LPBF Right the First Time—the Right Mix Between Modeling and Experiments
Laser powder bed fusion (LPBF) is an additive manufacturing process with many adjustable input parameters that directly affect manufacturability and quality of the final product. The selection of the optimal input parameters makes the process qualification and part certification a costly and time-consuming task if performed using the traditional sequential and empirical approach.
Within the scope of the DARPA open manufacturing program, a rapid qualification framework is developed that relies on parallel multi-physics modeling and experimental efforts for verification and validation of the process input parameters during process development and material characterization. Product manufacturability is tested a priori via modeling and in-process monitoring is deployed to ensure input parameters are rapidly screened, and an optimal process window is selected. Process consistency and repeatability is further ensured through process characterization, process qualification, and via quantitative analysis of digital In-Process Quality Metrics™ (IPQM®s).
This paper discusses the rapid qualification methodology, model validation, and the application of the framework towards manufacturing of a challenging part defined by AFRL. The combination of numerical predictions, experimental refinement, and in-process monitoring delivered the first print right at first trial. Distortions are within predictions, geometric accuracy is within expectations, and quantitative metallurgical analysis shows dense as-built material with properties expected to fulfill performance requirements. In-process monitoring results provide a quantitative, digital Quality Signature™ or Digital Quality Record™ of process consistency and product quality.
KeywordsMetal additive manufacturing Laser powder bed fusion Process modeling Validation Powder scale Residual stress Distortion Simulation Experimental validation Certification In-process monitoring Process development Process qualification ICME Uncertainty quantification
All authors contributed to the effort described in this article.
This study is financially supported by the DARPA Open Manufacturing program, USA.
Compliance with Ethical Standards
The results presented in this work are that of the team involved in the DARPA Open Manufacturing program. References to literature and results of other research teams are made neutrally to gain better understanding of the modeling algorithms and the implications for real-life applications.
- 3.(2014) Additive manufacturing strategic research agenda 2014. AM Platform, https://www.rm-platform.com/linkdoc/AM%20SRA%20-%20February%202014.pdf
- 5.C. Kamath B. Eldasher, GF Gallegos, WE King A Sisto (2013) Density of additively-manufactured, 316L SS parts using laser powder-bed fusion at powers up to 400 W. LLNL-TR-648000 Lawrence Livermore National LaboratoryGoogle Scholar
- 7.Hann DB, Iammi J, Folkes J (2011) A simple methodology for predicting laser-weld properties from material and laser parameters. J Phys D Appl Phys 44. https://doi.org/10.1088/0022-3727/44/44/445401
- 8.Megahed M, Mindt HW, N'Dri N, Duan HZ, Desmaison O (2016) Metal additive manufacturing process and residual stress modelling. Integr Mater Manuf Innov 5. https://doi.org/10.1186/s40192-016-0047-2
- 9.Attar E (2011) Simulation der selektiven Elektronenstrahlschmelzprozesse. PhD Thesis University of Erlangen-NurembergGoogle Scholar
- 16.Mindt HW, Megahed M, Peralta A, Neumann J (2015) DMLM models - numerical assessment of porosity. 22nd ISABE Conference, Oct. 25–30, Phoenix, AZ., USAGoogle Scholar
- 17.Mindt HW, Megahed M, Shula B, Peralta AD, Neumann J (2016) Powder bed models - numerical assessment of as-built quality. AIAA ,SciTech, 4-8 January, San Diego. https://doi.org/10.2514/6.2016-1657
- 18.Mindt HW, Megahed M, Lavery NP, Holmes MA, Brown SGR (2016) Powder bed layer charateristics: the overseen first-order process input. Metall Mater Trans A 47(8). https://doi.org/10.1007/s11661-016-3470-2
- 21.Denlinger ER, Heigel JC, Michaleris P (2014) Residual stress and distortion modeling of electron beam direct manufacturing Ti-6Al-4V. J Eng Manuf 1:1–11Google Scholar
- 23.Keller N, Ploshikhin V (2014) Fast numerical predictions of residual stress and distortion of AM parts. 1st International Symposium on Material Science and Technology of Additive Manufacturing, Bremen, GermanyGoogle Scholar
- 24.Neugebauer F, Keller N, Ploshikhin V, Feuerhahn F, Köhler H (2014) Multi scale FEM simulation for distortion calculation in additive manufacturing of hardening stainless steel. International workshop on thermal forming an welding distortion, Bremen, GermanyGoogle Scholar
- 25.Desmaison O, Pires PA, Levesque G, Peralta A, Sundarraj S, Makinde A, et al. (May 21–25, 2017) Influence of computational grid and deposit volume on residual stress and distortion prediction accuracy for additive manufacturing modeling. 4th World congress on integrated computational materials engineering - ICME 2017, Ypsilanti, Mi, USAGoogle Scholar
- 28.(2011) ATI 718 Plus alloy data sourcebook. : Revision 1.2, ATI AllvacGoogle Scholar
- 30.Chinesta F, Keunings R, Leygue A. (2014) The proper generalized decomposition for advanced numerical simulations. A primer. Cham Heidelberg New York Dordrecht London: SpringerGoogle Scholar
- 33.Boying TB, Grathwohl P (2001) Tracer diffusion coefficients in sedimentary rocks: correlations to porosity and hydraulic conductivity. J Contam Hydrol 53(1–2):85–100Google Scholar
- 34.Western Electric Company (1956) Statistical quality control handbook, 1st edn. Western Electric Co., IndianapolisGoogle Scholar