Univ. Prof. Dr. Ing.

Bergs, Thomas

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  • Publication
    Precision Glass Molding of Fused Silica Optics
    ( 2024-05-28)
    Karimova, Albina
    ;
    Vu, Anh Tuan orcid-logo
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    Grunwald, Tim
    ;
    Bergs, Thomas
    Fused silica glass products have exceptional properties that make them ideal for optical components in cutting-edge technologies. The traditional manufacturing process has limitations in scalability and cost. Glass molding offers a sustainable solution for series production of optical components. However, the transferability of glass molding to mass production is challenging due to high forming temperatures. This research focuses on enabling a high temperature molding process for fused silica optics through material screening, numerical simulation, and real experiments. The findings contribute to the development of a high temperature molding process for mass production.
  • Publication
    Surrogate Modeling for Multi-Objective Optimization in the High-Precision Production of LiDAR Glass Optics
    ( 2024-04-26)
    Vu, Anh Tuan orcid-logo
    ;
    Paria, Hamidreza
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    Grunwald, Tim
    ;
    Bergs, Thomas
    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.
  • Publication
    Surrogate modeling for multi-objective optimization in the high-precision production of LiDAR glass optics
    ( 2024-04-24)
    Vu, Anh Tuan orcid-logo
    ;
    Paria, Hamidreza
    ;
    Grunwald, Tim
    ;
    Bergs, Thomas
    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.
  • Publication
    Enabling Sustainability in Glass Optics Manufacturing by Wafer Scale Molding
    ( 2022-07-22)
    Strobl, Christian
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    Vogel, Paul-Alexander
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    Vu, Anh Tuan orcid-logo
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    Mende, Hendrik
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    Grunwald, Tim
    ;
    Schmitt, Robert H.
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    Bergs, Thomas
    Numerous optical applications have rising demands for ever increasing quantities from lighting and projection optics for modern vehicles to home or street lighting using LED technology. Glass is the material of choice for most of those application fields. It has several advantages over polymers, including heat and scratch resistance as well as longevity and recyclability. Non-isothermal glass molding has become a viable hot forming technology for mass production of optics. The major challenge is enabling a scalable replication process allowing the optical glass elements to be manufactured with high form accuracy and at low-cost production with low reject rates. This work introduces recent developments in glass optics manufacturing that allow the fulfilment of seemingly contradicting criteria: the economic growth and the need for less consumption of resources and energy. While single cavity non-isothermal molding is state-of-the-art, a manufacturing innovation through wafer-scale molding enables an exponentially increasing number of optics to be produced per production shift, allowing a significant reduction of unit costs. In parallel, as multiple optics are produced in one manufacturing cycle, the energy consumption and the consequent CO2 emission can be reduced. In contrast, the technological development arises several challenges that will be discussed in this work. Besides the selection of suitable mold concepts and materials, the challenges also include the temperature control of the mold and the blank up to the optimization of flow and shrinking mechanisms of the glass during rapid forming. Another difficulty in the non-isothermal glass molding is to maintain the low form deviation required for precision optics, repeatability, and low failure rates through process optimization. Finally, detail calculations of cost, energy and CO2 consumption, in comparison with conventional fabrication of glass components using grinding and polishing as well as single cavity molding, will be demonstrated. The non-isothermal wafer-level glass molding is a new technological solution for the sustainable manufacturing of optics at large-scaled production.
  • Publication
    Modeling nonequilibrium thermoviscoelastic material behaviors of glass in nonisothermal glass molding
    ( 2022)
    Vu, Anh Tuan orcid-logo
    ;
    Avila Hernandez, Rocio
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    Grunwald, Tim
    ;
    Bergs, Thomas
    Nonisothermal glass molding (NGM) is developed aiming to satisfy the fabrication of complex and precision optical elements at high production volumes. The technology, however, was encountered by the high form deviation of manufactured glass optics. During the nonisothermal compression molding, glass undergoes a rapid temperature change making it a transition from equilibrium to nonequilibrium nature of matter. Owing to this characteristic, viscoelastic material behaviors of glass depend on not only temperature but also thermal history. Understanding the nature of nonequilibrium thermoviscoelasticity, the central importance to control the final shape and optical characteristics of the manufactured glass optics is the focus of this research. First, this study presents preliminary experimental investigations indicating the difference in viscoelastic deformation responses under the equilibrated and nonequilibrated glass specimens. In the following, modeling the temperature-dependent viscoelasticity over a wide temperature range in glass transition region is described, where temperature is directly coupled in each parameter of a rheological constitutive model. This concept allows us to bypass the thermorheologically simple assumption used to tolerate the description of thermoviscoelastic responses in most of the earlier works. Finally, we propose a modeling method that incorporates temperature and thermal history into the rheological model parameters, each of which is described by employing the Mauro-Allan-Potuzak viscosity equation. Viscoelastic experiments were carried out to validate the model. The results show that the time-dependent responses varying with temperature are well predicted in both equilibrium and nonequilibrium regimes of glass-forming systems. The benefit of this research is that a unique material model is entirely applicable for numerical studies of various hot forming processes such as annealing when glass undergoes pure thermal loads, as well as precision glass molding and NGM when glass is deformed by fully coupled thermal–mechanical loads under either its equilibrium or nonequilibrium state of materials, respectively.
  • Publication
    Machine learning-based predictive modeling of contact heat transfer
    ( 2021)
    Vu, Anh Tuan orcid-logo
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    Gulati, Shrey
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    Vogel, Paul-Alexander
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    Grunwald, Tim
    ;
    Bergs, Thomas
    Heat transfer phenomena at the interface between two contacting solids are highly complex involving multiple influencing factors. Over the years, a large amount of experiments were carried out to determine the contact heat transfer coefficients between two dissimilar joint materials. However, there are still no existing theoretical or physics-based models that satisfactorily predict the contact heat transfer coefficients. By taking advantage of the existing data, in contrast, machine learning promises a powerful method, capable of predicting the contact heat transfer coefficients for different material pairs and contact conditions. This research introduces a robust machine learning-based model that succeeds in precisely estimating the heat transfer across the interfaces between glass and steel, a material pair widely used in hot forming of glass. The data used for training and validating the machine learning models were determined experimentally by means of infrared thermography. The datasets consisted of contact heat transfer coefficients with dependence on three factors - interfacial temperature, contact pressure, and surface finishes. Aim of this study is to analyze the prediction accuracy and interpretability of various supervised learning algorithms in order to realize the machine learning models that are able to capture the underlying physics governing the heat transfer phenomena at the glass-mold interface. Finally, the results were compared with those estimated by a theoretical model and a numerical simulation model. The comparison demonstrates enhancements in prediction accuracy enabled by the data-driven method. This study indicates accurate and efficient strategies for solving thermal problems in hot glass forming processes.
  • Publication
    Modeling of thermo-viscoelastic material behavior of glass over a wide temperature range in glass compression molding
    ( 2020)
    Vu, Anh Tuan orcid-logo
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    Vu, Anh Ngoc
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    Grunwald, Tim
    ;
    Bergs, Thomas
    In glass compression molding, most current modeling approaches of temperature-dependent viscoelastic behavior of glass materials are restricted to thermo-rheologically simple assumption. This research conducts a detailed study and demonstrates that this assumption, however, is not adequate for glass molding simulations over a wide range of molding temperatures. In this paper, we introduce a new method that eliminates the prerequisite of relaxation functions and shift factors for modeling of the thermo-viscoelastic material behavior. More specifically, the temperature effect is directly incorporated into each parameter of the mechanical model. The mechanical model parameters are derived from creep displacements using uniaxial compression experiments. Validations of the proposed method are conducted for three different glass categories, including borosilicate, aluminosilicate, and chalcogenide glasses. Excellent agreement between the creep experiments and simulation results is found in all glasses over long pressing time up to 900 seconds and a large temperature range that corresponds to the glass viscosity of log (η) = 9.5 â 6.8 Pas. The method eventually promises an enhancement of the glass molding simulation.