Application of Machine Learning-Based Approach to Predict and Optimize Mechanical Properties of Additively Manufactured Polyether Ether Ketone Biopolymer Using Fused Deposition Modeling
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OpenAlex topics: Additive Manufacturing and 3D Printing Technologies · Manufacturing Process and Optimization · Injection Molding Process and Properties
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References (69)
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1. R. Ma and D. Guo, Evaluating the Bioactivity of a Hydroxyapatite-Incorporated Polyetheretherketone Biocomposite, J. Orthop. Surg. Res. Orthop Surg Res, 2019 https://doi.org/10.1186/s13018-019-1069-110.1186/s13018-019-1069-1 ↗
2. I. Panayotov, V. Orti, F. Cuisinier and J. Yachouh, Polyetheretherketone (peek) for Medical Applications, J. Mater. Sci. Mater. Med., 2016 https://doi.org/10.1007/s10856-016-5731-410.1007/s10856-016-5731-4 ↗
3. Y. Ji, X. Yu and H. Zhu, Fabrication of mg Coating on Peek and Antibacterial Evaluation for Bone Application, Coatings, 2021, 11(8), p 1010. https://doi.org/10.3390/coatings1108101010.3390/coatings11081010 ↗
4. R. Surendran, A review of the current state of the art of polyether ether ketone (peek) composite based 3d-printed biomedical scaffolds, Designs, 2023, 7(6), p 128. https://doi.org/10.3390/designs706012810.3390/designs7060128 ↗
5. J. Toth, M. Wang, B. Estes, J. Scifert, H. Seim and A. Turner, Polyetheretherketone as a biomaterial for spinal applications, Biomaterials, 2006, 27(3), p 324–334. https://doi.org/10.1016/j.biomaterials.2005.07.01110.1016/j.biomaterials.2005.07.011 ↗
6. Y. Xu, F. Zhang, W. Zhai, S. Cheng, J. Li and Y. Wang, Unraveling of Advances in 3D-Printed Polymer-Based Bone Scaffolds, Polymers, 2022, 14(3), p 566. https://doi.org/10.3390/polym1403056610.3390/polym14030566 ↗
7. M. Omrani, A. Hadjizadeh, A. Milani and K. Kim, PEEK surface modification methods and effect of the laser method on surface properties, Biointerface. Res. Appl. Chem., 2020, 10(2), p 5132–5140. https://doi.org/10.33263/briac102.13214010.33263/briac102.132140 ↗
8. D. Pokorný, Polyetheretherketone (peek) part ii: application in clinical practice, Acta Chir. Orthop. Traumatol. Cech. Chir. Orthop. Traumatol. Cech., 2010, 77(6), p 470–478. https://doi.org/10.55095/achot2010/07810.55095/achot2010/078 ↗
9. Q. Li, W. Perrie, R. Potter, O. Allegre, Z. Li, T. Yu and G. Dearden, Femtosecond laser micro-structuring of amorphous polyether(ether)ketone at 775 nm and 387 nm, J. Phys. D Appl., 2020, 53(36), p 365301. https://doi.org/10.1088/1361-6463/ab8ed810.1088/1361-6463/ab8ed8 ↗
10. M. Ortiz-Rios, M. Haag, F. Balezeau, S. Frey, A. Thiele, K. Murphy and M. Schmid, Improved methods for mri-compatible implants in nonhuman primates, J. Neurosc. Methods., 2018, 308, p 377–389. https://doi.org/10.1016/j.jneumeth.2018.09.01310.1016/j.jneumeth.2018.09.013 ↗
11. Li Q, Perrie W, Tang Y, Allegre O, Ho J, Chalker P, Li Z, Edwardson SP, Dearden G. Ablation of amorphous Polyethy (ethyl) letone (PEEK) by a femtosecond Ti: sapphire laser. InLaser-based Micro-and Nanoprocessing XIV 2020 Mar 2 (Vol. 11268, pp. 109-117). SPIE.10.1117/12.2543735 ↗
12. E. González-Castillo, M. Žitňan, Y. Torres, P. Shuttleworth, D. Galusek, G. Ellis and A. Boccaccını, Relation between chemical composition, morphology, and microstructure of poly(ether ether ketone)/reduced graphene oxide nanocomposite coatings obtained by electrophoretic deposition, J. Mater. Sci.Sci, 2022, 57(10), p 5839–5854. https://doi.org/10.1007/s10853-022-06995-210.1007/s10853-022-06995-2 ↗
13. M. Gornet, F. Chan, J. Coleman, B. Murrell, R. Nockels, B. Taylor and J. Ochoa, Biomechanical assessment of a peek rod system for semi-rigid fixation of lumbar fusion constructs, J. Biomech. Eng.Biomech Eng, 2011 https://doi.org/10.1115/1400486210.1115/14004862 ↗
14. Ö. Keleş, E. Anderson and J. Huynh, Mechanical reliability of short carbon fiber reinforced abs produced via vibration assisted fused deposition modeling, Rapid Prototyping Journal, 2018, 24(9), p 1572–1578. https://doi.org/10.1108/rpj-12-2017-024710.1108/rpj-12-2017-0247 ↗
15. R. Ahmadi, D. D’Andrea and D. Santonocito, Fatigue assessment of 3d-printed porous pla-based scaffold structures by thermographic methods, Iop Conf Series Mater Sci Eng, 2023, 1275(1), p 012002. https://doi.org/10.1088/1757-899x/1275/1/01200210.1088/1757-899x/1275/1/012002 ↗
16. N. Maguluri, G. Suresh and K. Rao, Assessing the effect of fdm processing parameters on mechanical properties of pla parts using taguchi method, J. Thermoplast. Compos. Mater.Thermoplast Compos Mater, 2021, 36(4), p 1472–1488. https://doi.org/10.1177/0892705721105303610.1177/08927057211053036 ↗
17. P. Yro, K. Cochon, J. Rueda, G. Quiachon, M. Ebarvia and B. Basilia, Pla/mwcnt nanocomposite: improved electrical, thermal and antibacterial properties for fused deposition modelling additive manufacturing applications, Mater. Sci. Forum, 2023, 1085, p 29–34. https://doi.org/10.4028/p-04wj7010.4028/p-04wj70 ↗
18. N. Ranjan, R. Singh and I. Ahuja, Investigations for mechanical properties of PLA-HAp-CS based functional prototypes, Mater. Today. Proc., 2019, 18, p 2329–2334. https://doi.org/10.1016/j.matpr.2019.07.01610.1016/j.matpr.2019.07.016 ↗
19. N. Ranjan, R. Singh and I. Ahuja, Development of PLA-HAp-CS-based biocompatible functional prototype: a case study, J. Thermoplast. Compos. Mater., 2018, 33(3), p 305–323. https://doi.org/10.1177/089270571880553110.1177/0892705718805531 ↗
20. N. Ranjan, R. Singh and I. Ahuja, Investigations on joining of orthopaedic scaffold with rapid tooling, Proc. Inst. Mech. Eng. [H], 2019, 233(7), p 754–760. https://doi.org/10.1177/095441191985281110.1177/0954411919852811 ↗
21. T. Medibew, A comprehensive review on the optimization of the fused deposition modeling process parameter for better tensile strength of pla-printed parts, Adv. Mater. Sci. Eng., 2022, 2022, p 1–11. https://doi.org/10.1155/2022/549083110.1155/2022/5490831 ↗
22. M. Afrose, S. Masood, M. Nikzad and P. Iovenitti, Effects of build orientations on tensile properties of pla material processed by fdm, Adv. Mater. Res., 2014, 1044–1045, p 31–34. https://doi.org/10.4028/www.scientific.net/amr.1044-1045.3110.4028/www.scientific.net/amr.1044-1045.31 ↗
23. S.M. Deivasikamani, Enhancing mechanical characteristics of petg carbon fiber composite through various shell thickness in fdm processing, Int. Res. J. Adv. Engg. Hub., 2024, 2(04), p 706–714. https://doi.org/10.47392/irjaeh.2024.010110.47392/irjaeh.2024.0101 ↗
24. J. Mercado-Colmenero, M. Rubia, E. Mata-García, M. Rodriguez-Santiago and C. Martín-Doñate, Experimental and numerical analysis for the mechanical characterization of petg polymers manufactured with fdm technology under pure uniaxial compression stress states for architectural applications, Polymers, 2020, 12(10), p 2202. https://doi.org/10.3390/polym1210220210.3390/polym12102202 ↗
25. A. Gökmen, Investigation on wear behavior of petg and pla parts fabricated with fused deposition modelling under different wear conditions, Polymer Korea, 2023, 47(3), p 257–263. https://doi.org/10.7317/pk.2023.47.3.25710.7317/pk.2023.47.3.257 ↗
26. M. Moradi, Correlation between infill percentages, layer width, and mechanical properties in fused deposition modelling of poly-lactic acid 3d printing, Machines, 2023, 11(10), p 950. https://doi.org/10.3390/machines1110095010.3390/machines11100950 ↗
27. H. Alzyod, J. Takács and P. Ficzere, Improving surface smoothness in fdm parts through ironing post-processing, J. Reinf. Plast. Compos., 2023, 43(11–12), p 671–681. https://doi.org/10.1177/0731684423117305910.1177/07316844231173059 ↗
28. A. Portoaca, R. Ripeanu, A. Diniţă and M. Tănase, Optimization of 3d printing parameters for enhanced surface quality and wear resistance, Polymers, 2023, 15(16), p 3419. https://doi.org/10.3390/polym1516341910.3390/polym15163419 ↗
29. J. Wang, A. Fu, B. Liu, Y. Chen, Y. Cao, H. Zhou, B. Wang, X. Li, J. Li, Q. Fang and Y. Liu, Weakening the mechanical property anisotropy of additively manufactured medium entropy alloy by controlling the cellular structure, Additive Manuf., 2024 https://doi.org/10.1016/j.addma.2024.10430310.1016/j.addma.2024.104303 ↗
30. Ç. Bolat and B. Ergene, An experimental effort on impact properties of polylactic acid samples manufactured by additive manufacturing, Düzce Üniversitesi Bilim Ve Teknoloji Dergisi., 2023, 11(2), p 998–1013. https://doi.org/10.29130/dubited.107525910.29130/dubited.1075259 ↗
31. J. Wei, X. Chu, X. Sun, K. Xu, H. Deng, J. Chen and M. Lei, Machine learning in materials science, Infomat, 2019, 1(3), p 338–358. https://doi.org/10.1002/inf2.1202810.1002/inf2.12028 ↗
32. C. Zhou, Development of new materials for electrothermal metals using data driven and machine learning, PLoS ONE, 2024, 19(4), p e0297943. https://doi.org/10.1371/journal.pone.029794310.1371/journal.pone.0297943 ↗
33. J. Smith, Effects of machine learning algorithms for predicting and optimizing the properties of new materials in the united states, Eur. J. Phys. Sci., 2023, 6(1), p 23–34. https://doi.org/10.47672/ejps.144410.47672/ejps.1444 ↗
34. M. Zhang, J. Yuan, Z. Jian, Z. Sheng, Y. Hou, J. Xu and X. Hou, Performance prediction of magnetorheological fluid-based liquid gating membrane by kriging machine learning method, Interdisciplinary Mater., 2022, 1(1), p 157–169. https://doi.org/10.1002/idm2.1200510.1002/idm2.12005 ↗
35. K. Teramoto and K. Hirose, Sparse multivariate regression with missing values and its application to the prediction of material properties, Int. J. Numer. Meth. Eng., 2021, 123(2), p 530–546. https://doi.org/10.1002/nme.686710.1002/nme.6867 ↗
36. M. Karamad, R. Magar, Y. Shi, S. Siahrostami, I. Gates and A. Farimani, Orbital graph convolutional neural network for material property prediction, Phys. Rev. Mater., 2020 https://doi.org/10.1103/physrevmaterials.4.09380110.1103/physrevmaterials.4.093801 ↗
37. B. Fan, X. Liu, S. Si, S. Liu, R. Xie and J. Liu, Study on the key features of dynamic compressive fracture strain of ti-zr-nb solid solution alloys through random forest regressor, J. Phys. Conf. Ser., 2022, 2355(1), p 012078. https://doi.org/10.1088/1742-6596/2355/1/01207810.1088/1742-6596/2355/1/012078 ↗
38. T. Cawte and A. Bazylak, Accurately predicting transport properties of porous fibrous materials by machine learning methods, Electrochem. Sci. Adv., 2022 https://doi.org/10.1002/elsa.20210018510.1002/elsa.202100185 ↗
39. R. Jacobs, T. Mayeshiba, B. Afflerbach, L. Miles, M. Williams, M. Turner and D. Morgan, The materials simulation toolkit for machine learning (mast-ml): an automated open source toolkit to accelerate data-driven materials research, Computat. Mater. Sci., 2020, 176, p 109544. https://doi.org/10.1016/j.commatsci.2020.10954410.1016/j.commatsci.2020.109544 ↗
40. G. Pilania, C. Wang, X. Jiang, S. Rajasekaran and R. Ramprasad, Accelerating materials property predictions using machine learning, Sci. Rep., 2013 https://doi.org/10.1038/srep0281010.1038/srep02810 ↗
41. Y. Cao, C. Chen, S. Xu, R. Zhao, K. Guo, T. Hu, H. Liao, J. Wang and Z. Ren, Machine learning assisted prediction and optimization of mechanical properties for laser powder bed fusion of Ti6Al4V alloy, Addit. Manuf., 2024, 91, p 104341. https://doi.org/10.1016/j.addma.2024.10434110.1016/j.addma.2024.104341 ↗
42. V. Thakur, R. Kumar, R. Kumar, R. Singh and V. Kumar, Hybrid additive manufacturing of highly sustainable Polylactic acid -Carbon Fiber-Polylactic acid sandwiched composite structures: Optimization and machine learning, J. Thermoplast. Compos. Mater., 2023, 37(2), p 466–492. https://doi.org/10.1177/0892705723118018610.1177/08927057231180186 ↗
43. M. Kumar, R. Kumar and R. Kumar, Material extrusion-based additive manufacturing of sandwiched acrylonitrile butadiene styrene/glass fibers composites: Machine learning approach to model tensile stress, Polym. Compos., 2024 https://doi.org/10.1002/pc.2882310.1002/pc.28823 ↗
44. N. Ranjan, R. Kumar, R. Kumar, R. Kaur and S. Singh, Investigation of fused filament fabrication-based manufacturing of ABS-Al composite structures: prediction by machine learning and optimization, J. Mater. Eng. Perform., 2022, 32(10), p 4555–4574. https://doi.org/10.1007/s11665-022-07431-x10.1007/s11665-022-07431-x ↗
45. K. Badogu, V. Thakur, R. Kumar, R. Kumar and S. Singh, Acrylonitrile butadiene styrene-ZrO2 composites for roller burnishing as post-processing of 3D printed parts: machine learning modeling using classification and regression trees, J. Mater. Eng. Perform., 2023 https://doi.org/10.1007/s11665-023-08620-y10.1007/s11665-023-08620-y ↗
46. A.E. Hoerl and R.W. Kennard, Ridge regression: biased estimation for nonorthogonal problems, Technometrics, 2000, 42(1), p 80. https://doi.org/10.2307/127143610.2307/1271436 ↗
47. C. South and E. Egros, Forecasting college football game outcomes using modern modeling techniques, J. Sports Anal., 2020, 6(1), p 25–33. https://doi.org/10.3233/jsa-19031410.3233/jsa-190314 ↗
48. J. Zhang, H. Zhang and R. Qi, A study of size threshold for cooling effect in urban parks and their cooling accessibility and equity, Sci. Reports, 2024 https://doi.org/10.1038/s41598-024-67277-210.1038/s41598-024-67277-2 ↗
49. B. Ahmadi-Nedushan, A. St-Hilaire, M. Bérubé, É. Robichaud, N. Thiémonge and B. Bobée, A review of statistical methods for the evaluation of aquatic habitat suitability for instream flow assessment, River Res. Appl., 2006, 22(5), p 503–523. https://doi.org/10.1002/rra.91810.1002/rra.918 ↗
50. Zeemering, S. Sparse estimation: applications in atrial fibrillation. https://doi.org/10.26481/dis.20151126sz10.26481/dis.20151126sz ↗
51. Y. Jiang, J. Lu, T. Li and W. Lv, Enhanced word-unit broad learning system with sememes, IEEE Access, 2023, 11, p 109462–109476. https://doi.org/10.1109/access.2023.332243910.1109/access.2023.3322439 ↗
52. S.K. Safi, M. Alsheryani, M. Alrashdi, R. Suleiman, D. Awwad and Z.N. Abdalla, Optimizing linear regression models with lasso and ridge regression: a study on uae financial behavior during covid-19, Migrat. Lett., 2023, 20(6), p 139–153. https://doi.org/10.59670/ml.v20i6.346810.59670/ml.v20i6.3468 ↗
53. S. Huang, W. You, Q. Wang, J. Liu, Q. Han and J. Wang, Estimation of chlorophyll content in pepper leaves using spectral transmittance red-edge parameters, Int. J. Agricult. Biol. Eng., 2022, 15(5), p 85–90. https://doi.org/10.25165/j.ijabe.20221505.735010.25165/j.ijabe.20221505.7350 ↗
54. J. Fan and Y. Fan, High-dimensional classification using features annealed independence rules, Annals Stat., 2008 https://doi.org/10.1214/07-aos50410.1214/07-aos504 ↗
55. Botsas, T., Mason, L., Matar, O., & Pan, I. (2022). Rule-based evolutionary bayesian learning. arXiv preprint arXiv:2202.13778 https://doi.org/10.48550/arxiv.2202.1377810.48550/arxiv.2202.13778 ↗
56. S. Kim, D. Fong and W. DeSarbo, Model-based segmentation featuring simultaneous segment-level variable selection, J. Mark. Res., 2012, 49(5), p 725–736. https://doi.org/10.1509/jmr.10.039510.1509/jmr.10.0395 ↗
57. P. Heasler, C. Posse, J. Hylden and K. Anderson, Nonlinear bayesian algorithms for gas plume detection and estimation from hyper-spectral thermal image data, Sensors, 2007, 7(6), p 905–920. https://doi.org/10.3390/s706090510.3390/s7060905 ↗
58. H. Chipman, E. George, R. Gramacy and R. McCulloch, Bayesian treed response surface models, Wiley Interdisc. Rev. Data Mining Knowl. Disc., 2013, 3(4), p 298–305. https://doi.org/10.1002/widm.109410.1002/widm.1094 ↗
59. C. Rohrbeck, D. Costain and A. Frigessi, Bayesian spatial monotonic multiple regression, Biometrika, 2018, 105(3), p 691–707. https://doi.org/10.1093/biomet/asy01910.1093/biomet/asy019 ↗
60. L. Held and R. Sauter, Adaptive prior weighting in generalized regression, Biometrics, 2016, 73(1), p 242–251. https://doi.org/10.1111/biom.1254110.1111/biom.12541 ↗
61. R. Wolf and K. Tollefsen, A bayesian approach to incorporating spatiotemporal variation and uncertainty limits into modeling of predicted environmental concentrations from chemical monitoring campaigns, Environ. Sci. Technol., 2021, 55(3), p 1699–1709. https://doi.org/10.1021/acs.est.0c0626810.1021/acs.est.0c06268 ↗
62. Matt, P., Ziegler, R., Danilo, B., Roth, M., & Huber, M. (2022). A nested genetic algorithm for explaining classification data sets with decision rules. https://doi.org/10.48550/arxiv.2209.0757510.48550/arxiv.2209.07575 ↗
63. S. Forrest, Genetic algorithms: principles of natural selection applied to computation, Science, 1993, 261(5123), p 872–878. https://doi.org/10.1126/science.834643910.1126/science.8346439 ↗
64. Xu J, Zhang H, Zhu X, Li L, Ding P. Curve surface fitting based on an improved genetic algorithm. In2013 6th International Congress on Image and Signal Processing (CISP) 2013 Dec 16 (Vol. 2, pp. 747-752). IEEE.10.1109/CISP.2013.6745264 ↗
65. J. Borah and M. Chandrasekaran, Prediction and optimization of tensile strength of additively manufactured PEEK biopolymer using machine learning techniques, Model. Exp. and Des, Multiscale and Multidiscip, 2024. https://doi.org/10.1007/s41939-024-00505-410.1007/s41939-024-00505-4 ↗
66. J. Borah and M. Chandrasekaran, Development of ANN model for predicting mechanical properties of 3D printed PEEK polymer using FDM and optimization of process parameters for better mechanical properties, Phys. Scr., 2024 https://doi.org/10.1088/1402-4896/ad7f0f10.1088/1402-4896/ad7f0f ↗
67. J. Borah and M. Chandrasekaran, Predictive modeling and optimization of Rockwell hardness of additively manufactured PEEK using RSM, Physica Scripta, ANFIS and RNN integrated with PSO, 2024. https://doi.org/10.1088/1402-4896/ad651410.1088/1402-4896/ad6514 ↗
68. S. Abdi, M. Ashjaei and S. Mubeen, Cost-aware workflow offloading in edge-cloud computing using a genetic algorithm, J. Supercomput., 2024 https://doi.org/10.1007/s11227-024-06341-010.1007/s11227-024-06341-0 ↗
69. X. Xia, H. Qiu, X. Xu and Y. Zhang, Multi-objective workflow scheduling based on genetic algorithm in cloud environment, Inf. Sci., 2022, 606, p 38–59. https://doi.org/10.1016/j.ins.2022.05.05310.1016/j.ins.2022.05.053 ↗