Lattice Structure Optimization of 3D Printed TPMS under Different Loading Conditions Using Regression Machine Learning

Motgi, Rakesh; Jatti, Vijay Kumar; Tamboli, Shahid; Dhalait, Javed

doi:10.22075/macs.2025.35429.1732

اداره چاپ و انتشارات دانشگاه سمنان

تعداد نشریات	21
تعداد شماره‌ها	654
تعداد مقالات	9,577
تعداد مشاهده مقاله	68,498,245
تعداد دریافت فایل اصل مقاله	47,952,137

	Lattice Structure Optimization of 3D Printed TPMS under Different Loading Conditions Using Regression Machine Learning
Mechanics of Advanced Composite Structures
مقاله 11، دوره 12، شماره 3 - شماره پیاپی 26، بهمن 2025، صفحه 597-613 اصل مقاله (1.2 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22075/macs.2025.35429.1732
نویسندگان
Rakesh Motgi¹؛ Vijay Kumar Jatti²؛ Shahid Tamboli^* ³؛ Javed Dhalait¹
¹Department of Mechanical Engineering, A. G. Patil Polytechnic Institute, Solapur, 413008, Maharashtra. India
²Department of Mechanical Engineering, Bennett University, 201310, Greater Noida India
³Department of Mechanical Engineering, Symbiosis Institute of Technology, Symbiosis International University, Pune, 412115, Maharashtra. India
تاریخ دریافت: 04 مهر 1403، تاریخ بازنگری: 30 آذر 1403، تاریخ پذیرش: 13 دی 1403
چکیده
Modern manufacturing techniques have been significantly transformed by additive manufacturing (AM). Because of its capabilities like customized part manufacturing and, the ability to manufacture intricate and complex parts with reduced waste of material, additive manufacturing is becoming more popular. However, the properties of the parts manufactured by this method significantly vary with the variation in process parameters. Optimizing these parameters helps to extract enhanced mechanical properties. In addition, lattice structures have created new possibilities for increasing strength while lowering part weight through optimized lattice structures. The effect of lattice structure and process parameters on the specimen made using the fused deposition method (FDM) is the major focus of this study. In this work, three distinct TPMS-base (Triply Periodic Minimal Surfaces) lattice architectures are examined for a range of layer height levels. Investigations are conducted using the L9 orthogonal array. The FDM technique uses PLA plastic filament. The Taguchi method was used for optimization, and samples were evaluated on the UTM and Izod impact testing machines. Moreover, an ML model is created by applying machine learning to the collected data. In tensile and impact test data, neural network and Gaussian process regression models showed low error rates and predicted good accuracy. The neural network model for the flexural test data showed a moderate level of accuracy, suggesting potential for improvement. The models' performance was highlighted by their low RMSE, MSE, and MAE values, which show that they can predict material properties. The overall findings indicated that layer height has less impact on tensile and flexural strength than lattice structure. In contrast to the lattice structure, layer height influences the toughness.
کلیدواژه‌ها
Fused deposition modeling؛ Lattice structure؛ PLA؛ Optimization؛ Taguchi method

مراجع
[1] Tang, C., Liu, J., Yang, Y., Liu, Y., Jiang, S. and Hao, W., 2020. Effect of process parameters on mechanical properties of 3D printed PLA lattice structures. Composites Part C: Open Access, [online] 3, p.100076. doi:https://doi.org/10.1016/j.jcomc.2020.100076. [2] Md Mazedur Rahman, Sultana, J., Saiaf Bin Rayhan and Ahmed, A., 2023. Optimization of FDM manufacturing parameters for the compressive behavior of cubic lattice cores: an experimental approach by Taguchi method. The International Journal of Advanced Manufacturing Technology. doi:https://doi.org/10.1007/s00170-023-12342-9. [3] Mani, M., Karthikeyan, A.G., Kalaiselvan, K., Muthusamy, P. and Muruganandhan, P., 2022. Optimization of FDM 3-D printer process parameters for surface roughness and mechanical properties using PLA material. Materials Today: Proceedings. doi:https://doi.org/10.1016/j.matpr.2022.05.422. [4] Jing, S., Li, W., Ma, G., Cao, X., Zhang, L., Fang, L., Meng, J., Shao, Y., Shen, B., Zhang, C., Li, H., Wan, Z. and Xiao, D., 2023. Enhancing Mechanical Properties of 3D Printing Metallic Lattice Structure Inspired by Bambusa Emeiensis. Materials, 16(7), pp.2545–2545. doi:https://doi.org/10.3390/ma16072545. [5] Doodi, R. and Gunji, B.M., 2023. Prediction and experimental validation approach to improve performance of novel hybrid bio-inspired 3D printed lattice structures using artificial neural networks. Scientific Reports, 13(1), p.7763. doi:https://doi.org/10.1038/s41598-023-33935-0. [6] Namvar, N., Moloukzadeh, I., Zolfagharian, A., Demoly, F. and Bodaghi, M., 2023. Bio-inspired design, modeling, and 3D printing of lattice-based scale model scooter decks. The International Journal of Advanced Manufacturing Technology. doi:https://doi.org/10.1007/s00170-023-11185-8. [7] Harish, A., Alsaleh, N.A., Mahmoud Ahmadein, Elfar, A.A., Djuansjah, J., Hany Hassanin, Mahmoud Ahmed El-Sayed and Essa, K., 2024. Designing Lightweight 3D-Printable Bioinspired Structures for Enhanced Compression and Energy Absorption Properties. Polymers, 16(6), pp.729–729. doi:https://doi.org/10.3390/polym16060729. [8] Fongsamootr, T., Thawon, I., Tippayawong, N., Tippayawong, K.Y. and Suttakul, P., 2022. Effect of print parameters on additive manufacturing of metallic parts: performance and sustainability aspects. Scientific Reports, [online] 12(1), p.19292. doi:https://doi.org/10.1038/s41598-022-22613-2. [9] Barbosa, W.S., Gioia, M.M., Temporão, G.P., Meggiolaro, M.A. and Gouvea, F.C., 2022. Impact of multi-lattice inner structures on FDM PLA 3D printed orthosis using Industry 4.0 concepts. International Journal on Interactive Design and Manufacturing (IJIDeM), 17(1), pp.371–383. doi:https://doi.org/10.1007/s12008-022-00962-6. [10] Li, B. and Shen, C., 2022. Solid Stress-Distribution-Oriented Design and Topology Optimization of 3D-Printed Heterogeneous Lattice Structures with Light Weight and High Specific Rigidity. Polymers, 14(14), p.2807. doi:https://doi.org/10.3390/polym14142807. [11] Shevchenko, V., Balabanov, S., Sychov, M. and Karimova, L., 2023. Prediction of Cellular Structure Mechanical Properties with the Geometry of Triply Periodic Minimal Surfaces (TPMS). ACS omega, 8(30), pp.26895-26905. doi:https://doi.org/10.1021/acsomega.3c01631. [12] Libonati, F., Graziosi, S., Ballo, F., Mognato, M. and Sala, G., 2021. 3D-Printed Architected Materials Inspired by Cubic Bravais Lattices. ACS Biomaterials Science & Engineering. doi:https://doi.org/10.1021/acsbiomaterials.0c01708. [13] Bogusz, P., Popławski, A., Stankiewicz, M. and Kowalski, B., 2022. Experimental Research of Selected Lattice Structures Developed with 3D Printing Technology. Materials, 15(1), p.378. doi:https://doi.org/10.3390/ma15010378. [14] Tkac, J., Samborski, S., Monkova, K. and Debski, H., 2020. Analysis of mechanical properties of a lattice structure produced with the additive technology. Composite Structures, 242, p.112138. doi:https://doi.org/10.1016/j.compstruct.2020.112138. [15] Alarifi, I.M., 2023. Mechanical properties and numerical simulation of FDM 3D printed PETG/carbon composite unit structures. Journal of Materials Research and Technology. doi:https://doi.org/10.1016/j.jmrt.2023.01.043. [16] Sombatmai, A., Tapracharoen, K., Uthaisangsuk, V., Msolli, S. and Promoppatum, P., 2024. Post-yielding and failure mechanism of additively manufactured triply periodic minimal surface lattice structures. Results in Engineering, 23, p.102364. doi:https://doi.org/10.1016/j.rineng.2024.102364. [17] Almesmari, A., Sheikh-Ahmad, J., Jarrar, F. and Bojanampati, S., 2023. Optimizing the specific mechanical properties of lattice structures fabricated by material extrusion additive manufacturing. Journal of Materials Research and Technology, [online] 22, pp.1821–1838. doi:https://doi.org/10.1016/j.jmrt.2022.12.024. [18] Alarifi, I.M., 2023. PETG/carbon fiber composites with different structures produced by 3D printing. Polymer Testing, 120, p.107949. doi:https://doi.org/10.1016/j.polymertesting.2023.107949. [19] Perween, S., Fahad, M. and Khan, M.A., 2021. Systematic Experimental Evaluation of Function Based Cellular Lattice Structure Manufactured by 3D Printing. Applied Sciences, 11(21), p.10489. doi:https://doi.org/10.3390/app112110489. [20] Güdür, C., Türkoğlu, T. and Eren, İ., 2023. Effect of Lattice Design and Process Parameters on the Properties of PLA, ABS AND PETG Polymers Produced by Fused Deposition Modelling. Journal of Materials and Mechatronics A, 4(2), pp.561–570. doi:https://doi.org/10.55546/jmm.1357217. [21] Higuera, S., Miralbes, R. and Ranz, D., 2021. Mechanical properties and energy–absorption capabilities of thermoplastic sheet gyroid structures. Mechanics of Advanced Materials and Structures, pp.1–15. doi:https://doi.org/10.1080/15376494.2021.1919803. [22] Kumar, A., Verma, S. and Jeng, J.-Y., 2020. Supportless Lattice Structures for Energy Absorption Fabricated by Fused Deposition Modeling. 3D Printing and Additive Manufacturing. doi:https://doi.org/10.1089/3dp.2019.0089. [23] Alemayehu, D.B. and Todoh, M., 2024. Enhanced Energy Absorption with Bioinspired Composite Triply Periodic Minimal Surface Gyroid Lattices Fabricated via Fused Filament Fabrication (FFF). Journal of Manufacturing and Materials Processing, 8(3), p.86. doi:https://doi.org/10.3390/jmmp8030086. [24] Liu, T., Zhao, W., Yao, Y., Lin, C., Zhao, H. and Cai, J., 2024. Mechanical and shape-memory properties of TPMS with hybrid configurations and materials. International Journal of Smart and Nano Materials, pp.1–25. doi:https://doi.org/10.1080/19475411.2024.2410289. [25] Viswanath, A., Khalil, M., Khan, A., Fahad Al Maskari, Cantwell, W.J. and Khan, K.A., 2024. A novel design strategy to enhance buckling resistance of thin-walled single-cell lattice structures via topology optimisation. Virtual and Physical Prototyping, 19(1). doi:https://doi.org/10.1080/17452759.2024.2345390. [26] Alkhatib, S.E., Xu, S., Lu, G., Karrech, A. and Sercombe, T.B., 2024. Rate-dependent behaviour of additively manufactured topology optimised lattice structures. Thin-Walled Structures, 198, p.111710. doi:https://doi.org/10.1016/j.tws.2024.111710. [27] Razi, S.S., Pervaiz, S., Susantyoko, R.A. and Alyammahi, M., 2024. Optimization of Environment-Friendly and Sustainable Polylactic Acid (PLA)-Constructed Triply Periodic Minimal Surface (TPMS)-Based Gyroid Structures. Polymers, 16(8), p.1175. doi:https://doi.org/10.3390/polym16081175. [28] Ursini, C. and Collini, L., 2021. FDM Layering Deposition Effects on Mechanical Response of TPU Lattice Structures. Materials, 14(19), p.5645. doi:https://doi.org/10.3390/ma14195645. [29] Xue, Y., Gao, P., Zhou, L. and Han, F., 2020. An Enhanced Three-Dimensional Auxetic Lattice Structure with Improved Property. Materials, 13(4), pp.1008–1008. doi:https://doi.org/10.3390/ma13041008. [30] Qin, D., Sang, L., Zhang, Z., Lai, S. and Zhao, Y., 2022. Compression Performance and Deformation Behavior of 3D-Printed PLA-Based Lattice Structures. Polymers, [online] 14(5), pp.1062–1062. doi:https://doi.org/10.3390/polym14051062. [31] Antony, S., Cherouat, A. and Montay, G., 2020. Fabrication and Characterization of Hemp Fibre Based 3D Printed Honeycomb Sandwich Structure by FDM Process. Applied Composite Materials, 27(6), pp.935–953. doi:https://doi.org/10.1007/s10443-020-09837-z. [32] Abusabir, A., Khan, M.A., Asif, M. and Khan, K.A., 2022. Effect of Architected Structural Members on the Viscoelastic Response of 3D Printed Simple Cubic Lattice Structures. Polymers, 14(3), p.618. doi:https://doi.org/10.3390/polym14030618. [33] Beloshenko, V., Beygelzimer, Y., Chishko, V., Savchenko, B., Sova, N., Verbylo, D., Voznyak, A. and Vozniak, I., 2021. Mechanical Properties of Flexible TPU-Based 3D Printed Lattice Structures: Role of Lattice Cut Direction and Architecture. Polymers, 13(17), p.2986. doi:https://doi.org/10.3390/polym13172986. [34] Shu-Yu Jhou, Hsu, C.-C. and Yeh, J.-C., 2021. The Dynamic Impact Response of 3D-Printed Polymeric Sandwich Structures with Lattice Cores: Numerical and Experimental Investigation. Polymers, 13(22), pp.4032–4032. doi:https://doi.org/10.3390/polym13224032. [35] Yan, L., Zhu, K., Zhang, Y., Zhang, C. and Zheng, X., 2020. Effect of Absorbent Foam Filling on Mechanical Behaviors of 3D-Printed Honeycombs. Polymers, 12(9), p.2059. doi:https://doi.org/10.3390/polym12092059. [36] Choudhry, N.K., Panda, B. and Dixit, U.S., 2023. Energy Absorption Characteristics of Fused Deposition Modeling 3D Printed Auxetic Re-entrant Structures: A Review. Journal of Materials Engineering and Performance, 32(20), pp.8981–8999. doi:https://doi.org/10.1007/s11665-023-08243-3. [37] Buican, G.R., Zaharia, S.-M., Pop, M.A., Chicos, L.-A., Lancea, C., Stamate, V.-M. and Pascariu, I.S., 2021. Fabrication and Characterization of Fiber-Reinforced Composite Sandwich Structures Obtained by Fused Filament Fabrication Process. Coatings, 11(5), p.601. doi:https://doi.org/10.3390/coatings11050601. [38] He, Q., Hou, Y., Li, X., Li, S. and Meng, L., 2023. Investigation on the Compressive Behavior of Hybrid Polyurethane (PU)-Foam-Filled Hyperbolic Chiral Lattice Metamaterial. Polymers, 15(9), pp.2030–2030. doi:https://doi.org/10.3390/polym15092030. [39] Ahmed, A.M.R.M., Mahdi, E., Oosterhuis, K., Dean, A. and Cabibihan, J.J., 2023. Mechanical and energy absorption properties of 3D-printed honeycomb structures with Voronoi tessellations. Frontiers in Mechanical Engineering, 9, p.1204893. doi:https://doi.org/10.3389/fmech.2023.1204893. [40] Peloquin, J., Kirillova, A., Rudin, C., L. Catherine Brinson and Gall, K., 2023. Prediction of tensile performance for 3D printed photopolymer gyroid lattices using structural porosity, base material properties, and machine learning. Materials & Design, 232, pp.112126–112126. doi:https://doi.org/10.1016/j.matdes.2023.112126. [41] Poddar, P., Olles, M. and Cormier, D., 2022. Mechanical Response of Carbon Composite Octet Truss Structures Produced via Axial Lattice Extrusion. Polymers, 14(17), p.3553. doi:https://doi.org/10.3390/polym14173553. [42] Challapalli, A. and Li, G., 2021. Machine learning assisted design of new lattice core for sandwich structures with superior load carrying capacity. Scientific Reports, 11(1), p.18552. doi:https://doi.org/10.1038/s41598-021-98015-7. [43] Santiago, R., Ramos, H., AlMahri, S., Banabila, O., Haleimah Alabdouli, Lee, D.-W., Aziz, A., Rajput, N., Alves, M. and Guan, Z., 2023. Modelling and optimisation of TPMS-based lattices subjected to high strain-rate impact loadings. International journal of impact engineering, 177, pp.104592–104592. doi:https://doi.org/10.1016/j.ijimpeng.2023.104592. [44] Sharma, P., Vaid, H., Vajpeyi, R., Shubham, P., Agarwal, K.M. and Bhatia, D., 2022. Predicting the dimensional variation of geometries produced through FDM 3D printing employing supervised machine learning. Sensors International, 3, p.100194. doi:https://doi.org/10.1016/j.sintl.2022.100194. [45] Jayasudha, M., Elangovan, M., Mahdal, M. and Priyadarshini, J., 2022. Accurate Estimation of Tensile Strength of 3D Printed Parts Using Machine Learning Algorithms. Processes, 10(6), p.1158. doi:https://doi.org/10.3390/pr10061158.
آمار تعداد مشاهده مقاله: 713 تعداد دریافت فایل اصل مقاله: 575

سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب

پیوندهای مفید

پیوندهای مفید

آمار

Lattice Structure Optimization of 3D Printed TPMS under Different Loading Conditions Using Regression Machine Learning