A machine learning approach for adhesion forecasting of cold-sprayed coatings on polymer-based substrates

A machine learning approach for adhesion forecasting of cold-sprayed coatings on polymer-based substrates

PERNA Alessia Serena, CARRINO Luigi, CITARELLA Alessia Auriemma, De MARCO Fabiola, Di BIASI Luigi, TORTORA Genoveffa, VISCUSI Antonio

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Abstract. Cold spray is a novel production technology for creating metallic layers on various materials. Using a pressurized gas travelling at supersonic speeds, the metallic particles are accelerated and impact the target surface obtaining adhesion through mechanical interlocking between the powders and the substrate. This method is especially well suited for coating thermosensitive materials like composites since it only requires a little amount of heat, as the powders remain in a solid state. The quality and comprehension of this manufacturing process can be greatly improved by using machine learning techniques. In order to evaluate the characteristics of the particle’s deformation upon collision, the goal of this work is to forecast it using machine learning approaches. The parameters chosen as an input for the model were related to 3 macro-categories: process parameters, powder parameters and substrate parameters. As regards the output parameters, flattening and penetration were chosen as they are the main characteristics of the coating on which homogeneity and adhesion depend. In order to obtain reliable results, a mix of data FEM and experimental data were used to train the neural network. The model was then tested on a dataset of experimental data.

Machine Learning, Cold Spray, Composites, Neural Networks, Thermal Spray

Published online 4/19/2023, 8 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: 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

DOI: https://doi.org/10.21741/9781644902479-7

The article was published as article 7 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.

[1] V.K. Champagne, The Cold Spray Materials Deposition Process. Fundamentals and Applications, Woodhead Publishing Series in Metals and Surface Engineering, 2007. https://doi.org/10.1533/9781845693787.1
[2] R.C.C. Dykhuizen, M.F.F. Smith, Gas Dynamic Principles of Cold Spray, J. Therm. Spray Techn. 7 (1998) 205-212. https://doi.org/10.1361/105996398770350945
[3] R. della Gatta, A.S. Perna, A. Viscusi, G. Pasquino, A. Astarita, Cold spray deposition of metallic coatings on polymers: a review, J. Mater. Sci. (2021). https://doi.org/10.1007/s10853-021-06561-2
[4] K.-H. Leitz, M. O’sullivan, A. Plankensteiner, T. Lichtenegger, S. Pirker, H. Kestler,
L.S. Sigl, CFDEM modelling of particle heating and acceleration in cold spraying Cold Spraying-Technology Principle and Challenges, n.d. Available online: https://www-plansee-com.azureedge.net/fileadmin/user_upload/Plansee_Seminar/PDFs/CFDEM_modelling_of_particle_heating_and_acceleration_in_cold_spraying.pdf. (accessed 31 January 2023). doi:10.1016/j.ijrmhm.2018.02.003
[5] F. Rubino, A. Astarita, P. Carlone, Thermo-mechanical finite element modeling of the laser treatment of titanium cold-sprayed coatings, Coatings 8 (2018) 219. https://doi.org/10.3390/coatings8060219
[6] W.Y. Li, W. Gao, Some aspects on 3D numerical modeling of high velocity impact of particles in cold spraying by explicit finite element analysis, Appl. Surf. Sci. 255 (2009) 7878-7892. https://doi.org/10.1016/j.apsusc.2009.04.135
[7] R. della Gatta, A. Viscusi, A.S. Perna, A. Caraviello, A. Astarita, Feasibility of steel powder deposition on composites through cold spray, Mater. Manuf. Process. 36 (2021). https://doi.org/10.1080/10426914.2020.1832693
[8] A.S. Perna, A. Viscusi, A. Astarita, L. Boccarusso, L. Carrino, M. Durante, R. Sansone, Manufacturing of a Metal Matrix Composite Coating on a Polymer Matrix Composite Through Cold Gas Dynamic Spray Technique, J. Mater. Eng. Perform. 28 (2019) 3211-3219. https://doi.org/10.1007/s11665-019-03914-6
[9] A.S. Perna, A. Viscusi, A. Astarita, L. Boccarusso, A. Caraviello, L. Carrino, M. Durante, R. Sansone, Experimental study of functionalized polymer matrix composite with multi-material metal coatings produced by means of cold spray technology, Key Eng. Mater. 816 (2019) 267-272. https://doi.org/10.4028/www.scientific.net/KEM.813.267
[10] S. Ray, A Quick Review of Machine Learning Algorithms, 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon) (2019) 35-39.
[11] C.E. Rasmussen, Gaussian Processes in machine learning, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3176 (2004) 63-71. https://doi.org/10.1007/978-3-540-28650-9_4/COVER
[12] J. Nevitt, G.R. Hancock, Improving the Root Mean Square Error of Approximation for Nonnormal Conditions in Structural Equation Modeling, J. Exp. Educ. 68 (2010) 251-268. https://doi.org/10.1080/00220970009600095
[13] G.I. Webb, C. Sammut, C. Perlich, T. Horváth, S. Wrobel, K.B. Korb, W.S. Noble, C. Leslie, M.G. Lagoudakis, N. Quadrianto, W.L. Buntine, N. Quadrianto, W.L. Buntine, L. Getoor, G. Namata, L. Getoor, X.J.J. Han, J.-A. Ting, S. Vijayakumar, S. Schaal, L. de Raedt, Encyclopedia of Machine Learning, Encyclopedia of Machine Learning. (2010) 613-624. https://doi.org/10.1007/978-0-387-30164-8_488
[14] J. Fürnkranz, P.K. Chan, S. Craw, C. Sammut, W. Uther, A. Ratnaparkhi, X. Jin, J. Han, Y. Yang, K. Morik, M. Dorigo, M. Birattari, T. Stützle, P. Brazdil, R. Vilalta, C. Giraud-Carrier, C. Soares, J. Rissanen, R.A. Baxter, I. Bruha, R.A. Baxter, G.I. Webb, L. Torgo, A. Banerjee, H. Shan, S. Ray, P. Tadepalli, Y. Shoham, R. Powers, Y. Shoham, R. Powers, G.I. Webb, S. Ray, S. Scott, H. Blockeel, L. De Raedt, Mean Absolute Error, Encyclopedia of Machine Learning. (2011) 652-652. https://doi.org/10.1007/978-0-387-30164-8_525