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Design methodology for optimal sensor placement for cure monitoring and load detection of sensor-integrated, gentelligent composite parts

Published online by Cambridge University Press:  16 May 2024

Sören Meyer zu Westerhausen ,
Alexander Kyriazis ,
Christian Hühne  and
Roland Lachmayer
Sören Meyer zu Westerhausen*
Affiliation:
Leibniz University Hannover, Germany
Alexander Kyriazis
Affiliation:
Technische Universität Braunschweig, Germany
Christian Hühne
Affiliation:
Technische Universität Braunschweig, Germany
Roland Lachmayer
Affiliation:
Leibniz University Hannover, Germany
*
meyer-zu-westerhausen@ipeg.uni-hannover.de

Abstract

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Selecting right positions for composite-integrated sensors for monitoring cure during manufacturing and loads during product use presents challenges for engineering design. Since an optimal sensor placement (OSP) methodology for both phases is not emphasised enough in literature, a new methodology is proposed. This methodology is based on a Genetic Algorithm and strain gauges, temperature sensors and interdigitated electrode sensors for cure monitoring and physics-informed neural network-based load detection. Additionally, it includes sensor node positions optimization in a sensor network.

Keywords

design methodsdata-driven designoptimal sensor placementwireless sensor networksartificial intelligence (AI)

Information

Type
Design Methods and Tools
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 673 - 682
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

References

Altun, O., Zhang, D., Siqueira, R., Wolniak, P., Mozgova, I. and Lachmayer, R. (2020), “Identification of dynamic loads on structural component with artificial neural networks”, Procedia Manufacturing, Vol. 52, pp. 181–186. https://doi.org/10.1016/j.promfg.2020.11.032.CrossRefGoogle Scholar
Bergmayr, T., Höll, S., Kralovec, C. and Schagerl, M. (2023), “Local residual random forest classifier for strain-based damage detection and localization in aerospace sandwich structures”, Composite Structures, Vol. 304, p. 116331. https://doi.org/10.1016/j.compstruct.2022.116331.CrossRefGoogle Scholar
Bhondekar, A.P., Vig, R., Lal Singla, M., Ghanshyam, C. and Kapur, P. (2009), “Genetic Algorithm Based Node Placement Methodology For Wireless Sensor Networks”, Proceedings of the International MultiConference of Engineers and Computer Scientists 2009, IMECS 2009.Google Scholar
Bhuiyan, M.Z.A., Wang, G., Cao, J. and Wu, J. (2014), “Sensor Placement with Multiple Objectives for Structural Health Monitoring”, ACM Transactions on Sensor Networks, Vol. 10 No. 4, pp. 1–45. https://doi.org/10.1145/2533669.CrossRefGoogle Scholar
Damm, A.M., Spitzmüller, C., Raichle, A.T.S., Bühler, A., Weißgraeber, P. and Middendorf, P. (2020), “Deep learning for impact detection in composite plates with sparsely integrated sensors”, Smart Materials and Structures, Vol. 29 No. 12, p. 125014. https://doi.org/10.1088/1361-665X/abb644.CrossRefGoogle Scholar
Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T. (2002), “A fast and elitist multiobjective genetic algorithm: NSGA-II”, IEEE Transactions on Evolutionary Computation, Vol. 6 No. 2, pp. 182–197. https://doi.org/10.1109/4235.996017.CrossRefGoogle Scholar
Esposito, M. and Gherlone, M. (2020), “Composite wing box deformed-shape reconstruction based on measured strains: Optimization and comparison of existing approaches”, Aerospace Science and Technology, Vol. 99, p. 105758. https://doi.org/10.1016/j.ast.2020.105758.CrossRefGoogle Scholar
Esposito, M., Gherlone, M. and Marzocca, P. (2021), “External loads identification and shape sensing on an aluminum wing box: An integrated approach”, Aerospace Science and Technology, Vol. 114, p. 106743. https://doi.org/10.1016/j.ast.2021.106743.CrossRefGoogle Scholar
Ghasemzadeh, M. and Kefal, A. (2022), “Sensor Placement Optimization for Shape Sensing of Plates and Shells Using Genetic Algorithm and Inverse Finite Element Method”, Sensors (Basel, Switzerland), Vol. 22 No. 23. https://doi.org/10.3390/s22239252.CrossRefGoogle ScholarPubMed
Gherlone, M., Cerracchio, P. and Mattone, M. (2018), “Shape sensing methods: Review and experimental comparison on a wing-shaped plate”, Progress in Aerospace Sciences, Vol. 99, pp. 14–26. https://doi.org/10.1016/j.paerosci.2018.04.001.CrossRefGoogle Scholar
Hasselbruch, H., Hehl, A. von and Zoch, H.-W. (2015), “Properties and failure behavior of hybrid wire mesh/carbon fiber reinforced thermoplastic composites under quasi-static tensile load”, Materials & Design, Vol. 66, pp. 429–436. https://doi.org/10.1016/j.matdes.2014.07.032.CrossRefGoogle Scholar
Hoffer, J.G., Geiger, B.C., Ofner, P. and Kern, R. (2021), “Mesh-Free Surrogate Models for Structural Mechanic FEM Simulation: A Comparative Study of Approaches”, Applied Sciences, Vol. 11 No. 20, p. 9411. https://doi.org/10.3390/app11209411.CrossRefGoogle Scholar
Hudson, T.B., Auwaijan, N. and Yuan, F.-G. (2019), “Guided Wave-based System for Real-time Cure Monitoring of Composites using Piezoelectric Discs and Phase-shifted Fiber Bragg Gratings”, Journal of composite materials, Vol. 53 No. 7, pp. 969–979. https://doi.org/10.1177/0021998318793512.CrossRefGoogle ScholarPubMed
Klein, L. and Middendorf, P. (2016), “Functionalized lightweight parts: Application of LCM for the integration of automotive sensors in CFRP structures”, in 21–25 September 2015, Graz, Austria, Author(s), p. 90001.Google Scholar
Ko, W.L., Richards, W.L. and van Tran, T. (2007), “Displacement Theories for In-Flight Displacement Theories for In-Flight Deformed Shape Predictions of Aerospace Structures”, NASA/TP-2007-214612, October 2007.Google Scholar
Kyriazis, A., Pommer, C., Lohuis, D., Rager, K., Dietzel, A. and Sinapius, M. (2022), “Comparison of Different Cure Monitoring Techniques”, Sensors (Basel, Switzerland), Vol. 22 No. 19. https://doi.org/10.3390/s22197301.CrossRefGoogle ScholarPubMed
Lachmayer, R., Mozgova, I., Reimche, W., Colditz, F., Mroz, G. and Gottwald, P. (2014), “Technical Inheritance: A Concept to Adapt the Evolution of Nature to Product Engineering”, Procedia Technology, Vol. 15, pp. 178–187. https://doi.org/10.1016/j.protcy.2014.09.070.CrossRefGoogle Scholar
Lachmayer, R., Mozgova, I. and Scheidel, W. (2016), “An Approach to Describe Gentelligent Components in their Life Cycle”, Procedia Technology, Vol. 26, pp. 199–206. https://doi.org/10.1016/j.protcy.2016.08.027.CrossRefGoogle Scholar
Meyer zu Westerhausen, S., Schneider, J. and Lachmayer, R. (2023), “RELIABILITY ANALYSIS FOR SENSOR NETWORKS AND THEIR DATA ACQUISITION: A SYSTEMATIC LITERATURE REVIEW”, Proceedings of the Design Society, Vol. 3, pp. 3065–3074. https://doi.org/10.1017/pds.2023.307.CrossRefGoogle Scholar
Mirzaei, S., Krishnan, K., Al Kobtawy, C., Roberts, J. and Palmer, E. (2021), “Heat transfer simulation and improvement of autoclave loading in composites manufacturing”, The International Journal of Advanced Manufacturing Technology, Vol. 112 No. 11-12, pp. 2989–3000. https://doi.org/10.1007/s00170-020-06573-3.CrossRefGoogle Scholar
Ostachowicz, W., Soman, R. and Malinowski, P. (2019), “Optimization of sensor placement for structural health monitoring: a review”, Structural Health Monitoring, Vol. 18 No. 3, pp. 963–988. https://doi.org/10.1177/1475921719825601.CrossRefGoogle Scholar
Prussak, R., Stefaniak, D., Kappel, E., Hühne, C. and Sinapius, M. (2019), “Smart cure cycles for fiber metal laminates using embedded fiber Bragg grating sensors”, Composite Structures, Volume 213, pp. 252–260. https://doi.org/10.1016/j.compstruct.2019.01.079.CrossRefGoogle Scholar
Qiu, Y., Arunachala, P.K. and Linder, C. (2023), “SenseNet: A Physics-Informed Deep Learning Model for Shape Sensing”, Journal of Engineering Mechanics, Vol. 149 No. 3. https://doi.org/10.1061/JENMDT.EMENG-6901.CrossRefGoogle Scholar
Raissi, M., Perdikaris, P. and Karniadakis, G.E. (2019), “Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations”, Journal of Computational Physics, Volume 378, pp. 686–707. https://doi.org/10.1016/j.jcp.2018.10.045.CrossRefGoogle Scholar
Richards, W.L. and Ko, W.L. (2010), PROCESS FOR USING SURFACE STRAIN MEASUREMENTS TO OBTAIN OPERATIONAL LOADS FOR COMPLEX STRUCTURES No. US 7,715,994 B1.Google Scholar
Ruzek, R., Kadlec, M., Tserpes, K. and Karachalios, E. (2017), “Monitoring of compressive behaviour of stiffened composite panels using embedded fibre optic and strain gauge sensors”, International Journal of Structural Integrity, Vol. 8 No. 1, pp. 134–150. https://doi.org/10.1108/IJSI-11-2015-0052.CrossRefGoogle Scholar
Teimouri, H., Milani, A.S., Seethaler, R. and Heidarzadeh, A. (2016), “On the Impact of Manufacturing Uncertainty in Structural Health Monitoring of Composite Structures: A Signal to Noise Weighted Neural Network Process”, Open Journal of Composite Materials, Vol. 06 No. 01, pp. 28–39. https://doi.org/10.4236/ojcm.2016.61004.CrossRefGoogle Scholar
Tessler, A. and Spangler, J.L. (2005), “A least-squares variational method for full-field reconstruction of elastic deformations in shear-deformable plates and shells”, Computer Methods in Applied Mechanics and Engineering, Vol. 194 No. 2-5, pp. 327–339. https://doi.org/10.1016/j.cma.2004.03.015.CrossRefGoogle Scholar
Thiene, M., Khodaei, Z.S. and Aliabadi, M.H. (2016), “Optimal sensor placement for maximum area coverage (MAC) for damage localization in composite structures”, Smart Materials and Structures, Vol. 25 No. 9, p. 95037. https://doi.org/10.1088/0964-1726/25/9/095037.CrossRefGoogle Scholar
Thonemann, U. (2015), Operations Management: Konzepte, Methoden und Anwendungen, Pearson Studium - Economic BWL, 3rd ed., Pearson Studium.Google Scholar
Valoriani, F., Esposito, M. and Gherlone, M. (2022), “Shape Sensing for an UAV Composite Half-Wing: Numerical Comparison between Modal Method and Ko's Displacement Theory”, Aerospace, No. 9, pp. 1–18. https://doi.org/10.3390/aerospace9090509.CrossRefGoogle Scholar
Welzbacher, P., Geipl, A., Kraus, B., Puchtler, S. and Kirchner, E. (2023), “A FOLLOW-UP ON THE METHODICAL FRAMEWORK FOR THE IDENTIFICATION, ANALYSIS AND CONSIDERATION OF UNCERTAINTY IN THE CONTEXT OF THE INTEGRATION OF SENSORY FUNCTIONS BY MEANS OF SENSING MACHINE ELEMENTS”, Proceedings of the Design Society, Vol. 3, pp. 141–150. https://doi.org/10.1017/pds.2023.15.CrossRefGoogle Scholar
Wiedemann, J., Prussak, R., Kappel, E. and Hühne, C. (2022), “In-situ quantification of manufacturing-induced strains in fiber metal laminates with strain gages”, Composite Structures, Volume 297. https://doi.org/10.1016/j.compstruct.2022.115967.CrossRefGoogle Scholar
Xu, C., Cao, B.T. and Meschke, G. (2023), “Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios”, Computer Methods in Applied Mechanics and Engineering, Volume 405 No. 115852. https://doi.org/10.1016/j.cma.2022.115852.CrossRefGoogle Scholar
Ye, J., Do, N.C. and Lambert, M. (2022), “Physics-informed neural networks for hydraulic transient analysis in pipeline systems”, Water Research, Volume 221 No. 118828. https://doi.org/10.1016/j.watres.2022.118828.CrossRefGoogle ScholarPubMed
Zhu, Q., Zhao, Z. and Yan, J. (2023), “Physics-informed machine learning for surrogate modeling of wind pressure and optimization of pressure sensor placement”, Computational Mechanics, Vol. 71 No. 3, pp. 481–491. https://doi.org/10.1007/s00466-022-02251-1.CrossRefGoogle Scholar