in affected layers was corrected before printing by adjusting process parameters optimized through iterative simulations. The effectiveness of the approach was demonstrated experimentally on two separate build plates. In the first build plate, termed fxed processing, ten different nickel alloy 718 parts were produced under constant processing conditions. On a second identical build plate, called con-trolled processing, the laser power and dwell time for each part was adjusted before printing based on thermal simulations to avoid heat buildup. To validate the thermal model predictions, the surface tem-perature of each part was tracked with a calibrated infrared thermal camera. Post-process the parts were examined with non-destructive and destructive materials characterization techniques. Compared to fixed processing, parts produced under controlled processing showed superior geometric accuracy and resolu-tion, finer grain size, increased microhardness, and reduced surface roughness.(c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
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页数:26
相关论文
共 65 条
[1]   Process parameter selection and optimization of laser powder bed fusion for 316L stainless steel: A review [J].
Ahmed, N. ;
Barsoum, I. ;
Haidemenopoulos, G. ;
Abu Al-Rub, R. K. .
JOURNAL OF MANUFACTURING PROCESSES, 2022, 75 :415-434
[2]   On the limitations of Volumetric Energy Density as a design parameter for Selective Laser Melting [J].
Bertoli, Umberto Scipioni ;
Wolfer, Alexander J. ;
Matthews, Manyalibo J. ;
Delplanque, Jean-Pierre R. ;
Schoenung, Julie M. .
MATERIALS & DESIGN, 2017, 113 :331-340
[3]   Discrete Green's functions and spectral graph theory for computationally efficient thermal modeling [J].
Cole, Kevin D. ;
Riensche, Alex ;
Rao, Prahalada K. .
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER, 2022, 183
[4]   Computational heat transfer with spectral graph theory: Quantitative verification [J].
Cole, Kevin D. ;
Yavari, M. Reza ;
Rao, Prahalada K. .
INTERNATIONAL JOURNAL OF THERMAL SCIENCES, 2020, 153
[5]   Additive manufacturing of metallic components - Process, structure and properties [J].
DebRoy, T. ;
Wei, H. L. ;
Zuback, J. S. ;
Mukherjee, T. ;
Elmer, J. W. ;
Milewski, J. O. ;
Beese, A. M. ;
Wilson-Heid, A. ;
De, A. ;
Zhang, W. .
PROGRESS IN MATERIALS SCIENCE, 2018, 92 :112-224
[6]  
Diegel O, 2019, A practical guide to design for additive manufacturing, P978
[7]   A review of critical repeatability and reproducibility issues in powder bed fusion [J].
Dowling, L. ;
Kennedy, J. ;
O'Shaughnessy, S. ;
Trimble, D. .
MATERIALS & DESIGN, 2020, 186
[8]   Process optimization of complex geometries using feed forward control for laser powder bed fusion additive manufacturing [J].
Druzgalski, C. L. ;
Ashby, A. ;
Guss, G. ;
King, W. E. ;
Roehling, T. T. ;
Matthews, M. J. .
ADDITIVE MANUFACTURING, 2020, 34
[9]   Heterogeneous sensing and scientific machine learning for quality assurance in laser powder bed fusion - A single-track study [J].
Gaikwad, Aniruddha ;
Giera, Brian ;
Guss, Gabriel M. ;
Forien, Jean-Baptiste ;
Matthews, Manyalibo J. ;
Rao, Prahalada .
ADDITIVE MANUFACTURING, 2020, 36
[10]  
Gouge M, 2018, THERMO-MECHANICAL MODELING OF ADDITIVE MANUFACTURING, P19, DOI 10.1016/B978-0-12-811820-7.00003-3