Abstract
Selective laser melting (SLM) is an additive manufacturing process that builds a complex three-dimensional part, layer-by-layer, using a laser beam to fuse fine metal powder together. The design freedom afforded by SLM comes associated with complexity. As the physical phenomena occur over a broad range of length and time scales, the computational cost of modeling the process is high. At the same time, the large number of parameters that control the quality of a part make experiments expensive. In this paper, we describe ways in which we can use data mining and statistical inference techniques to intelligently combine simulations and experiments to build parts with desired properties. We start with a brief summary of prior work in finding process parameters for high-density parts. We then expand on this work to show how we can improve the approach by using feature selection techniques to identify important variables, data-driven surrogate models to reduce computational costs, improved sampling techniques to cover the design space adequately, and uncertainty analysis for statistical inference. Our results indicate that techniques from data mining and statistics can complement those from physical modeling to provide greater insight into complex processes such as selective laser melting.
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References
Bourell DL, Leu MC, Rosen DW (2009) Roadmap for additive manufacturing - Identifying the future of freeform processing. The University of Texas at Austin
Bourell DL, Rosen DW, Leu MC (2014) The roadmap for additive manufacturing and its impact. 3D Print Addit Manuf 1:6–9
Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. CRC Press, Boca Raton
Bridson R (2007) Fast Poisson disk sampling in arbitrary dimensions. In: ACM SIGGRAPH 2007 sketches. ACM, New York
Delgado J, Ciurana J, Rodriguez C (2012) Influence of process parameters on part quality and mechanical properties for DMLS and SLM with iron-based materials. Int J Adv Manuf Technol 60:601–610
Eagar T, Tsai N (1983) Temperature-fields produced by traveling distributed heat-sources. Weld J 62:S346–S355
Ebden M (2008) Gaussian process for regression and classification: a quick introduction. arXiv:1505.02965, submitted May 2015
Fang KT, Li R, Sudjianto A (2005) Design and modeling for computer experiments. Chapman and Hall/CRC Press, Boca Raton
Gusarov AV, Yadoirtsev I, Bertrand P, Smurov I (2009) Model of radiation and heat transfer in laser-powder interaction zone at selective laser melting. J Heat Transf 131:072,101
Hall MA (2000) Correlation-based feature selection for discrete and numeric class machine learning. In: Proceedings of 17th international conference on machine learning. Morgan Kaufmann, San Francisco, pp 359–366
Hodge NE, Ferencz RM, Solberg JM (2014) Implementation of a thermomechanical model for the simulation of selective laser melting. Comput Mech 54:33–51
Inselberg A (2009) Parallel coordinates: visual multidimensional geometry and its applications. Springer, New York
Kamath C (2009) Scientific data mining: a practical perspective. Society for Industrial and Applied Mathematics (SIAM), Philadelphia
Kamath C, Cantú-Paz E (2001) Creating ensembles of decision trees through sampling. In: Proceedings of the 33rd symposium on the interface: computing science and statistics
Kamath C, El-dasher B, Gallegos GF, King WE, Sisto A (2014) Density of additively-manufactured, 316L SS parts using laser powder-bed fusion at powers up to 400 W. Int J Adv Manuf Technol 74:65–78
Kempen K, Thijs L, Yasa E, Badrossamay M, Verheecke W, Kruth JP (2011) Process optimization and microstructural analysis for selective laser melting of AlSi10Mg. In: Bourell D (ed) Proceedings of solid freeform fabrication symposium, vol 22. University of Texas at Austin, Austin, pp 484–495
Khairallah SA, Anderson A (2014) Mesoscopic simulation model of selective laser melting of stainless steel powder. J Mater Process Technol 214:2627–2636
King WE, Barth HD, Castillo VM, Gallegos GF, Gibbs JW, Hahn DE, Kamath C, Rubenchik AM (2014) Observation of keyhole-mode laser melting in laser powder-bed fusion additive manufacturing. J Mater Process Technol 214:2915–2925
Körner C, Attar E, Heinl P (2011) Mesoscopic simulation of selective beam melting processes. J Mater Process Technol 211:978–987
Kruth J, Badrossamay M, Yasa E, Deckers J, Thijs L, Van Humbeeck J (2010) Part and material properties in selective laser melting of metals. In: Proceedings of 16th international symposium on electromachining (ISEM XVI), Shanghai
Laohaprapanon A, Jeamwatthanachai P, Wongcumchang M, Chantarapanich N, Chantaweroad S, Sitthiseripratip K, Wisutmethangoon S (2012) Optimal scanning condition of selective laser melting processing with stainless steel 316l powder. Material and Manufacturing Technology Ii, Pts 1 and 2. Trans Tech Publications Ltd, Stafa-Zurich, pp 816– 820
Li Y, Gu D (2014) Parametric analysis of thermal behavior during selective laser melting additive manufacturing of aluminum alloy powder. Mater Des 63:856–867
Mitchell DP (1991) Spectrally optimal sampling for distribution ray tracing. Comput Graph 25(4):157–164
National Institute of Standards and Technology (2013) Measurement Science Roadmap for Metal-Based Additive Manufacturing. Tech. rep. National Institute of Standards and Technology
Oehlert GW, Freeman WH (2000) A first course in design and analysis of experiments. Available from http://users.stat.umn.edu/gary/Book.html
Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. MIT Press, Cambridge
Rokach L (2010) Pattern classification using ensemble methods. World Scientific Publishing, Singapore
Rokach L, Maimon O (2014) Data mining with decision trees: theory and applications. World Scientific Publishing, Singapore
Spierings A, Levy G (2009) Comparison of density of stainless steel 316L parts produced with selective laser melting using different powder grades. In: Bourell D (ed) 20th annual international solid freeform fabrication symposium, an additive manufacturing conference. University of Texas at Austin, Austin, pp 342–353
Verhaeghe F, Craeghs T, Heulens J, Pandalaers L (2009) A pragmatic model for selective laser melting with evaporation. Acta Mater 57:6006–6012
Yadroitsev I (2009) Selective laser melting: direct manufacturing of 3D-objects by selective laser melting of metal powders. LAP Lambert Academic Publishing
Yadroitsev I, Gusarov A, Yadroitsava I, Smurov I (2010) Single track formation in selective laser melting of metal powders. J Mater Process Technol 210:1624–1631
Yadroitsev I, Smurov I (2010) Selective laser melting technology: from the single laser melted track stability to 3D parts of complex shape. Phys Procedia 5:551–560
Yasa E (2011) Manufacturing by combining selective laser melting and selective laser erosion / laser re-melting. Ph.D. thesis, Faculty of Engineering, Department of Mechanical Engineering. Katholieke Universiteit Leuven, Heverlee, Leuven. Available from Katholieke Universiteit Leuven
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Kamath, C. Data mining and statistical inference in selective laser melting. Int J Adv Manuf Technol 86, 1659–1677 (2016). https://doi.org/10.1007/s00170-015-8289-2
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DOI: https://doi.org/10.1007/s00170-015-8289-2