Surrogate modeling for multi-objective optimization in the high-precision production of LiDAR glass optics

Surrogate modeling for multi-objective optimization in the high-precision production of LiDAR glass optics

VU Anh Tuan, PARIA Hamidreza, GRUNWALD Tim, BERGS Thomas

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Abstract. This study addresses the ever-increasing demands on glass optics for LiDAR systems in autonomous vehicles, highlighting the pivotal role of the recently developed Nonisothermal Glass Molding (NGM) in enabling the mass production of complex and precise glass optics. While NGM promises a significant advancement in cost- and energy-efficient solutions, achieving the requisite shape and form accuracy for high-precision optics remains a persistent challenge. The research focuses on expediting the development phase, presenting a methodology that strategically utilizes a sparse dataset for determining optimized molding parameters with a minimized number of experimental trials. Importantly, our method highlights the exceptional ability of a robust surrogate model to precisely predict the accuracy outputs of glass optics, strongly influenced by numerous input molding parameters of the NGM process. This significance emphasizes the surrogate model, which emerges as a promising alternative to inefficient traditional methods, such as time-consuming experiments or computation-intensive simulations, particularly in the realm of high-precision production for LiDAR glass optics. In contributing to optics manufacturing advancements, this study also aligns with contemporary trends in digitalization and Industry 4.0 within modern optics production, thereby fostering innovation in the automotive industry.

Keywords
Nonisothermal Glass Molding, Glass Optics, LiDAR, Surrogate Modeling, Bayesian Optimization, Industry 4.0

Published online 4/24/2024, 10 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: VU Anh Tuan, PARIA Hamidreza, GRUNWALD Tim, BERGS Thomas, Surrogate modeling for multi-objective optimization in the high-precision production of LiDAR glass optics, Materials Research Proceedings, Vol. 41, pp 1779-1788, 2024

DOI: https://doi.org/10.21741/9781644903131-197

The article was published as article 197 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] H. Kreilkamp, A.T. Vu, O. Dambon, N.F. Klocke, Non‐Isothermal Glass Moulding of Complex Led Optics, in: S.K. Sundaram (Ed.), 77th Conference on Glass Problems, Wiley (2017) 141–149. https://doi.org/10.1002/9781119417507.ch13
[2] H. Kreilkamp, A.T. Vu, O. Dambon, F. Klocke, Replicative manufacturing of complex lighting optics by non-isothermal glass molding, in: D.H. Krevor, W.S. Beich, M.P. Schaub, A. Symmons (Eds.), Polymer Optics and Molded Glass Optics: Design, Fabrication, and Materials SPIE (2016) 99490B. https://doi.org/10.1117/12.2235848
[3] C. Strobl, P.A. Vogel, A.T. Vu, H. Mende, T. Grunwald, R.H. Schmitt, T. Bergs, Enabling Sustainability in Glass Optics Manufacturing by Wafer Scale Molding, KEM 926 (2022) 2371. https://doi.org/10.4028/p-hachrx
[4] A.T. Vu, H. Kreilkamp, O. Dambon, F. Klocke, Nonisothermal glass molding for the cost-efficient production of precision freeform optics, Opt. Eng 55 (2016) 71207. https://doi.org/10.1117/1.OE.55.7.071207
[5] A.T. Vu, H. Kreilkamp, B.J. Krishnamoorthi, O. Dambon, F. Klocke, A hybrid optimization approach in non-isothermal glass molding, in: Author(s) (2016) 40006.
[6] A.T. Vu, A.N. Vu, T. Grunwald, T. Bergs, Modeling of thermo‐viscoelastic material behavior of glass over a wide temperature range in glass compression molding, J Am Ceram Soc 103 (2020) 2791–2807. https://doi.org/10.1111/jace.16963
[7] G. Liu, A.T. Vu, O. Dambon, F. Klocke, Glass Material Modeling and its Molding Behavior, MRS Advances 2 (2017) 875–885. https://doi.org/10.1557/adv.2017.64
[8] F. Wang, Simulating the precision glass molding process. Zugl.: Aachen, Techn. Hochsch., Diss., 2013, 1st ed., Apprimus-Verl., Aachen, 2014.
[9] A.T. Vu, T. Grunwald, T. Bergs, Thermo-viscoelastic Modeling of Nonequilibrium Material Behavior of Glass in Nonisothermal Glass Molding, Procedia Manufacturing 47 (2020) 1561. https://doi.org/10.1016/j.promfg.2020.04.350
[10] T.D. Pallicity, A.T. Vu, K. Ramesh, P. Mahajan, G. Liu, O. Dambon, Birefringence measurement for validation of simulation of precision glass molding process, J Am Ceram Soc 100 (2017) 4680–4698. https://doi.org/10.1111/jace.15010
[11] F. Wang, Y. Chen, F. Klocke, G. Pongs, A.Y. Yi, Numerical Simulation Assisted Curve Compensation in Compression Molding of High Precision Aspherical Glass Lenses, Journal of Manufacturing Science and Engineering 131 (2009). https://doi.org/10.1115/1.3063652
[12] A.T. Vu, Modeling Relaxation Nature of Nonequilibrium Glass in Nonisothermal Glass Molding. Dissertation, 2023.
[13] A.T. Vu, A.N. Vu, G. Liu, T. Grunwald, O. Dambon, F. Klocke, T. Bergs, Experimental investigation of contact heat transfer coefficients in nonisothermal glass molding by infrared thermography, J Am Ceram Soc 102 (2019) 2116–2134. https://doi.org/10.1111/jace.16029
[14] A.T. Vu, T. Helmig, A.N. Vu, Y. Frekers, T. Grunwald, R. Kneer, T. Bergs, Numerical and experimental determinations of contact heat transfer coefficients in nonisothermal glass molding, J Am Ceram Soc 103 (2020) 1258–1269. https://doi.org/10.1111/jace.16756
[15] A.T. Vu, R.d.l.A. Avila Hernandez, T. Grunwald, T. Bergs, Modeling nonequilibrium thermoviscoelastic material behaviors of glass in nonisothermal glass molding, J Am Ceram Soc 105 (2022) 6799–6815. https://doi.org/10.1111/jace.18605
[16] A.T. Vu, T. Grunwald, T. Bergs, Friction in Glass Forming: Tribological Behaviors of Optical Glasses and uncoated Steel near Glass Transition Temperature, J Non-Cryst Solids (2024).
[17] B. Shahriari, K. Swersky, Z. Wang, R.P. Adams, N. de Freitas, Taking the Human Out of the Loop: A Review of Bayesian Optimization, Proc. IEEE 104 (2016) 148–175. https://doi.org/10.1109/JPROC.2015.2494218
[18] S. Greenhill, S. Rana, S. Gupta, P. Vellanki, S. Venkatesh, Bayesian Optimization for Adaptive Experimental Design: A Review, IEEE Access 8 (2020) 13937–13948. https://doi.org/10.1109/ACCESS.2020.2966228
[19] S. Ranftl, W. von der Linden, Bayesian Surrogate Analysis and Uncertainty Propagation, in: The 40th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MDPI, Basel Switzerland (2021) 6. https://doi.org/10.3390/psf2021003006
[20] F. Mekki-Berrada, Z. Ren, T. Huang, W.K. Wong, F. Zheng, J. Xie, I.P.S. Tian et al., Two-step machine learning enables optimized nanoparticle synthesis, npj Comput Mater 7 (2021). https://doi.org/10.1038/s41524-021-00520-w
[21] Y. Tian, M.K. Luković, T. Erps, M. Foshey, W. Matusik, AutoOED: Automated Optimal Experiment Design Platform, arXiv, 2021.
[22] M.K. Luković, Y. Tian, W. Matusik, Diversity-Guided Multi-Objective Bayesian Optimization with Batch Evaluations, in: Proceedings of the 34th International Conference on Neural Information Processing Systems, Curran Associates Inc, Red Hook, NY, USA, 2020.
[23] P. Saves et al., SMT 2.0: A Surrogate Modeling Toolbox with a focus on hierarchical and mixed variables Gaussian processes, Advances in Engineering Software 188 (2024) 103571. https://doi.org/10.1016/j.advengsoft.2023.103571