SavedAdd your journal

Journals / Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi / 2026 / Cilt: 32 Sayı: 4

Examination of 3D printing parameters using machine learning

Makine öğrenmesi ile 3D yazdırma parametrelerinin incelenmesi

Mehmet Altuğ, Yakup Yılmaz

Pages
616–627
Publication Date
2026-07-13
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗

Abstract

In this study, the mechanical properties of tensile samples produced in 3D printers with the fused deposition method (FDM) were investigated. Here, the parameters such as layer (filament) thickness, infill type and support angle in the FDM method were examined. The production was produced with Up-right and edge directions. As a result of the experiments, the best layer thickness in terms of tensile strength was 0.09 mm, and the infill type was full infill type, while different results were obtained in the support angle. According to the variance analysis (ANOVA) values, it was observed that the layer thickness and infill type were quite effective on the tensile strength, but the support angle was at a negligible level. In the second stage, the results were estimated with xgboost and catboost from the machine learning algorithms and linear regression models. The most effective algorithm on the examined mechanical properties was determined as the catboost algorithm.

Özet

Bu çalışmada ergiyik yığma metodu (Fused Deposition Modelleme/FDM) yöntemi ile 3D yazıcılarda üretilen çekme numunelerinin mekanik özellikleri incelendi. Burada FDM yöntemindeki katman (filament) kalınlığı, dolgu tipi ve support açısı gibi parametreler incelendi. Üretim Up-right ve edge yönleri ve her bir yön için Taguchi L25 deney tasarımıyla üretildi. Deneyler neticesinde çekme mukavemeti açısından en iyi katman kalınlığı 0,09 mm, dolgu tipi olarak full dolgu tipi olurken support açısında farklı sonuçlar elde edildi. Varyans analizi (ANOVA) değerlerine göre parametrelerden katman kalınlığı ve dolgu tipinin çekme mukavemeti üzerinde oldukça etkili olduğu ancak support açısının göz ardı edilebilecek düzeyde olduğu gözlemlendi. İkinci aşamada sonuçlar makine öğrenmesi algoritmalarından xgboost ve catboost ile ve linear regression ile tahmin modelleri yapıldı. İncelenen mekanik özellikler üzerinde en etkin algoritma catboost algoritması olarak belirlendi.

Keywords
🔖

Cite This Article

Altuğ, M., Yılmaz, Y. (2026). Examination of 3D printing parameters using machine learning. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 32 Sayı: 4, 616–627. https://doi.org/10.65206/pajes.91679

📄 Full Text PDF Viewer

References (37)

  1. [1] Cantrell J, Rohde S, Damiani D, Gurnani R, DiSandro L, Anton J, Young A, Jerez A, Steinbach D, Kroese C, Ifju PG. “Experimental characterization of the mechanical properties of 3D-printed ABS and polycarbonate parts”. Rapid Prototyping Journal, 23(4), 811-824, 2017.
  2. [2] Sood AK, Ohdar RK, Mahapatra SS. “Parametric appraisal of mechanical property of fused deposition modelling processed parts”. Materials and Design, 31(1), 287-295, 2010.
  3. [3] Tekinalp HL, Meng XT, Lu Y, Kunc V, Love LJ, Peter WH, Ozcan S. “High modulus biocomposites via additive manufacturing: Cellulose nanofibril networks as ‘microsponges’”. Composites Part B: Engineering, 173, 106817, 2019.
  4. [4] Ning F, Cong W, Hu Y, Wang H. “Additive manufacturing of carbon fiber-reinforced plastic composites using fused deposition modeling: Effects of process parameters on tensile properties”. Journal of Composite Materials, 51(4), 451-462, 2017.
  5. [5] Sa’ude N, Masood SH, Nikzad M, Ibrahim MHI. “Dynamic Mechanical Properties of Copper-ABS Composites for FDM Feedstock”. International Journal of Engineering Research and Applications, 3(3), 1257-1263, 2013.
  6. [6] Bacak S, Varol Özkavak H, Tatlı M. “FDM Yöntemi ile Üretilen PLA Numunelerin Çekme Özelliklerine İşlem Parametrelerinin Etkisinin İncelenmesi”. Mühendislik Bilimleri ve Tasarım Dergisi, 9(1), 209-216, 2021.
  7. [7] Evlen H. “Doluluk Oranının 3B Yazıcıda Üretilen TPU ve TPE Numunelerinin Mekanik Özellikleri Üzerine Etkilerinin İncelenmesi”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 21(63), 793-804, 2019.
  8. [8] Günay M, Gündüz S, Yılmaz H, Yaşar N, Kaçar R. “PLA Esaslı Numunelerde Çekme Dayanımı İçin 3D Baskı İşlem Parametrelerinin Optimizasyonu”. Politeknik Dergisi, 23(1), 73-79, 2020.
  9. [9] Sezer HK, Eren O, Börklü HR, Özdemir V. “Karbon Fiber Takviyeli Polimer Kompozitlerin Ergiyik Biriktirme Yöntemi İle Eklemeli İmalatı: Fiber Oranı ve Yazdırma Parametrelerinin Mekanik Özelliklere Etkisi”. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 34(2), 663-674, 2019.
  10. [10] Demir S. “The effect of printing parameters on hardness in the production of Poli lactic acid (PLA)-based samples with a 3D printer”. Pamukkale University Journal of Engineering Sciences, 30(2), 136-144, 2024.
  11. [11] Malashin I, Tynchenko V, Gantimurov A, Nelyub V, Borodulin A. “Boosting-Based Machine Learning Applications in Polymer Science: A Review”. Polymers, 17(4), 499, 2025.
  12. [12] Zhang L, Janosik D. “Enhanced short-term load forecasting with hybrid machine learning models: CatBoost and XGBoost approaches”. Expert Systems with Applications, 241, 122686, 2024.
  13. [13] Mesghali H, Akhlaghi B, Gozalpour N, Mohammadpour J, Salehi F, Abbassi R. “Predicting maximum pitting corrosion depth in buried transmission pipelines: Insights from tree-based machine learning and identification of influential factors”. Process Safety and Environmental Protection, 187, 1269-1285, 2024.
  14. [14] Dhaliwal SS, Al Nahid A, Abbas R. “Effective intrusion detection system using XGBoost”. Information (Switzerland), 9(7), 149, 2018.
  15. [15] Thongsuwan S, Jaiyen S, Padcharoen A, Agarwal P. “ConvXGB: A new deep learning model for classification problems based on CNN and XGBoost”. Nuclear Engineering and Technology, 53(2), 522-531, 2021.
  16. [16] Zabin R, Haque KF, Abdelgawad A. “PredXGBR: A Machine Learning Framework for Short-Term Electrical Load Prediction”. Electronics (Switzerland), 13(22), 4521, 2024.
  17. [17] Tian J, Tsai PW, Tsai K, Cai X, Xiao H, Yu K, Zhao W, Chen J. “Synergetic Focal Loss for Imbalanced Classification in Federated XGBoost”. IEEE Transactions on Artificial Intelligence, 5(2), 647-660, 2024.
  18. [18] Alsulamy S. “Predicting construction delay risks in Saudi Arabian projects: A comparative analysis of CatBoost, XGBoost, and LGBM”. Expert Systems with Applications, 268, 126268, 2025.
  19. [19] Zhuo H, Li T, Lu W, Zhang Q, Ji L, Li J. “Prediction model for spontaneous combustion temperature of coal based on PSO-XGBoost algorithm”. Scientific Reports, 15(1), 2752, 2025.
  20. [20] Ueki Y, Seko N, Maekawa Y. “Machine learning approach for prediction of the grafting yield in radiation-induced graft polymerization”. Applied Materials Today, 25, 101158, 2021.
  21. [21] Cevik ZA, Ozsoy K, Ercetin A, Sariisik G. “Machine Learning-Driven Optimization of Machining Parameters Optimization for Cutting Forces and Surface Roughness in Micro-Milling of AlSi10Mg Produced by Powder Bed Fusion Additive Manufacturing”. Applied Sciences (Switzerland), 15(12), 6553, 2025.
  22. [22] Wang X, Wen L, Chai R. “Optimization of Laser Additive Manufacturing Process Based on XGBoost Algorithm”. Journal of The Institution of Engineers (India): Series C, 105, 1581-1590, 2024.
  23. [23] L. Wang, W. Zhang, Q. Bao, Q. Wang. “XGBoost-based failure prediction method for metal additive manufacturing equipment”. Proceedings of the 34th Chinese Control and Decision Conference, Hefei, China, 2022, 3059-3064
  24. [24] Dabbagh SR, Ozcan O, Tasoglu S. “Machine learning-enabled optimization of extrusion-based 3D printing”. Methods, 206, 27-40, 2022.
  25. [25] Zhu X, Jiang F, Guo C, Wang Z, Dong T, Li H. “Prediction of melt pool shape in additive manufacturing based on machine learning methods”. Optics and Laser Technology, 159, 108964, 2023.
  26. [26] Zhang Z, Liu Z, Wu D. “Prediction of melt pool temperature in directed energy deposition using machine learning”. Additive Manufacturing, 37, 101692, 2021.
  27. [27] Huang Y, Tian X, Zheng Z, Li D, Malakhov AV, Polilov AN. “Multiscale concurrent design and 3D printing of continuous fiber reinforced thermoplastic composites with optimized fiber trajectory and topological structure”. Composite Structures, 285, 115241, 2022.
  28. [28] Zhang J, Yin C, Xu Y, Sing SL. “Machine learning applications for quality improvement in laser powder bed fusion: A state-of-the-art review”. International Journal of AI for Materials and Design, 1(1), 26-43, 2024.
  29. [29] Veeman D, Sudharsan S, Surendhar GJ, Shanmugam R, Guo L. “Machine learning model for predicting the hardness of additively manufactured acrylonitrile butadiene styrene”. Materials Today Communications, 35, 106147, 2023.
  30. [30] Garg A, Tai K. “An ensemble approach of machine learning in evaluation of mechanical property of the rapid prototyping fabricated prototype”. Applied Mechanics and Materials, 575, 493-496, 2014.
  31. [31] Agarwal R, Singh J, Gupta V. “Predicting the compressive strength of additively manufactured PLA-based orthopedic bone screws: A machine learning framework”. Polymer Composites, 43(8), 5663-5674, 2022.
  32. [32] Zhu JJ, Su ZQ, Wang QQ, Lan ZF, Chan FSF, Han ZB, Wang ZK, Wong SWF, Ngan ACF, “Surface quality prediction and quantitative evaluation of process parameter effects for 3D printing with transfer learning-enhanced gradient-boosting decision trees”. Expert Systems with Applications, 237, 121478, 2024.
  33. [33] Barrios J M, Romero P E. “Decision tree methods for predicting surface roughness in fused deposition modeling parts”. Materials, 12(16), 2574, 2019.
  34. [34] Boschetto A, Giordano V, Veniali F. “Surface roughness prediction in fused deposition modelling by neural networks”. International Journal of Advanced Manufacturing Technology, 67(9-12), 2727-2742, 2013.
  35. [35] Li Z, Zhang Z, Shi J, Wu D. “Prediction of surface roughness in extrusion-based additive manufacturing with machine learning”. Robotics and Computer-Integrated Manufacturing, 57, 488-495, 2019.
  36. [36] Hooda N, Chohan J S, Gupta R, Kumar R. “Deposition angle prediction of Fused Deposition Modeling process using ensemble machine learning”. ISA Transactions, 116, 121-128, 2021.
  37. [37] Douard A, Grandvallet C, Pourroy F, Vignat F. “An Example of Machine Learning Applied in Additive Manufacturing”. IEEE International Conference on Industrial Engineering and Engineering Management, 2019, 1746-1750, 2018.