Artificial Neural Network Performance Modeling and Evaluation of Additive Manufacturing 3D Printed Parts

Authors

  • Sivarao Subramonian Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, Malaysia
  • Kumaran Kadirgama Faculty of Mechanical and Automotive Engineering Technology, Universiti Malaysia Pahang, Malaysia
  • Abdulkareem Sh. Mahdi Al-Obaidi Faculty of Innovation and Technology, School of Engineering, Taylor's University, Malaysia
  • Mohd Shukor Mohd Salleh Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, Malaysia
  • Umesh Kumar Vatesh Mechanical Engineering Department, Amity University, India
  • Satish Pujari Faculty of Mechanical Engineering, Lendi Institute of Engineering and Technology, India
  • Dharsyanth Rao Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, Malaysia
  • Devarajan Ramasamy Faculty of Mechanical and Automotive Engineering Technology, Universiti Malaysia Pahang, Malaysia
Volume: 13 | Issue: 5 | Pages: 11677-11684 | October 2023 | https://doi.org/10.48084/etasr.6185

Abstract

This research article presents a comprehensive study on the performance modeling of 3D printed parts using Artificial Neural Networks (ANNs). The aim of this study is to optimize the mechanical properties of 3D printed components through accurate prediction and analysis. The study focuses on the widely employed Fused Deposition Modeling (FDM) technique. The ANN model is trained and validated using experimental data, incorporating input parameters such as temperature, speed, infill direction, and layer thickness to predict mechanical properties including yield stress, Young's modulus, ultimate tensile strength, flexural strength, and elongation at fracture. The results demonstrate the effectiveness of the ANN model with an average error below 10%. The study also reveals the significant impact of process parameters on the mechanical properties of 3D printed parts and highlights the potential for optimizing these parameters to enhance the performance of printed components. The findings of this research contribute to the field of additive manufacturing by providing valuable insights into the optimization of 3D printing processes and facilitating the development of high-performance 3D printed components.

Keywords:

3D printing, Artificial Neural Networks (ANNs), predictive modeling, Fused Deposition Modeling (FDM), mechanical properties

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References

N. Grimmelsmann, M. Kreuziger, M. Korger, H. Meissner, and A. Ehrmann, "Adhesion of 3D printed material on textile substrates," Rapid Prototyping Journal, vol. 24, no. 1, pp. 166–170, Jan. 2018.

A. A. Bakır, R. Atik, and S. Özerinç, "Effect of fused deposition modeling process parameters on the mechanical properties of recycled polyethylene terephthalate parts," Journal of Applied Polymer Science, vol. 138, no. 3, 2021, Art. no. 49709.

S. Rouf, A. Raina, M. Irfan Ul Haq, N. Naveed, S. Jeganmohan, and A. Farzana Kichloo, "3D printed parts and mechanical properties: Influencing parameters, sustainability aspects, global market scenario, challenges and applications," Advanced Industrial and Engineering Polymer Research, vol. 5, no. 3, pp. 143–158, Jul. 2022.

M. Behzadnasab, A. A. Yousefi, D. Ebrahimibagha, and F. Nasiri, "Effects of processing conditions on mechanical properties of PLA printed parts," Rapid Prototyping Journal, vol. 26, no. 2, pp. 381–389, Jan. 2019.

T. M. Joseph et al., "3D printing of polylactic acid: recent advances and opportunities," The International Journal of Advanced Manufacturing Technology, vol. 125, no. 3, pp. 1015–1035, Mar. 2023.

K. Elhattab, S. B. Bhaduri, and P. Sikder, "Influence of Fused Deposition Modelling Nozzle Temperature on the Rheology and Mechanical Properties of 3D Printed β-Tricalcium Phosphate (TCP)/Polylactic Acid (PLA) Composite," Polymers, vol. 14, no. 6, Jan. 2022, Art. no. 1222.

A. Shahrjerdi, M. Karamimoghadam, and M. Bodaghi, "Enhancing Mechanical Properties of 3D-Printed PLAs via Optimization Process and Statistical Modeling," Journal of Composites Science, vol. 7, no. 4, Apr. 2023, Art. no. 151.

H. Zhang and W. Sun, "Mechanical properties and failure behavior of 3D printed thermoplastic composites using continuous basalt fiber under high-volume fraction," Defence Technology, Aug. 2022.

F. Ning, W. Cong, J. Qiu, J. Wei, and S. Wang, "Additive manufacturing of carbon fiber reinforced thermoplastic composites using fused deposition modeling," Composites Part B: Engineering, vol. 80, pp. 369–378, Oct. 2015.

D. N. Fente and D. Kumar Singh, "Weather Forecasting Using Artificial Neural Network," in 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, India, Apr. 2018, pp. 1757–1761.

E. Ayan and H. M. Ünver, "Diagnosis of Pneumonia from Chest X-Ray Images Using Deep Learning," in 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), Istanbul, Turkey, Apr. 2019.

D. Merayo, A. Rodríguez-Prieto, and A. M. Camacho, "Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys," Materials, vol. 13, no. 22, Jan. 2020, Art. no. 5227.

A. D. Tura, H. G. Lemu, H. B. Mamo, and A. J. Santhosh, "Prediction of tensile strength in fused deposition modeling process using artificial neural network and fuzzy logic," Progress in Additive Manufacturing, vol. 8, no. 3, pp. 529–539, Jun. 2023.

H. Sondagar, S. S. Bhadauria, and V. S. Sharma, "Artificial neural network (ANN) based prediction of process parameters in additive manufacturing," IOP Conference Series: Materials Science and Engineering, vol. 1136, no. 1, Mar. 2021, Art. no. 012026.

R. Srinivasan, T. Pridhar, L. S. Ramprasath, N. S. Charan, and W. Ruban, "Prediction of tensile strength in FDM printed ABS parts using response surface methodology (RSM)," Materials Today: Proceedings, vol. 27, pp. 1827–1832, Jan. 2020.

M. Çallı, E. İ. Albak, and F. Öztürk, "Prediction and Optimization of the Design and Process Parameters of a Hybrid DED Product Using Artificial Intelligence," Applied Sciences, vol. 12, no. 10, Jan. 2022, Art. no. 5027.

I. Rojek, D. Mikołajewski, P. Kotlarz, K. Tyburek, J. Kopowski, and E. Dostatni, "Traditional Artificial Neural Networks Versus Deep Learning in Optimization of Material Aspects of 3D Printing," Materials, vol. 14, no. 24, Jan. 2021, Art. no. 7625.

O. A. Mohamed, S. H. Masood, and J. L. Bhowmik, "Influence of processing parameters on creep and recovery behavior of FDM manufactured part using definitive screening design and ANN," Rapid Prototyping Journal, vol. 23, no. 6, pp. 998–1010, Jan. 2017.

D. G. Zisopol, I. Nae, A. I. Portoaca, and I. Ramadan, "A Statistical Approach of the Flexural Strength of PLA and ABS 3D Printed Parts," Engineering, Technology & Applied Science Research, vol. 12, no. 2, pp. 8248–8252, Apr. 2022.

D. G. Zisopol, I. Nae, A. I. Portoaca, and I. Ramadan, "A Theoretical and Experimental Research on the Influence of FDM Parameters on Tensile Strength and Hardness of Parts Made of Polylactic Acid," Engineering, Technology & Applied Science Research, vol. 11, no. 4, pp. 7458–7463, Aug. 2021.

D. G. Zisopol, M. Minescu, and D. V. Iacob, "A Theoretical-Experimental Study on the Influence of FDM Parameters on the Dimensions of Cylindrical Spur Gears Made of PLA," Engineering, Technology & Applied Science Research, vol. 13, no. 2, pp. 10471–10477, Apr. 2023.

G. S. Fesghandis, A. Pooya, M. Kazemi, and Z. N. Azimi, "Comparison of Multilayer Perceptron and Radial Basis Function Neural Networks in Predicting the Success of New Product Development," Engineering, Technology & Applied Science Research, vol. 7, no. 1, pp. 1425–1428, Feb. 2017.

S. Ranjan and V. Singh, "ANN and GRNN-Based Coupled Model for Flood Inundation Mapping of the Punpun River Basin," Engineering, Technology & Applied Science Research, vol. 13, no. 1, pp. 9941–9946, Feb. 2023.

A. S. Kote and D. V. Wadkar, "Modeling of Chlorine and Coagulant Dose in a Water Treatment Plant by Artificial Neural Networks," Engineering, Technology & Applied Science Research, vol. 9, no. 3, pp. 4176–4181, Jun. 2019.

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How to Cite

[1]
S. Subramonian, “Artificial Neural Network Performance Modeling and Evaluation of Additive Manufacturing 3D Printed Parts”, Eng. Technol. Appl. Sci. Res., vol. 13, no. 5, pp. 11677–11684, Oct. 2023.

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