Abstract
Material extrusion is a well-recognized facet of additive manufacturing that involves the fabrication of parts through the deposition of structural material from an extrusion head from a bulk supply. In the subdivision of Direct Ink Writing (DIW) additive manufacturing, challenges arise when the structural material is flowable, synchronous extrusion control and tool movement becomes critical for achieving high-quality parts with low defect populations. DIW techniques are most used in laboratory settings using expensive custom instruments and may require specialized 3D slicing software. In this study, the fabrication of an inexpensive, consumer-friendly progressive cavity pump dispensing system is detailed, in which can create high-quality parts by executing G-code commands produced from a commercial slicing software. The precision and repeatability of the movement-synchronized material extrusion is demonstrated through a series of optimization schemes, entailing the alteration of various control parameters, which directly affect the extrusion properties demonstrated during a print. In situ diagnostics were implemented to evaluate the results of the established optimization experiment. Using a machine vision technique, images of the optimization prints are processed. Following this, a supervised machine learning model was trained to autonomously judge whether or not the extrusion parameters produced a passing or failing result. The machine learning scheme serves as a preliminary benchmark for future layer-by-layer evaluation of more complex DIW parts. The construction of the printer and development of in situ characterization capabilities demonstrates the ability for this printer to create high-fidelity DIW parts for a fraction of the price of other systems.
Similar content being viewed by others
References
Gibson I, Rosen DW, Stucker B, Khorasani M, Rosen D, Stucker B, Khorasani M (2021) Additive Manufacturing Technologies. Springer, vol. 17
Toombs JT, Luitz M, Cook CC, Jenne S, Li CC, Rapp BE, Kotz-Helmer F, Taylor HK (2022) Volumetric Additive Manufacturing of Silica Glass with Microscale Computed Axial Lithography. Science 376(6590):308–312
Kelly BE, Bhattacharya I, Heidari H, Shusteff M, Spadaccini CM, Taylor HK (2019) Volumetric Additive Manufacturing via Tomographic Reconstruction. Science 363(6431):1075–1079
Shusteff M, Browar AE, Kelly BE, Henriksson J, Weisgraber TH, Panas RM, Fang NX, Spadaccini CM (2017) One-step Volumetric Additive Manufacturing of Complex Polymer Structures. Sci Adv 3(12):eaao5496
Li L, Lin Q, Tang M, Duncan AJ, Ke C (2019) Advanced Polymer Designs for Direct-Ink-Write 3D Printing. Chemistry-A European Journal 25(46):10 768-10 781
Howell BM, Cook CC, Grapes MD, Dubbin K, Robertson EL, Sain JD, Sullivan KT, Duoss EB, Bukovsky EV (2022) Spatially Controlled 3D Printing of Dual-Curing Urethane Elastomers. Advanced Materials Technologies 7:2100700, 3
del Mazo-Barbara L, Ginebra M-P (2021) Rheological Characterisation of Ceramic Inks for 3D Direct Ink Writing: A Review. J Eur Ceram Soc 41(16):18–33
Pinargote NWS, Smirnov A, Peretyagin N, Seleznev A, Peretyagin P (2020) Direct Ink Writing Technology (3D Printing) of Graphene-Based Ceramic Nanocomposites: A Review. Nanomaterials 10:1300, 7
Hou Z, Lu H, Li Y, Yang L, Gao Y (2021) Direct Ink Writing of Materials for Electronics-Related Applications: a Mini Review. Frontiers in Materials 8:91
Gungor-Ozkerim PS, Inci I, Zhang YS, Khademhosseini A, Dokmeci MR (2018) Bioinks for 3D Bioprinting: An Overview. Biomaterials Science 6(5):915–946
Murphy SV, Atala A (2014) 3D Bioprinting of Tissues and Organs. Nat Biotechnol 32(8):773–785
Woods H, Boddorff A, Ewaldz E, Adams Z, Ketcham M, Jang DJ, Sinner E, Thadhani N, Brettmann B (2020) Rheological Considerations for Binder Development in Direct Ink Writing of Energetic Materials. Propellants, Explos, Pyrotech 45:26–35, 1
Wang H, Shen J, Kline DJ, Eckman N, Agrawal NR, Wu T, Wang P, Zachariah MR (2019) Direct Writing of a 90 wt% Particle Loading Nanothermite. Adv Mater 31(23):1806575
Wainwright ER, Sullivan KT, Grapes MD (2020) Designer Direct Ink Write 3D-Printed Thermites with Tunable Energy Release Rates. Adv Eng Mater 22(6):1901196. https://doi.org/10.1002/adem.201901196
Yang F, Zhang M, Bhandari B (2017) Recent Development in 3D Food Printing. Crit Rev Food Sci Nutr 57(14):3145–3153
Karyappa R, Hashimoto M (2019) Chocolate-based Ink Three-dimensional Printing (Ci3DP). Sci Rep 9:14178, 12
Armstrong CD, Yue L, Deng Y, Qi HJ (2022) Enabling Direct Ink Write Edible 3D Printing of Food Purees With Cellulose Nanocrystals. J Food Eng 330:111086, 10
Xu C, Quinn B, Lebel LL, Therriault D, L’Espérance G (2019) Multi-Material Direct Ink Writing (DIW) for Complex 3D Metallic Structures with Removable Supports. ACS Applied Materials & Interfaces 11:8499–8506, 2
Kokkinis D, Bouville F, Studart AR (2018) 3D Printing of Materials With Tunable Failure via Bioinspired Mechanical Gradients. Adv Mater 30(19):1705808
Golobic AM, Durban MD, Fisher SE, Grapes MD, Ortega JM, Spadaccini CM, Duoss EB, Gash AE, Sullivan KT (2019) Active Mixing of Reactive Materials for 3D Printing. Adv Eng Mater 21(8):1900147
Lewis JA (2006) Direct Ink Writing of 3D Functional Materials. Adv Func Mater 16(17):2193–2204
Tamez MBA, Taha I (2021) A Review of Additive Manufacturing Technologies and Markets for Thermosetting Resins and their Potential for Carbon Fiber Integration. Addit Manuf 37:101748
Ligon SC, Liska R, Stampfl J, Gurr M, Mülhaupt R (2017) Polymers for 3D Printing and Customized Additive Manufacturing. Chem Rev 117(15):10 212-10 290
Kumar MB, Sathiya P (2021) Methods and Materials for Additive Manufacturing: A Critical Review on Advancements and Challenges. Thin-Walled Structures 159:107228
Ruscitti A, Tapia C, Rendtorff NM (2020) A Review on Additive Manufacturing of Ceramic Materials Based on Extrusion Processes of Clay Pastes. Cerâmica 66:354–366, 12
Kline DJ, Grapes MD, Morales RC, Egan GC, Sain JD, Doorenbos ZD, Fletcher HE, Avalos EA, English BM, Eliasson V, Sullivan KT, Belof JL (2022) In Situ Laser Profilometry for Material Segmentation and Digital Reconstruction of a Multicomponent Additively Manufactured Part, Additive Manufacturing, vol. 56, p. 102896, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2214860422002949
Hu F, Mikolajczyk T, Pimenov DY, Gupta MK (2021) Extrusion-Based 3D Printing of Ceramic Pastes:Mathematical Modeling and In-Situ Shaping Retention Approach. Materials 14:1–22, 3
Rueda MM, Auscher M-C, Fulchiron R, Périé T, Martin G, Sonntag P, Cassagnau P (2017) Rheology and Applications of Highly Filled Polymers: A Review of Current Understanding. Prog Polym Sci 66:22–53, 3
Wang C, Tan XP, Tor SB, Lim CS (2020) Machine Learning in Additive Manufacturing: State-of-the-Art and Perspectives. Addit Manuf 36:12
MATLAB, Statistics and Machine Learning Toolbox version 12.1 (R2021a). Natick, Massachusetts: The MathWorks Inc., (2022)
Acknowledgements
This work was funded by Lawrence Livermore National Laboratory. Prepared by LLNL under Contract DE-AC52-07NA27344. The authors also gratefully thank the LLNL Lab Directed Research and Development project 21-SI-006 for funding of this project. Document release number LLNL-JRNL-839301.
Author information
Authors and Affiliations
Contributions
MS: conceptualization, methodology, software, resources, formal analysis, investigation, data curation, writing—original draft, visualization. GB: conceptualization, methodology, software, resources, formal analysis, investigation, data curation, writing—original draft. FW: conceptualization, methodology, software, resources, formal analysis, investigation, data curation. DJK: conceptualization, methodology, resources, writing—review, editing, supervision, project administration. RCM: conceptualization, methodology, software, resources, formal analysis, investigation, data curation, writing - original draft, visualization. HF: methodology, resources. KG: methodology. MDG: methodology, resources. SS: methodology, writing—review, editing. KTS: conceptualization, writing—review, editing, supervision, project administration, funding acquisition. JLB: writing—review, editing, supervision, project administration, funding acquisition. VE: conceptualization, resources, writing—review, editing, supervision, project administration, funding acquisition.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Sevcik, M.J., Bjerke, G., Wilson, F. et al. Extrusion parameter control optimization for DIW 3D printing using image analysis techniques. Prog Addit Manuf 9, 517–528 (2024). https://doi.org/10.1007/s40964-023-00470-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40964-023-00470-3