Shrinkage prediction and correction in material extrusion of cellulose-chitin biopolymers using neural network regression

References

• Bikas, H., P. Stavropoulos, and G. Chryssolouris. 2016. “Additive Manufacturing Methods and Modelling Approaches: A Critical Review.” The International Journal of Advanced Manufacturing Technology 83 (1–4): 389–405. https://doi.org/10.1007/s00170-015-7576-2.
   Web of Science ®Google Scholar
• Brøtan, V. 2014. “A new Method for Determining and Improving the Accuracy of a Powder bed Additive Manufacturing Machine.” The International Journal of Advanced Manufacturing Technology 74 (9–12): 1187–1195. https://doi.org/10.1007/s00170-014-6012-3.
   Web of Science ®Google Scholar
• Chen, H., and Y. F. Zhao. 2016. “Process Parameters Optimization for Improving Surface Quality and Manufacturing Accuracy of Binder Jetting Additive Manufacturing Process.” Rapid Prototyping Journal 22 (3): 527–538. https://doi.org/10.1108/RPJ-11-2014-0149.
   Web of Science ®Google Scholar
• Chowdhury, S., and S. Anand. 2016. “Artificial Neural Network Based Geometric Compensation for Thermal Deformation in Additive Manufacturing Processes.” Proceedings of the ASME MSEC-NAMRC. https://doi.org/10.1115/MSEC2016-8784.
   Google Scholar
• Corradini, F., and M. Silvestri. 2022. “Design and Testing of a Digital Twin for Monitoring and Quality Assessment of Material Extrusion Process.” Additive Manufacturing 51:102633. https://doi.org/10.1016/j.addma.2022.102633.
   Google Scholar
• Dastjerdi, A. A., M. R. Movahhedy, and J. Akbari. 2017. “Optimization of Process Parameters for Reducing Warpage in Selected Laser Sintering of Polymer Parts.” Additive Manufacturing 18:285–294. https://doi.org/10.1016/j.addma.2017.10.018.
   Web of Science ®Google Scholar
• Delli, U., and S. Chang. 2018. “Automated Process Monitoring in 3D Printing Using Supervised Machine Learning.” Procedia Manufacturing 26:865–870. https://doi.org/10.1016/j.promfg.2018.07.111.
   Google Scholar
• Farhan Khan, M., A. Alam, M. Ateeb Siddiqui, M. Saad Alam, Y. Rafat, N. Salik, and I. Al-Saidan. 2021. “Real-time Defect Detection in 3D Printing Using Machine Learning.” Materials Today: Proceedings 42:521–528. https://doi.org/10.1016/j.matpr.2020.10.482.
   Google Scholar
• Goh, G. D., N. M. Bin Hamzah, and Y. Y. Wai. 2021. “Anomaly Detection in Fused Filament Fabrication Using Machine Learning.” 3D Printing and Additive Manufacturing. https://doi.org/10.1089/3dp.2021.0231.
   Google Scholar
• Guven, E., Y. Karpat, and M. Cakmakci. 2022. “Improving the Dimensional Accuracy of Micro Parts 3D Printed with Projection-based Continuous Vat Photopolymerization Using a Model-based Grayscale Optimization Method.” Additive Manufacturing 57:102954. https://doi.org/10.1016/j.addma.2022.102954.
   Web of Science ®Google Scholar
• Hong, R., L. Zhang, J. Lifton, S. Daynes, J. Wei, S. Feih, and W. F. Lu. 2021. “Artificial Neural Network-based Geometry Compensation to Improve the Printing Accuracy of Selective Laser Melting Fabricated Sub-millimetre Overhang Trusses.” Additive Manufacturing 37:101594. https://doi.org/10.1016/j.addma.2020.101594.
   Web of Science ®Google Scholar
• Hoo, J. L., S. Dritsas, and J. G. Fernandez. 2022. “Improving the Geometric Accuracy in Large-scale Additive Manufacturing of Fungus-like Adhesive Materials.” Proceedings Materials Today 70:603–610. https://doi.org/10.1016/j.matpr.2022.08.562.
   Google Scholar
• Huang, Q., Z. Zhang, A. T. Sabbaghi, and A. Dasgupta. 2015. “Optimal Offline Compensation of Shape Shrinkage for Three-dimensional Printing Processes.” IIE Transactions 47 (5): 431–441. https://doi.org/10.1080/0740817X.2014.955599.
   Web of Science ®Google Scholar
• Kafara, M., J. Kemnitzer, H. H. Westermann, and R. Steinhilper. 2018. “Influence of Binder Quantity on Dimensional Accuracy and Resilience in 3D-printing.” Procedia Manufacturing 21:638–646. https://doi.org/10.1016/j.promfg.2018.02.166.
   Google Scholar
• Kakanuru, P., and K. Pochiraju. 2022. “Simulation of Shrinkage during Sintering of Additively Manufactured Silica Green Bodies.” Additive Manufacturing 56:102908. https://doi.org/10.1016/j.addma.2022.102908.
   Web of Science ®Google Scholar
• Kim, H., H. Lee, J. S. Kim, and S. H. Ahn. 2020. “Image-based Failure Detection for Material Extrusion Process Using a Convolutional Neural Network.” The International Journal of Advanced Manufacturing Technology 111 (5–6): 1291–1302. https://doi.org/10.1007/s00170-020-06201-0.
   Web of Science ®Google Scholar
• Kruth, J. P., M. C. Leu, and T. Nakagawa. 1998. “Progress in Additive Manufacturing and Rapid Prototyping.” Annals CIRP 47 (2): 525–540. https://doi.org/10.1016/S0007-8506(07)63240-5.
   Google Scholar
• Lee, P. H., H. Chung, S. W. Lee, J. Yoo, and J. Ko. 2014. “Review: Dimensional Accuracy in Additive Manufacturing Processes.” Proceedings of ASME V001T04A045. https://doi.org/10.1115/MSEC2014-4037.
   Google Scholar
• Liu, J., and L. Li. 2004. “In-time Motion Adjustment in Laser Cladding Manufacturing Process for Improving Dimensional Accuracy and Surface Finish of the Formed Part.” Optics and Laser Technology 36 (6): 477–483. https://doi.org/10.1016/j.optlastec.2003.12.003.
   Web of Science ®Google Scholar
• Lynn-Charney, C., and D. Rosen. 2000. “Usage of Accuracy Models in Stereolithography Process Planning.” Rapid Prototyping 6 (2): 77–87. https://doi.org/10.1108/13552540010323600.
   Web of Science ®Google Scholar
• Mani, M., S. Feng, B. Lane, A. Donmez, S. Moylan, and R. Fesperman. 2015. “Measurement Science Needs for Real-time Control of Additive Manufacturing Powder bed Fusion Processes.” NISTIR 8036. https://doi.org/10.6028/NIST.IR.8036.
   Google Scholar
• Mayur, V., K. Narendra, K. J. Prashant, T. Puneet, and M. P. Pulak. 2018. “Shrinkage Compensation Study for Performing Machining on Additive Manufactured Parts.” Materials Today 5 (9): 18544–18551. https://doi.org/10.1016/j.matpr.2018.06.197.
   Google Scholar
• Moroni, G., W. P. Syam, and S. Petrò. 2014. “Towards Early Estimation of Part Accuracy in Additive Manufacturing.” Procedia CIRP 21:300–305. https://doi.org/10.1016/j.procir.2014.03.194.
   Google Scholar
• Mostafaei, A., P. Rodriguez De Vecchis, I. Nettleship, and M. Chmielus. 2019. “Effect of Powder Size Distribution on Densification and Microstructural Evolution of Binder-jet 3D-Printed Alloy 625.” Materials & Design 162:375–383. https://doi.org/10.1016/j.matdes.2018.11.051.
   Web of Science ®Google Scholar
• Muñiz Castro, B., Moe Elbadawi, J. J. Ong, T. Pollard, Z. Song, S. Gaisford, G. Pérez, A. W. Basit, P. Cabalar, and A. Goyanes. 2021. “Machine Learning Predicts 3D Printing Performance of Over 900 Drug Delivery Systems.” Journal of Controlled Release 337:530–545. https://doi.org/10.1016/j.jconrel.2021.07.046.
   PubMed Web of Science ®Google Scholar
• Nath, P., J. D. Olson, S. Mahadevan, and Y. T. T. Lee. 2020. “Optimization of Fused Filament Fabrication Process Parameters under Uncertainty to Maximize Part Geometry Accuracy.” Additive Manufacturing 35:101331. https://doi.org/10.1016/j.addma.2020.101331.
   Web of Science ®Google Scholar
• Noriega, A., D. Blanco, and B. J. Alvarez. 2013. “Dimensional Accuracy Improvement of FDM Square Cross-section Parts Using Artificial Neural Networks and an Optimization Algorithm.” International Journal Advanced Manufacturing Technology 69 (9–12): 2301–2313. https://doi.org/10.1007/s00170-013-5196-2.
   Web of Science ®Google Scholar
• Osswald, A. T., J. Puentes, and J. Kattinger. 2018. “Fused Filament Fabrication Melting Model.” Additive Manufacturing 22:51–59. https://doi.org/10.1016/j.addma.2018.04.030.
   Web of Science ®Google Scholar
• Pavan, K. G., and P. R. Srinivasa. 2014. “Multi-objective Optimisation of Strength and Volumetric Shrinkage of FDM Parts.” Virtual and Physical Prototyping 9 (2): 127–138. https://doi.org/10.1080/17452759.2014.898851.
   Google Scholar
• Rajabi, M., M. McConnell, J. Cabral, and A. M. Azam. 2021. “Chitosan Hydrogels in 3D Printing for Biomedical Applications.” Carbohydrate Polymers 260:117768. https://doi.org/10.1016/j.carbpol.2021.117768.
   PubMed Web of Science ®Google Scholar
• Rebaioli, L., and I. Fassi. 2017. “A Review on Benchmark Artifacts for Evaluating the Geometrical Performance of Additive Manufacturing Processes.” International Journal of Advanced Manufacturing Technology 93 (5–8): 2571–2598. https://doi.org/10.1007/s00170-017-0570-0.
   Web of Science ®Google Scholar
• Sanandiya, N. D., Y. Vijay, M. Dimopoulou, S. Dritsas, and J. G. Fernandez. 2018. “Large-scale Additive Manufacturing with Bioinspired Cellulosic Materials.” Scientific Reports 8:8642. https://doi.org/10.1038/s41598-018-26985-2.
   PubMed Web of Science ®Google Scholar
• Soe, S. P., D. R. Eyers, and R. Setchi. 2013. “Assessment of Non-uniform Shrinkage in the Laser Sintering of Polymer Materials.” International Journal of Advanced Manufacturing Technology 68 (1–4): 111–125. https://doi.org/10.1007/s00170-012-4712-0.
   Web of Science ®Google Scholar
• Spears, T. G., and S. A. Gold. 2016. “In-process Sensing in Selective Laser Melting (SLM) Additive Manufacturing.” Integrated Material Manufacturing Innovation 5 (1): 16–40. https://doi.org/10.1186/s40192-016-0045-4.
   Google Scholar
• Spoerk, M., J. Sapkota, G. Weingrill, T. Fischinger, F. Arbeiter, and C. Holzer. 2017. “Shrinkage and Warpage Optimization of Expanded-perlite-filled Polypropylene Composites in Extrusion-based Additive Manufacturing.” Macromolecular Material Engineering 302 (10): 1700143. https://doi.org/10.1002/mame.201700143.
   Web of Science ®Google Scholar
• Strong, D., M. Kay, B. Conner, T. Wakefield, and G. Manogharan. 2018. “Hybrid Manufacturing – Integrating Traditional Manufacturers with Additive Manufacturing (AM) Supply Chain.” Additive Manufacturing 21:159–173. https://doi.org/10.1016/j.addma.2018.03.010.
   Web of Science ®Google Scholar
• Tang, Y., C. Henderson, J. Muzzy, and D. Rosen. 2004. “Stereolithography Cure Modelling and Simulation.” International Journal of Material and Product Technology 21 (4): 255–272. https://doi.org/10.1504/IJMPT.2004.004941.
   Web of Science ®Google Scholar
• Tofail, A. M. S., E. Koumoulos, A. Bandyopadhyay, S. Bose, L. O’Donoghue, and C. Charitidis. 2018. “Additive Manufacturing: Scientific and Technological Challenges, Market Uptake and Opportunities.” Materials Today 21 (1): 22–37. https://doi.org/10.1016/j.mattod.2017.07.001.
   Web of Science ®Google Scholar
• Tong, K., S. Joshi, and E. Lehtihet. 2008. “Error Compensation for Fused Deposition Modelling (FDM) Machine by Correcting Slice files.” Rapid Prototyping Journal 14 (1): 4–14. https://doi.org/10.1108/13552540810841517.
   Web of Science ®Google Scholar
• Turner, B. N., and S. A. Gold. 2015. “A Review of Melt Extrusion Additive Manufacturing Processes: II. Materials, Dimensional Accuracy, and Surface Roughness.” Rapid Prototyping Journal 21 (3): 250–261. https://doi.org/10.1108/RPJ-02-2013-0017.
   Web of Science ®Google Scholar
• Vijay, Y., N. D. Sanadiya, S. Dritsas, and J. G. Fernandez. 2019. “Control of Process Settings for Large-scale Additive Manufacturing with Sustainable Natural Composites.” Journal of Mechanical Design 141 (8): 081701. https://doi.org/10.1115/1.4042624.
   Web of Science ®Google Scholar
• Yang, Y., D. McGregor, S. Tawfick, W. King, and C. Shao. 2022. “Hierarchical Data Models Improve the Accuracy of Feature Level Predictions for Additively Manufactured Parts.” Additive Manufacturing 51:102621. https://doi.org/10.1016/j.addma.2022.102621.
   Web of Science ®Google Scholar
• Zago, M., F. M. Lecis, M. Vedani, and I. Cristofolini. 2021. “Dimensional and Geometrical Precision of Parts Produced by Binder Jetting Process as Affected by the Anisotropic Shrinkage on Sintering.” Additive Manufacturing 43:102007. https://doi.org/10.1016/j.addma.2021.102007.
   Web of Science ®Google Scholar