Accelerating process development for 3D printing of new metal alloys

被引:0
作者
David Guirguis
Conrad Tucker
Jack Beuth
机构
[1] Carnegie Mellon University,Next Manufacturing Center
[2] Carnegie Mellon University,Mechanical Engineering Department
[3] Carnegie Mellon University,Machine Learning Department
来源
Nature Communications | / 15卷
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摘要
Addressing the uncertainty and variability in the quality of 3D printed metals can further the wide spread use of this technology. Process mapping for new alloys is crucial for determining optimal process parameters that consistently produce acceptable printing quality. Process mapping is typically performed by conventional methods and is used for the design of experiments and ex situ characterization of printed parts. On the other hand, in situ approaches are limited because their observable features are limited and they require complex high-cost setups to obtain temperature measurements to boost accuracy. Our method relaxes these limitations by incorporating the temporal features of molten metal dynamics during laser-metal interactions using video vision transformers and high-speed imaging. Our approach can be used in existing commercial machines and can provide in situ process maps for efficient defect and variability quantification. The generalizability of the approach is demonstrated by performing cross-dataset evaluations on alloys with different compositions and intrinsic thermofluid properties.
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[21]  
Pistorius PC(2021)Process performance evaluation and classification via in-situ melt pool monitoring in directed energy deposition CIRP J. Manuf. Sci. Technol. 35 117550-6009
[22]  
Beuth JL(2022)Monitoring and prediction of porosity in laser powder bed fusion using physics-informed meltpool signatures and machine learning J. Mater. Process. Technol. 304 1800136-2925
[23]  
du Plessis A(2018)Machine-learning-based monitoring of laser powder bed fusion Adv. Mater. Technol. 3 640-484
[24]  
Yadroitsava I(2019)Investigation of deep learning for real-time melt pool classification in additive manufacturing IEEE Int. Conf. Autom. Sci. Eng. 2019 101470-8
[25]  
Yadroitsev I(2020)Observing molten pool surface oscillations during keyhole processing in laser powder bed fusion as a novel method to estimate the penetration depth Addit. Manuf. 36 2627-622
[26]  
Gaikwad A(2014)Mesoscopic simulation model of selective laser melting of stainless steel powder J. Mater. Process. Technol. 214 36-39
[27]  
Scime L(2016)Laser powder-bed fusion additive manufacturing: physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones Acta Mater. 108 1080-240
[28]  
Beuth J(2020)Critical instability at moving keyhole tip generates porosity in laser melting Science 370 064054-824
[29]  
Gaikwad A(2019)Effect of laser-matter interaction on molten pool flow and keyhole dynamics Phys. Rev. Appl. 11 1-2605
[30]  
Hooper PA(2022)Keyhole fluctuation and pore formation mechanisms during laser powder bed fusion additive manufacturing Nat. Commun. 13 5999-661