Machine learning application for optimization of laser directed energy deposition process for aerospace component rapid prototyping in additive manufacturing

Machine learning application for optimization of laser directed energy deposition process for aerospace component rapid prototyping in additive manufacturing

ERTUGRUL Gökhan, ALIMOV Artem, SVIRIDOV Alexander, HÄRTEL Sebastian

Abstract. The paper proposes a methodology for determining the optimal L-DED parameters based on the minimum number planned of L-DED trials. A dataset compiled from planned L-DED experiments was used to train a machine learning model. The algorithm demonstrated a robust ability to predict the output metrics with notable accuracy and proposed a theoretical framework that modeled the complex relationships between the input variables and the resulting critical welding properties for AM. The application of the developed model and its comparison with conventional methods thus offers a methodical approach to determining the optimum process parameters in advance. This is a step towards the development and production of additively manufactured components for future digital twin application in the aerospace industry.

Keywords
Directed Energy Deposition, Additive Manufacturing, Machine Learning, Digital Twin, Rapid Prototyping

Published online 4/24/2024, 12 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: ERTUGRUL Gökhan, ALIMOV Artem, SVIRIDOV Alexander, HÄRTEL Sebastian, Machine learning application for optimization of laser directed energy deposition process for aerospace component rapid prototyping in additive manufacturing, Materials Research Proceedings, Vol. 41, pp 271-282, 2024

DOI: https://doi.org/10.21741/9781644903131-31

The article was published as article 31 of the book Material Forming

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

References
[1] G. Piscopo and L. Iuliano, “Current research and industrial application of laser powder directed energy deposition,” Int J Adv Manuf Technol, vol. 119, 11-12, pp. 6893–6917, 2022. https://doi.org/10.1007/s00170-021-08596-w
[2] I. Z. Era, M. A. Farahani, T. Wuest, and Z. Liu, “Machine learning in Directed Energy Deposition (DED) additive manufacturing: A state-of-the-art review,” Manufacturing Letters, vol. 35, pp. 689–700, 2023. https://doi.org/10.1016/j.mfglet.2023.08.079
[3] A. Abdulraheem, R. Abdullah Arshah, and H. Qin, “Evaluating the Effect of Dataset Size on Predictive Model Using Supervised Learning Technique,” International Journal of Software Engineering & Computer Sciences (IJSECS), vol. 1, pp. 75–84, 2015. https://doi.org/10.15282/ijsecs.1.2015.6.0006.
[4] Y. Zhu, Z. Yuan, M. M. Khonsari, S. Zhao, and H. Yang, “Small-Dataset Machine Learning for Wear Prediction of Laser Powder Bed Fusion Fabricated Steel,” Journal of Tribology, vol. 145, no. 9, p. 91101, 2023. https://doi.org/10.1115/1.4062368
[5] X. Zhu, F. Jiang, C. Guo, de Xu, Z. Wang, and G. Jiang, “Surface morphology inspection for directed energy deposition using small dataset with transfer learning,” Journal of Manufacturing Processes, vol. 93, pp. 101–115, 2023. https://doi.org/10.1016/j.jmapro.2023.03.016
[6] J. Xiong, G. Zhang, J. Hu, and L. Wu, “Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis,” Journal of Intelligent Manufacturing, vol. 25, no. 1, pp. 157–163, 2014. https://doi.org/10.1007/s10845-012-0682-1
[7] PERNA Alessia Serena, CARRINO Luigi, CITARELLA Alessia Auriemma, De MARCO Fabiola, Di BIASI Luigi, TORTORA Genoveffa, VISCUSI Antonio, A machine learning approach for adhesion forecasting of cold-sprayed coatings on polymer-based substrates, Materials Research Proceedings, Vol. 28, pp 57-64, 2023. https://doi.org/10.21741/9781644902479-7
[8] Raphaela Rauter, “Laserauftragschweißen von Wolframkarbidschichten in Nickelbasismatrizen zur Herstellung verschleißfester Beschichtungen von Warmformwerkzeugen,” Technischen Universität Graz, 2016.
[9] J. Witzel, “Qualifizierung des Laserstrahl-Auftragschweißens zur generativen Fertigung von Luftfahrtkomponenten,” Von der Fakultät für Maschinenwesen der Rheinisch-Westfälischen Technischen Hochschule Aachen zur Erlangung des akademischen Grades eines Doktors der Ingenieurwissenschaften genehmigte Dissertation, Dec. 2014.