Advanced software development of 2D and 3D model visualization for TwinPrint, a dual-arm 3D bioprinting system for multi-material printing
This research highlights the development of a two-dimensional (2D) and three-dimensional (3D) preview software for additive manufacturing (AM). The presented software can produce a virtual representation of an actuator’s path movements by reading and parsing the orders of the desired geometric code (G-code) file. It then simulates the coded sections into separate 2D layers and colored 3D objects in a graphical model. This allows users to validate the shapes before the 3D printing process. G-code is an operation language which is based on command lines of code written in an alphanumeric format. Each line of these commands controls one machining operation; this instructs the machine’s motion to move in an arc, a circle, or a straight line to perform a specific shape after compiling all code lines. AM technology is widely used in most manufacturing fields (e.g., medical, chemical, and research laboratories) as a prototyping technology due to its ability to produce rapid prototyping models. 3D printing creates physical 3D models by extruding material layer by layer as 2D layers. At present, the most critical challenges in AM technology are drastically reducing prototyping materials’ consumption and time spent. To address these challenges, the proposed software allows for visualization of G-code files and predicting the overall layers’ shapes, allowing both structure prediction and subsequent printing error reduction.
Kim Y, Nowzari H, Rich SK, 2013, Risk of prion disease transmission through bovine-derived bone substitutes: A systematic review. Clin Implant Dent Relat Res, 15: 645–653. https://doi.org/10.1111/j.1708-8208.2011.00407.x
Ashammakhi N, Kaarela O, 2018, Three-dimensional bioprinting can help bone. The J Craniofac Surg, 29: 9–11. https://doi.org/10.1097/SCS.0000000000004143
Tavafoghi M, Darabi MA, Mahmoodi M, et al., 2021, Multimaterial bioprinting and combination of processing techniques towards the fabrication of biomimetic tissues and organs. Biofabrication, 13: 42002. https://doi.org/10.1088/1758-5090/ac0b9a
Liu W, Zhang YS, Heinrich MA, et al., 2017, Bioprinting: Rapid continuous multimaterial extrusion bioprinting (Adv. Mater. 3/2017). Adv Mater (Weinheim), 29:1604630. https://doi.org/10.1002/adma.201770016
Ng WL, Lee JM, Zhou M, et al., 2020, Vat polymerization-based bioprinting process, materials, applications and regulatory challenges. Biofabrication, 12: 022001. https://doi.org/10.1088/1758-5090/ab6034
Jiang T, Munguia-Lopez JG, Flores-Torres S, et al., 2019, Extrusion bioprinting of soft materials: An emerging technique for biological model fabrication. Appl Phys Rev, 6: 011310. https://doi.org/10.1063/1.5059393
Li X, Liu B, Pei B, et al., 2020, Inkjet bioprinting of biomaterials. Chem Rev, 120: 10793–10833.
Tekin E, Smith PJ, Schubert US, 2008, Inkjet printing as a deposition and patterning tool for polymers and inorganic particles. Soft Matter, 4: 703–713.
Villar G, Graham AD, Bayley H, 2013, A tissue-like printed material. Science, 340: 48–52.
Xu T, Jin J, Gregory C, et al., 2005, Inkjet printing of viable mammalian cells. Biomaterials, 26: 93–99.
Zhang LG, Leong K, Fisher JP, editors. 2022, 3D Bioprinting and Nanotechnology in Tissue Engineering and Regenerative Medicine. Academic Press, Cambridge, Massachusetts.
Miri AK, Nieto D, Iglesias L, et al., 2018, Bioprinting: Microfluidics‐enabled multimaterial maskless stereolithographic bioprinting (Adv. Mater. 27/2018). Adv Mater (Weinheim), 30: 1870201–n/a. https://doi.org/10.1002/adma.201870201
Ng WL, Chan A, Ong YS, et al., 2020, Deep learning for fabrication and maturation of 3D bioprinted tissues and organs. Virtual Phys Prototyp, 15: 340–358. https://doi.org/10.1080/17452759.2020.1771741
An J, Chua CK, Mironov V, 2021, Application of machine learning in 3D bioprinting: Focus on development of big data and digital twin. Int J Bioprint, 7: 342. https://doi.org/10.18063/ijb.v7i1.342
Bikas H, Stavropoulos P, Chryssolouris G, 2015, Additive manufacturing methods and modelling approaches: A critical review. Int J Adv Manuf Technol, 83: 389–405. https://doi.org/10.1007/s00170-015-7576-2
Ueng SK, Chen LK, Jen SY, 2017, A Preview System for 3D Printing. In: 2017 International Conference on Applied System Innovation (ICASI), p1508–1511.
Gahbiche MA, Boudhaouia S, Giraud E, 2020, A finite element simulation of the incremental sheet forming process: A new method for G-code implementation. Int J Mater Prod Technol, 61: 68–86.
Khan Z, Albalawi H, Valle-Perez A, 2022, From 3D printed molds to bioprinted scaffolds: A hybrid material extrusion and vat polymerization bioprinting approach for soft matter constructs. Mater Sci Addit Manuf, 1: 7. https://doi.org/10.18063/msam.v1i1.7
Abdelrahman S, Alsanie WF, Khan ZN, 2022, A Parkinson’s disease model composed of 3D bioprinted dopaminergic neurons within a biomimetic peptide scaffold. Biofabrication, 14: 044103. https://doi.org/10.1088/1758-5090/ac7eec
Hammad N, AlZaid S, Alwazani H, 2022, TwinPrint: A Dual-Arm Robotic System for 3D Biofabrication, Viewed August 22, 2022. In: RobotoKAUST KAUST Research Conference on Robotics and Autonomy.
Albalawi H, Khan ZN, Valle-Pérez AU, et al., 2021, Sustainable and eco-friendly coral restoration through 3D printing and fabrication. ACS Sustain Chem Eng, 9: 12634– 12645. https://doi.org/10.1021/acssuschemeng.1c04148
Lim J, 2022, Plotting with PyQtGraph Create custom plots in PyQt with PyQtGraph, 2020. Available from: https:// www.pythonguis.com/tutorials/plotting-pyqtgraph [Last accessed on 2020 May 02].
Zhang Y, 2020, Gcode-Reader. Available from: https:// github.com/zhangyaqi1989/Gcode-Reader [Last accessed on 2021 Feb 02].
Marvin Killing, 2014, QxtSpanSlider. Available from: https:// github.com/mkilling/QxtSpanSlider.py [Last accessed on 2021 Sep 16].
Coolsunxu, 2021, Video_Cut. Available from: https:// github.com/coolsunxu/Video_Cut/blob/main/main.py [Last accessed on 2021 Sep 22].