A focused simulation-based optimization of print time and material usage with respect to orientation, layer height and support settings for multi-pathological anatomical models in inverted vat photopolymerization 3D printing
Tóm tắt
3D printing of anatomical models requires multi-factorial decision making for optimal model manufacturing. Due to the complex nature of the printing process, there are frequently multiple potentialities based on the desired end goal. The task of identifying the most optimal combination of print control variables is inherently subjective and rests on sound operator intuition. This study investigates the effect of orientation, layer and support settings on print time and material usage. This study also presents a quantitative optimization framework to jointly optimize print time and material usage as a function of those settings for multi-pathological anatomical models. Seven anatomical models representing different anatomical regions (cardiovascular, abdominal, neurological and maxillofacial) were selected for this study. A reference cube was also included in the simulations. Using PreForm print preparation software the print time and material usage was simulated for each model across 4 orientations, 2 layer heights, 2 support densities and 2 support tip sizes. A 90–10 weighted optimization was performed to identify the 5 most optimal treatment combinations that resulted in the lowest print time (90% weight) and material usage (10% weight) for each model. The 0.1 mm layer height was uniformly the most optimal setting across all models. Layer height had the largest effect on print time. Orientation had a complex effect on both print time and material usage in certain models. The support density and the support tip size settings were found to have a relatively minor effect on both print time and material usage. Hollow models had a larger support volume fraction compared to solid models. The quantitative optimization framework identified the 5 most optimal treatment combinations for each model using a 90–10 weighting for print time and material usage. The presented optimization framework could be adapted based on the individual circumstance of each 3D printing lab and/or to potentially incorporate additional response variables of interest.
Tài liệu tham khảo
Mitsouras D, Liacouras P, Imanzadeh A, Giannopoulos AA, Cai T, Kumamaru KK, et al. Medical 3D printing for the radiologist. Radiographics. 2015;35(7):1965–88. https://doi.org/10.1148/rg.2015140320.
Lichtenberger JP, Tatum PS, Gada S, Wyn M, Ho VB, Liacouras P. Using 3D printing (additive manufacturing) to produce low-cost simulation models for medical training. Mil Med. 2018;183(suppl_1):73–7. https://doi.org/10.1093/milmed/usx142.
Melchels FPW, Feijen J, Grijpma DW. A review on stereolithography and its applications in biomedical engineering. Biomaterials. 2010;31(24):6121–30. https://doi.org/10.1016/j.biomaterials.2010.04.050.
Ravi P, Chepelev L, Lawera N. A systematic evaluation of medical 3D printing accuracy of multi-pathological anatomical models for surgical planning manufactured in elastic and rigid material using desktop inverted vat photopolymerization. Med Phys. 2021;48(6):1–11. https://doi.org/10.1002/mp.14850.
Khodaygan S, Golmohammadi AH. Multi-criteria optimization of the part build orientation (PBO) through a combined meta-modeling/NSGAII/TOPSIS method for additive manufacturing processes. Int J Interact Des Manuf. 2018;12(3):1071–85. https://doi.org/10.1007/s12008-017-0443-7.
Jaiswal P, Patel J, Rai R. Build orientation optimization for additive manufacturing of functionally graded material objects. Int J Adv Manuf Technol. 2018;96(1-4):223–35. https://doi.org/10.1007/s00170-018-1586-9.
Ravi P, Antoline S, Rybicki FJ. 3D printing of open-source respirators (including N95 respirators), surgical masks, and community mask designs to address COVID-19 shortages. In: 3D printing in medicine and its role in the COVID-19 pandemic: Springer; 2021. p. 91–106. https://doi.org/10.1007/978-3-030-61993-0_11.
Griffiths CA, Howarth J, De Almeida-Rowbotham G, et al. A design of experiments approach for the optimisation of energy and waste during the production of parts manufactured by 3D printing. J Clean Prod. 2016;139:74–85. https://doi.org/10.1016/j.jclepro.2016.07.182.
Jiang J, Ma Y. Path planning strategies to optimize accuracy, quality, build time and material use in additive manufacturing: a review. Micromachines. 2020;11(7). https://doi.org/10.3390/MI11070633.
Jiang J, Xu X, Stringer J. Optimization of process planning for reducing material waste in extrusion based additive manufacturing. Robot Comput Integr Manuf. 2019;59:317–25. https://doi.org/10.1016/j.rcim.2019.05.007.
Jiang J, Stringer J, Xu X. Support optimization for flat features via path planning in additive manufacturing. 3D print. Addit Manuf. 2019;6:171–9. https://doi.org/10.1089/3dp.2017.0124.
Kamio T, Hayashi K, Onda T, Takaki T, Shibahara T, Yakushiji T, et al. Utilizing a low-cost desktop 3D printer to develop a “one-stop 3D printing lab” for oral and maxillofacial surgery and dentistry fields. 3D Print Med. 2018;4:1–2. https://doi.org/10.1186/s41205-018-0028-5.
Rubayo DD, Phasuk K, Vickery JM, Morton D, Lin WS. Influences of build angle on the accuracy, printing time, and material consumption of additively manufactured surgical templates. J Prosthet Dent. 2020:1–6. https://doi.org/10.1016/j.prosdent.2020.09.012.
Salmi M, Paloheimo KS, Tuomi J, Wolff J, Mäkitie A. Accuracy of medical models made by additive manufacturing (rapid manufacturing). J Cranio-Maxillofacial Surg. 2013;41(7):603–9. https://doi.org/10.1016/j.jcms.2012.11.041.
Unkovskiy A, Bui PHB, Schille C, Geis-Gerstorfer J, Huettig F, Spintzyk S. Objects build orientation, positioning, and curing influence dimensional accuracy and flexural properties of stereolithographically printed resin. Dent Mater. 2018;34(12):e324–33. https://doi.org/10.1016/j.dental.2018.09.011.
Hada T, Kanazawa M, Iwaki M, Arakida T, Soeda Y, Katheng A, et al. Effect of printing direction on the accuracy of 3D-printed dentures using stereolithography technology. Materials (Basel). 2020;13(15):1–12. https://doi.org/10.3390/ma13153405.
Tahayeri A, Morgan MC, Fugolin AP, Bompolaki D, Athirasala A, Pfeifer CS, et al. 3D printed versus conventionally cured provisional crown and bridge dental materials. Dent Mater. 2018;34(2):192–200. https://doi.org/10.1016/j.dental.2017.10.003.