Abstract
The process of surface coating is widely applied in the manufacturing industry. The accuracy of coating strongly affects the mechanical properties of the coated components. This work suggests the use of Self-Organizing Maps (Kohonen neural networks) for an optimal robotic beam trajectory planning for surface coating applications. The trajectory is defined by the one-dimensional sequence of neurons around a triangulated substrate and the neuron weights are defined as the position, beam vector and node velocity. During the training phase, random triangles are selected according to local curvature and the weights of the neurons whose beam coats the selected triangles are gradually adapted. This is achieved using a complicated coating thickness model as a function of stand-off distance, spray impact angle and beam surface spot speed. Initial results are presented from three objects widely used in manufacturing. The accuracy of this method is validated by comparing the simulated coating resulting from the SOM-planned trajectory to the coating performed for the same objects by an expert.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biol. Cybern. 43, 59–69 (1982)
Kubota, N., Nojima, Y., Kojima, F., Fukuda, T., Shibata, S.: Intelligent control of self-organizing manufacturing system with local learning mechanism. In: IECON Proceedings (Industrial Electronics Conference) (1999)
Razavian, A.A., Sun, J.: Cognitive based adaptive path-planning algorithm for autonomous robotic vehicles. In: Proceedings of the IEEE SoutheastCon, Ft. Lauderdale, FL, USA, 8–10 Apr 2005
Miljković, D.: Brief review of self-organizing maps. In: 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, pp. 1061–1066 (2017)
Maltarollo, V.G., Honório, K.M., da Silva, A.B.F.: Applications of artificial neural networks in chemical problems. In: Artificial Neural Networks-Architectures and Applications, pp. 203–223, InTech (2013)
Kohonen, T.: MATLAB Implementations and Applications of the Self-Organizing Map. Unigrafia, Helsinki, Finland (2014)
Pampalk, E., Dixon, S., Widmer, G.: Exploring music collections by browsing different views. Comput. Musical J. 28(2), 49–62 (2004)
Lobo, V.J.: Application of self-organizing maps to the maritime environment. In: Popovich, V.V., Claramunt, C., Schrenk, M., Korolenko, K.V. (eds.) Information Fusion and Geographic Information Systems, pp. 19–36. Springer, Berlin, Heidelberg (2009)
Richardson, A.J., Risien, C., Shillington, F.A.: Using self-organizing maps to identify patterns in satellite imagery. Prog. Oceanogr. 59(2–3), 223–239 (2003)
Tzinava, M., Delibasis, K., Allcock, B., Kamnis, S.: A general-purpose spray coating deposition software simulator. Surf. Coat. Technol. 399, (2020)
Katranidis, V., Gu, S., Allcock, B., Kamnis, S.: Experimental study of high velocity oxy-fuel sprayed WC-17Co coatings applied on complex geometries. Part A: influence of kinematic spray parameters on thickness, porosity, residual stresses and microhardness, Surf. Coat. Technol. 311, 206–215 (2017)
Acknowledgment
This work was partially financially supported by the Interdepartmental Postgraduate Programme “Informatics and Computational Biomedicine”, School of Science, University of Thessaly, Greece.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 IFIP International Federation for Information Processing
About this paper
Cite this paper
Tzinava, M., Delibasis, K., Kamnis, S. (2021). Self-organizing Maps for Optimized Robotic Trajectory Planning Applied to Surface Coating. In: Maglogiannis, I., Macintyre, J., Iliadis, L. (eds) Artificial Intelligence Applications and Innovations. AIAI 2021. IFIP Advances in Information and Communication Technology, vol 627. Springer, Cham. https://doi.org/10.1007/978-3-030-79150-6_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-79150-6_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-79149-0
Online ISBN: 978-3-030-79150-6
eBook Packages: Computer ScienceComputer Science (R0)
