Abstract
Additive manufacturing (AM) is a technology that produces a part layer by layer based on the computer-aided designed (CAD) model. Each AM process is defined by a set of parameters and materials. The laser power, scan spacing and speed, preheating and bed temperatures, hatch length, pulse frequency, and part placement (coordinates of a part placed in the build) are among the most studied process parameters reported in the literature. Recent attention to improving part quality is caused by the possibility of using AM for manufacturing, but the inconsistency of results’ repeatability is the main challenge that is not solved yet. This work attempts to improve the dimensional accuracy by predicting dimensional features of the part, namely length, width, and thickness. Data is collected from two identical runs done on EOS P395 polymer laser sintering system. By identical runs is meant that build layout, material and process parameters were kept constant in both runs. Pearson correlation test is used to identify whether the new parameters (the number of mesh triangles, surface, and volume of CAD model) are significantly correlated to dimensional features. Based on the correlation results, linear regression models are developed to predict dimensional features (compensate shrinkage effect). The obtained results are the following: models for thickness (in XZY orientation), length (in ZYX orientation), and length and thickness (in Angle orientation) can already be used to predict dimensional features (to minimize shrinkage effect by proposing scaling ratio for each specimen in the build separately).
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References
Baturynska I, Semeniuta O, Martinsen K (2018) Optimization of process parameters for powder bed fusion additive manufacturing by combination of machine learning and finite element method: a conceptual framework. Procedia CIRP 67C:227–232
Casalino G, Campanelli S, Contuzzi N, Ludovico A (2015) Experimental investigation and statistical optimisation of the selective laser melting process of a maraging steel. Opt Laser Technol 65:151–158
Caulfield B, McHugh P, Lohfeld S (2007) Dependence of mechanical properties of polyamide components on build parameters in the sls process. J Mater Process Technol 182(1):477–488
De Ciurana J, Serenóa L, Vallès È (2013) Selecting process parameters in reprap additive manufacturing system for pla scaffolds manufacture. Procedia CIRP 5:152–157
Delgado J, Ciurana J, Rodríguez CA (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(5–8):601–610
DIN 16742:2013 (2013) Plastics mouldings: tolerances and acceptance conditions. Standard, German Institute for Standardization
Dingal S, Pradhan T, Sundar JS, Choudhury AR, Roy S (2008) The application of taguchi’s method in the experimental investigation of the laser sintering process. Int J Adv Manuf Technol 38(9-10):904–914
Hur SM, Choi KH, Lee SH, Chang PK (2001) Determination of fabricating orientation and packing in sls process. J Mater Process Technol 112(2):236–243
ISO/ASTM 52921:2013(E) (2013) Standard terminology for additive manufacturing: coordinate systems and test methodologies. Standard, ISO/ASTM International
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830
Rüsenberg S, Josupeit S, Schmid HJ (2014) A method to characterize the quality of a polymer laser sintering process. Adv Mech Eng 6:185,374
Senthilkumaran K, Pandey PM, Rao P (2009) Influence of building strategies on the accuracy of parts in selective laser sintering. Mater Des 30(8):2946–2954
Senthilkumaran K, Pandey PM, Rao P (2009) New model for shrinkage compensation in selective laser sintering. Virtual and Physical Prototyping 4(2):49–62
Shah P, Racasan R, Bills P (2016) Comparison of different additive manufacturing methods using computed tomography. Case Studies in Nondestructive Testing and Evaluation 6:69–78. https://doi.org/10.1016/j.csndt.2016.05.008, special Issue: Industrial computed tomography
Singh S, Sharma VS, Sachdeva A (2012) Optimization and analysis of shrinkage in selective laser sintered polyamide parts. Mater Manuf Process 27(6):707–714
Wohlers T (2016) Wohlers report 2016. Wohlers Associates, Inc
Yang HJ, Hwang PJ, Lee SH (2002) A study on shrinkage compensation of the sls process by using the taguchi method. Int J Mach Tools Manuf 42(11):1203–1212
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This research is funded by Norwegian Research Council as a part of MKRAM project.
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Baturynska, I. Statistical analysis of dimensional accuracy in additive manufacturing considering STL model properties. Int J Adv Manuf Technol 97, 2835–2849 (2018). https://doi.org/10.1007/s00170-018-2117-4
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DOI: https://doi.org/10.1007/s00170-018-2117-4