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Computational intelligence in development of 3D printing and reverse engineering

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Computational intelligence (CI) can adopt/optimize important principles in the workflow of 3D printing. This article aims to examine to what extent the current possibilities for using CI in the development of 3D printing and reverse engineering are being used, and where there are still reserves in this area. Methodology: A literature review is followed by own research on CI-based solutions. Results: Two ANNs solving the most common problems are presented. Conclusions: CI can effectively support 3D printing and reverse engineering especially during the transition to Industry 4.0. Wider implementation of CI solutions can accelerate and integrate the development of innovative technologies based on 3D scanning, 3D printing, and reverse engineering. Analyzing data, gathering experience, and transforming it into knowledge can be done faster and more efficiently, but requires a conscious application and proper targeting.
Rocznik
Strony
art. no. e140016
Opis fizyczny
Bibliogr. 33 poz., rys., tab.
Twórcy
  • Institute of Computer Science, Kazimierz Wielki University, Bydgoszcz, Poland
  • Institute of Computer Science, Kazimierz Wielki University, Bydgoszcz, Poland
autor
  • Faculty of Mechatronics, Kazimierz Wielki University, Bydgoszcz, Poland
  • Faculty of Mechatronics, Kazimierz Wielki University, Bydgoszcz, Poland
autor
  • Faculty of Mechatronics, Kazimierz Wielki University, Bydgoszcz, Poland
Bibliografia
  • [1] I. Rojek, D. Mikołajewski, P. Kotlarz, M. Macko and J. Kopowski, “Intelligent system supporting technological process planning for machining and 3D printing”, Bull. Polish Acad. Sci. Tech. Sci., vol. 69, no. 2, p. e136722, 2021, doi: 10.24425/bpasts.2021.136722.
  • [2] I. Rojek., D. Mikołajewski, E. Dostatni, and M. Macko, “AI-optimized technological aspects of the material used in 3D printing processes for selected medical applications”, Materials, vol. 13, no. 23, p. 5437, 2020.
  • [3] K. Paraskevoudis, P. Karayannis, and E.P. Koumoulos, “Real-time 3D printing remote defect detection (stringing) with Computer Vision and Artificial Intelligence”, Processes, vol. 8, pp. 1464, 2020.
  • [4] J. Yang, Y. Chen, W. Huang, and Y. Li, “Survey on artificial intelligence for additive manufacturing”, in Proc. 23rd International Conference on Automation and Computing (ICAC), Huddersfield, UK, 2017, pp. 1–6.
  • [5] Y. Wu, G. Peng, L. Chen and H. Zhang, “Service architecture and evaluation model of distributed 3D printing based on cloud manufacturing”, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016, pp. 002762–002767, doi: 10.1109/SMC.2016.7844657.
  • [6] R. Minetto, N. Volpato, J. Stolfi, R.M.M.H. Gregori, and M.V.G. Silva, “An optimal algorithm for 3D triangle mesh slicing and loop-closure”, Comput.-Aided Des., vol. 92, pp. 1–10, 2017, doi: 10.1016/j.cad.2017.07.001.
  • [7] R.M.M.H. Gregori, N. Volpato, R. Minetto, and M.V.G.D. Silva, “Slicing triangle meshes: An asymptotically optimal algorithm”, in Proc. 14th International Conference on Computational Science and Its Applications, 2014, pp. 252–255.
  • [8] M. Vatani, A. Rahimi and F. Brazandeh, “An enhanced slicing algorithm using nearest distance analysis for layer manufacturing”, Proc. World Acad. Sci. Eng. Technol., vol. 3, no. 25, pp. 721–726, 2009.
  • [9] A. Telea and A. Jalba, “Voxel-based assessment of printability of 3D shapes”, Mathematical Morphology and Its Applications to Image and Signal Processing: 10th International Symposium ISMM 2011 Verbania-Intra Italy, 2011, pp. 393–404.
  • [10] T. Lu, “Towards a fully automated 3D printability checker”, Proc. IEEE Int. Conf. Ind. Technol, 2016, pp. 922–927.
  • [11] T. Wuest, D. Weimer, C. Irgens and K-D. Thoben, “Machine learning in manufacturing: advantages challenges and applications”, Prod. Manuf. Res, vol. 4, no. 1, pp. 23-45, 2016.
  • [12] U. Delli and S. Chang, “Automated process monitoring in 3D printing using sepervised mechaine learning”, Procedia Manufacturing, no. 26, pp. 865–870, 2018.
  • [13] K. Takagishi and S. Umezu, “Development of the improving process for the 3D printed structure”, Scientific Reports, vol. 7, p. 39852, 2017 doi: 10.1038/srep39852.
  • [14] J.-T. Lin, H.-W. Liu, Y.-C. Chiu, K.-T. Chen, and D.-C. Cheng, “3-wavelength (UV, Blue, Red) controlled photo-confinement for 3D-printing: kinetics and modeling”, in IEEE Access, vol. 8, pp. 49353–49362, 2020, doi: 10.1109/ACCESS.2020.2979172.
  • [15] T. Pereira, A. Patterson, and S. Messimer, “Buckling strength of 3-D printed thermoplastic thin shells: notes on an exploratory study of as-printed and reinforced cases”, Appl. Sci., vol. 10, pp. 5863, 2020, doi: 10.3390/app10175863.
  • [16] M. Sardinha, C. Vicente, N. Frutuoso, M. Leite, R. Ribeiro, and L. Reis, “Effect of the ironing process on ABS parts produced by FDM”, Mater. Des. Process. Comm., vol. 3, p. e151, 2021, doi:10.1002/mdp2.151.
  • [17] R. Kumar, R. Singh, I. Ahuja, and M.S.J. Hashmi, “Processing techniques of polymeric materials and their reinforced composites”, Adv. Mater. Process. Technol., vol. 6, pp. 1–17, 2020, doi: 10.1080/2374068X.2020.1728989.
  • [18] L. Tzounis, M. Petousis, S. Grammatikos, and N. Vidakis, “3D Printed Thermoelectric Polyurethane/Multiwalled Carbon Nanotube Nanocomposites: A Novel Approach towards the Fabrication of Flexible and Stretchable Organic Thermoelectrics”, Materials, vol. 13, no. 12, p. 2879, 2020, doi: 13.10.3390/ma131228792, 2020.
  • [19] R. Najafi Zare, E. Doustkhah, and M.H.N. Assadi, “Three-dimensional bone printing using hydroxyapatite-PLA composite”, Mater. Today: Proc., vol. 42, pp. 1531–1533, 2021, doi: 10.1016/j.matpr.2019.12.046, 2019.
  • [20] P. Dasgupta, “Artificial intelligence, three-dimensional printing and global health”, BJU Int., vol. 124, no. 6, p. 897, 2019.
  • [21] B. Meskó, “the real era of the art of medicine begins with artificial intelligence”, J. Med. Internet Res., vol. 21, no. 11, p. e16295, 2019.
  • [22] D.D. Wang et al., “3D printing, computational modeling, and artificial intelligence for structural heart disease”, JACC Cardiovasc. Imag., vol. 14, no. 1, pp. 41–60, 2021, doi: S1936-878X(20)30515-5.
  • [23] R. Vashistha, P. Kumar, A.K. Dangi, N. Sharma, D. Chhabra, and P. Shukla, “Quest for cardiovascular interventions: precise modeling and 3D printing of heart valves”, J. Biol. Eng., vol. 13, p. 12, 2019.
  • [24] B.N. Peele, T.J. Wallin, H. Zhao, and R.F. Shepherd, “3D print- ing antagonistic systems of artificial muscle using projection stereolithography”, Bioinspir. Biomim., vol. 10, no. 5, p. 055003, 2015.
  • [25] C. Tawk, M. In Het Panhuis, G.M. Spinks, and G. Alici, “Bioinspired 3D printable soft vacuum actuators for locomotion robots, grippers and artificial muscles”, Soft Robot., vol. 5, no. 6, pp. 685–694, 2018.
  • [26] M.M. Stanton, C. Trichet-Paredes, and S. Sánchez, “Applications of three-dimensional (3D) printing for microswimmers and bio-hybrid robotics”, Lab Chip., vol. 15, no. 7, pp. 1634–1637, 2015.
  • [27] N. Nagarajan, A. Dupret-Bories, E. Karabulut, P. Zorlutuna, and N.E. Vrana, “Enabling personalized implant and controllable biosystem development through 3D printing”, Biotechnol. Adv., vol. 36, no. 2, pp. 521–533, 2018.
  • [28] D. Kokkinis, F. Bouville, and A.R. Studart, “3D printing of materials with tunable failure via bioinspired mechanical gradients”, Adv. Mater., vol. 30, no. 19, p. e1705808, 2018.
  • [29] K. Harren, F. Dittrich, F. Reinecke, and M. Jäger, “Digitalization and artificial intelligence in orthopedics and traumatology”, Orthopade, vol. 47, no. 12, pp. 1039–1054, 2018.
  • [30] J. Szczepa ́nski, J. Klamka, K.M. W ̨egrzyn-Wolska, I. Rojek, and P. Prokopowicz, “Computational Intelligence and optimization techniques in communications and control”, Bull. Polish Acad. Sci. Tech. Sci., vol. 68, no. 2, pp. 181–184, 2020, doi: 10.24425/bpasts.2020.131851.
  • [31] A. Tlija, K. W ̨egrzyn-Wolska, and D. Istrate, “Missing-data imputation using wearable sensors in heart rate variability”, Bull. Polish Acad. Sci. Tech. Sci., vol. 68, no. 2, pp. 255–261, 2020, doi: 10.24425/bpasts.2020.133118.
  • [32] B. Nowak, M. Piechowiak, M. Stasiak, and P. Zwierzykowski, “An analytical model of a system with priorities servicing a mixture of different elastic traffic streams”, Bull. Polish Acad. Sci. Tech. Sci., vol. 68, no. 2, pp. 263–270, 2020, doi: 10.24425/bpasts.2020.133111.
  • [33] Y. Wang, L. Wang, and C.A. Xue, “Medical information security in the era of artificial intelligence”, Med. Hypotheses, vol. 115, pp. 58–60, 2018.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8b10b57f-a06d-4dbd-98b9-bd1fc5b867aa
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