Modeling of thermo‐viscoelastic material behavior of glass over a wide temperature range in glass compression molding

Vu, Anh Tuan; Grunwald, Tim; Bergs, Thomas

doi:10.1111/jace.16963

Cited by 25 publications

(22 citation statements)

References 41 publications

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“…Finally, Figure 4C plots the creep compliances that are derived from the creep deformations. The computational procedure of the creep compliances can be referred to the earlier work of the authors 18 . The creep compliances are used to identify the parameters of the viscoelastic constitutive model, elaborated in the following section.…”

Section: Experimental Methodsmentioning

confidence: 99%

“…However, as the shift function is used, this modeling approach has to strictly rely on the assumption called thermo‐rheological simplicity (TRS) 17 . The drawback of the TRS assumption is that this concept can only be applicable in a narrow temperature range, typically in the vicinity of

{T}_g

, 29 but is not truly valid for the entire temperature range of glass transition, 30 which was particularly elaborated in the recent investigation of Vu et al 18 . As a result, this fact limits the validity of the TRS‐based modeling approach when glass undergoes a huge temperature change during the hot forming process.…”

Section: Introductionmentioning

confidence: 99%

“…In the previous work, we have introduced a modeling method where the physical temperature T can be directly incorporated into each parameter of the constitutive viscoelastic model 18 . This modeling allows us to bypass the use of TRS and, thus, to enhance the accuracy in computing the time‐dependent responses of both creep and stress relaxation varying with temperature.…”

Section: Introductionmentioning

confidence: 99%

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Modeling nonequilibrium thermoviscoelastic material behaviors of glass in nonisothermal glass molding

Hernandez

Grunwald

et al. 2022

J Am Ceram Soc.

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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 glassforming 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-mechanicalThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Experimental Methodsmentioning

confidence: 99%

{T}_g

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling nonequilibrium thermoviscoelastic material behaviors of glass in nonisothermal glass molding

Hernandez

Grunwald

et al. 2022

J Am Ceram Soc.

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“…It is, however, emphasized that imperfections of the molded components such as form deviation, chill ripples, or glass cracks are primarily driven by the molding step [17][18][19][20]. Those are the common defects observed in the nonisothermal molding process, mainly resulted from the high heat exchanges at the glass-mold interface [21,22] as well as the complex thermo-viscoelastic material behaviors of glass at elevated molding temperatures [23][24][25]. In serial production, if the defects are not detected during the molding step, it commonly results in a large number of glass rejects and high energy cost vain to anneal the glass failures [26].…”

Section: Fig 1 Process Chain Of Thin Glass Formingmentioning

confidence: 99%

Real-Time Quality Control in Thin Glass Forming Using Infrared Thermography and Deep Learning

Vogel

Subramanian

et al. 2022

KEM

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Towards the growing trends in lightweight, flexible, and optical advantages, thin glasses become key components in numerous applications such as consumer electronics like foldable smartphones, or automotive interiors. Nonisothermal glass molding promises a viable technology for the cost-efficient production of precision glass components. In the existing production, the quality of the glass products can only be accessed at the end of the hot forming process. Due to high rates of product failures often appeared in the precision glass molding processes, the current quality control of the produced optical products suffers low process efficiency. This work introduces an enabling approach for monitoring the product quality in real-time using thermography and machine learning. Specifically, the acquisition of the temperature fields of the glass components during the hot forming stage enabled by an infrared thermographic camera allows machine learning to predict the final shape of the molded components at the end of the forming process. Several transfer learning models have been investigated to demonstrate the proposed method. To further enhance the prediction performance, self-built convolutional neural network models were developed using different types of image data. By incorporating the time-series image data as an input to the learning models, the prediction performance was achieved. The model built in the present work demonstrates an excellent prediction accuracy where the difference between the measured and predicted shapes of the glass products can be kept at low double-digit micrometers. Such accuracy achieved by our self-developed machine learning model promisingly satisfy the quality control in serial productions of numerous precision optical glass components in automotive and consumer electronics sectors.

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“…Stress evolution in the cooling process was also simulated through finite-element simulation [34]. However, most of them are about polymer-microforming simulation, and there are few studies on the analysis of flow behavior, deformation, and strain rate during the hot embossing of glass microlenses [35].…”

Section: Introductionmentioning

confidence: 99%

Theoretical and Experimental Study on Hot-Embossing of Glass-Microprism Array without Online Cooling Process

Xie

et al. 2020

Micromachines

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Optical glass-microprism arrays are generally embossed at high temperatures, so an online cooling process is needed to remove thermal stress, but this make the cycle long and its equipment expensive. Therefore, the hot-embossing of a glass-microprism array at a low strain rate with reasonable embossing parameters was studied, aiming at reducing thermal stress and realizing its rapid microforming without online cooling process. First, the flow-field, strain-rate, and deformation behavior of glass microforming were simulated. Then, the low-cost microforming control device was designed, and the silicon carbide (SiC) die-core microgroove array was microground by the grinding-wheel microtip. Lastly, the effect of the process parameters on forming rate was studied. Results showed that the appropriate embossing parameters led to a low strain rate; then, the trapezoidal glass-microprism array could be formed without an online cooling process. The standard deviation of the theoretical and experimental forming rates was only 7%, and forming rate increased with increasing embossing temperature, embossing force, and holding duration, but cracks and adhesion occurred at a high embossing temperature and embossing force. The highest experimental forming rate reached 66.56% with embossing temperature of 630 °C, embossing force of 0.335 N, and holding duration of 12 min.

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Modeling of thermo‐viscoelastic material behavior of glass over a wide temperature range in glass compression molding

Cited by 25 publications

References 41 publications

Modeling nonequilibrium thermoviscoelastic material behaviors of glass in nonisothermal glass molding

Modeling nonequilibrium thermoviscoelastic material behaviors of glass in nonisothermal glass molding

Real-Time Quality Control in Thin Glass Forming Using Infrared Thermography and Deep Learning

Theoretical and Experimental Study on Hot-Embossing of Glass-Microprism Array without Online Cooling Process

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