Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel
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Laser Powder-Bed Fusion (L-PBF) metal-based additive manufacturing (AM) is complex and not fully understood. Successful processing for one material, might not necessarily apply to a different material. This paper describes a workflow process that aims at creating a material data sheet standard that describes regimes where the process can be expected to be robust. The procedure consists of building a Gaussian process-based surrogate model of the L-PBF process that predicts melt pool depth in single-track experiments given a laser power, scan speed, and laser beam size combination. The predictions are then mapped onto a power versus scan speed diagram delimiting the conduction from the keyhole melting controlled regimes. This statistical framework is shown to be robust even for cases where experimental training data might be suboptimal in quality, if appropriate physics-based filters are applied. Additionally, it is demonstrated that a high-fidelity simulation model of L-PBF can equally be successfully used for building a surrogate model, which is beneficial since simulations are getting more efficient and are more practical to study the response of different materials, than to re-tool an AM machine for new material powder.
KeywordsAdditive manufacturing Laser powder-bed fusion 316L stainless steel Gaussian processes Bayesian statistics
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This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344, and was partially supported by an Early Stage Innovations grant from NASA’s Space Technology Research Grants Program, Grant No. NNX15AD71G. This work was also partially funded through a Laboratory Directed Research and Development (LDRD) grant, Grant No. 15-ERD-037. LLNL Release No. LLNL-JRNL-726754.
- 3.American Society of Testing Materials (2012) ASTM F2792 - 12a: standard terminology for additive manufacturing technologies. Standard, ASTM. [Online]. Available from http://www.astm.org/Standards/F2792.htm
- 4.Wohlers TT, Wohlers Associates, Campbell RI, Caffrey T (2016) Wohlers Report 2016: 3D Printing and Additive Manufacturing State of the Industry: Annual Worldwide Progress Report. Wohlers Associates, USA. ISBN 9780991333226Google Scholar
- 5.The Minerals Metals & Materials Society (TMS) (2015) Modeling across scales: a roadmapping study for connecting materials models and simulations across length and time scales. TMS, Warrendale. ISBN 9780692376065. www.tms.org/multiscalestudy
- 6.Frazier WE (2010) Direct digital manufacturing of metallic components: vision and roadmap. Direct digital manufacturing of metallic components: affordable, durable, and structurally efficient airframes, at Solomons Island. Austin, pp 9–11Google Scholar
- 7.National Institute of Standards and Technology (NIST) Measurement science roadmap for metal-based additive manufacturing, 2013 Online. Available from https://www.nist.gov/sites/default/files/documents/el/isd/NISTAdd_Mfg_Report_FINAL-2.pdf. Accessed 10 Jun 2015
- 8.Bourell DL, Leu MC, Rosen DW (2009) Roadmap for additive manufacturing: identifying the future of freeform processing. The University of Texas at Austin, AustinGoogle Scholar
- 20.Tapia G, Elwany AH, Sang H (2016) Prediction of porosity in metal-based additive manufacturing using spatial Gaussian process models. Addit Manuf 12:282–290Google Scholar
- 23.Mao R, Zhu H, Zhang L, Chen A (2006) A new method to assist small data set neural network learning. In: Sixth international conference on intelligent systems design and applications, ISDA06, 2006, vol 1. IEEE, New York, pp 17–22Google Scholar
- 24.O’Hagan A (2013) Polynomial chaos: a tutorial and critique from a statistician’s perspective. SIAM/ASA J Uncert Quantif 20:1– 20Google Scholar
- 27.Christen A, Sansó B (2008) Advances in the design of Gaussian processes as surrogate models for computer experiments. Technical report, Tech. Report 5, University of California, Santa Cruz CAGoogle Scholar
- 29.Conti S, Gosling JP, Oakley JE, O’Hagan A (2009) Gaussian process emulation of dynamic computer codes. Biometrika 96(3): 663–676Google Scholar
- 33.Higdon D, Gattiker J, Williams B, Rightley M (2008) Computer model calibration using high-dimensional output. J Amer Stat Assoc 103(482):570–583Google Scholar
- 35.Kamath C (2016) Data mining and statistical inference in selective laser melting. Int J Adv Manuf Technol 1–19Google Scholar
- 36.Tapia G, Elwany AH (2015) Prediction of porosity in SLM parts using a MARS statistical model and bayesian inference. In: Proceedings of the solid freeform fabrication symposium. Austin, pp 1205–1219Google Scholar
- 37.Stein ML (2012) Interpolation of spatial data: some theory for Kriging, Springer Science & Business Media, New YorkGoogle Scholar
- 38.Gong H, Gu H, Zeng K, Dilip JJS, Pal D, Stucker B, Christiansen D, Beuth J, Lewandowski JJ (2014) Melt pool characterization for selective laser melting of Ti-6Al-4V pre-alloyed powder. In: Proceedings of the solid freeform fabrication symposium. Austin, pp 256–267Google Scholar