Abstract
Finding the optimal process parameters for an adhesive bonding process is challenging: the optimization is inherently multi-objective (aiming to maximize break strength while minimizing cost), constrained (the process should not result in any visual damage to the materials, and stress tests should not result in adhesive failures), and uncertain (measuring the same process parameters several times lead to different break strength). Real-life physical experiments in the lab are expensive to perform (\(\sim \)6 h of experimentation and subsequent production costs); traditional evolutionary approaches are then ill-suited to solve the problem, due to the prohibitive amount of experiments required for evaluation. In this research, we successfully applied specific machine learning techniques (Gaussian Process Regression and Logistic Regression) to emulate the objective and constraint functions based on a limited amount of experimental data. The techniques are embedded in a Bayesian optimization algorithm, which succeeds in detecting Pareto-optimal process settings in a highly efficient way (i.e., requiring a limited number of experiments).
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
Notes
- 1.
- 2.
Other noise factors not controlled in the simulator are not further discussed.
References
Ankenman, B., Nelson, B.L., Staum, J.: Stochastic kriging for simulation metamodeling. Oper. Res. 58(2), 371–382 (2010)
Auger, A., Bader, J., Brockhoff, D., Zitzler, E.: Hypervolume-based multiobjective optimization: theoretical foundations and practical implications. Theoret. Comput. Sci. 425, 75–103 (2012). https://doi.org/10.1016/j.tcs.2011.03.012
Briffoteaux, G., Gobert, M., Ragonnet, R., Gmys, J., Mezmaz, M., Melab, N., Tuyttens, D.: Parallel surrogate-assisted optimization: batched Bayesian neural network-assisted GA versus q-EGO. Swarm Evol. Comput. 57, 100717 (2020)
Briffoteaux, G., Ragonnet, R., Tomenko, P., Mezmaz, M., Melab, N., Tuyttens, D.: Comparing parallel surrogate-based and surrogate-free multi-objective optimization of COVID-19 vaccines allocation. In: Dorronsoro, B., Pavone, M., Nakib, A., Talbi, E.G. (eds.) OLA 2022. CCIS, pp. 201–212. Springer, Cham (2022)
Brockmann, W., Geiß, P.L., Klingen, J., Schröder, K.B.: Adhesive Bonding: Materials Applications and Technology. Wiley, Hoboken (2008)
Brownlee, A.E., Wright, J.A.: Constrained, mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation. Appl. Soft Comput. 33, 114–126 (2015). https://doi.org/10.1016/j.asoc.2015.04.010
Budhe, S., Banea, M., De Barros, S., Da Silva, L.: An updated review of adhesively bonded joints in composite materials. Int. J. Adhes. Adhes. 72, 30–42 (2017)
Cavezza, F., Boehm, M., Terryn, H., Hauffman, T.: A review on adhesively bonded aluminium joints in the automotive industry. Metals 10(6), 730 (2020)
Correia, S., Anes, V., Reis, L.: Effect of surface treatment on adhesively bonded aluminium-aluminium joints regarding aeronautical structures. Eng. Fail. Anal. 84, 34–45 (2018)
Forrester, A., Sobester, A., Keane, A.: Engineering Design via Surrogate Modelling (A Practical Guide), 1st edn. John Wiley and Sons, West Sussex, UK (2008)
Ishibuchi, H., Masuda, H., Tanigaki, Y., Nojima, Y.: Modified distance calculation in generational distance and inverted generational distance. In: International Conference on Evolutionary Multi-criterion Optimization, pp. 110–125 (2015)
Jalali, H., Van Nieuwenhuyse, I., Picheny, V.: Comparison of kriging-based algorithms for simulation optimization with heterogeneous noise. Eur. J. Oper. Res. 261(1), 279–301 (2017)
Knowles, J.: ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Trans. Evol. Comput. 10(1), 50–66 (2006)
Loeppky, J., Sacks, J., Welch, W.: Choosing the sample size of a computer experiment: a practical guide. Technometrics 51(4), 366–376 (2009)
Qin, S., Sun, C., Jin, Y., Zhang, G.: Bayesian approaches to surrogate-assisted evolutionary multi-objective optimization: A comparative study. In: 2019 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 2074–2080 (2019)
Quan, N., Yin, J., Ng, S.H., Lee, L.H.: Simulation optimization via kriging: a sequential search using expected improvement with computing budget constraints. IIE Trans. 45(7), 763–780 (2013)
Rojas Gonzalez, S., Jalali, H., Van Nieuwenhuyse, I.: A multiobjective stochastic simulation optimization algorithm. Eur. J. Oper. Res. 284(1), 212–226 (2020). https://doi.org/10.1016/j.ejor.2019.12.014
da Silva, L., Ochsner, A., Adams, R., Spelt, J.: Handbook of Adhesion Technology. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-01169-6
Steel, R.G.D., Torrie, J.H., et al.: Principles and Procedures of Statistics, a Biometrical Approach. No. Ed. 2, McGraw-Hill Kogakusha, Ltd. (1980)
Tao, T., Zhao, G., Ren, S.: An efficient kriging-based constrained optimization algorithm by global and local sampling in feasible region. J. Mech. Des. 142(5), 051401 (2020)
Williams, C.K., Rasmussen, C.E.: Gaussian Processes for Machine Learning, vol. 2. MIT Press, Cambridge (2006)
Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann, Burlington (2011)
Yarat, S., Senan, S., Orman, Z.: A comparative study on PSO with other metaheuristic methods. In: Mercangöz, B.A. (ed.) Applying Particle Swarm Optimization. ISORMS, vol. 306, pp. 49–72. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-70281-6_4
Zhou, A., Qu, B.Y., Li, H., Zhao, S.Z., Suganthan, P.N., Zhang, Q.: Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evol. Comput. 1(1), 32–49 (2011)
Acknowledgements
This research was supported by the FLAIR Program and by the Research Foundation Flanders (FWO Grant 1216021N).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Morales-Hernández, A., Van Nieuwenhuyse, I., Rojas Gonzalez, S., Jordens, J., Witters, M., Van Doninck, B. (2023). Multi-objective Optimization of Adhesive Bonding Process in Constrained and Noisy Settings. In: Dorronsoro, B., Chicano, F., Danoy, G., Talbi, EG. (eds) Optimization and Learning. OLA 2023. Communications in Computer and Information Science, vol 1824. Springer, Cham. https://doi.org/10.1007/978-3-031-34020-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-34020-8_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-34019-2
Online ISBN: 978-3-031-34020-8
eBook Packages: Computer ScienceComputer Science (R0)