Approaches and methodologies for process development of thin glass forming

Vogel, Paul-Alexander; Vu, Anh-Tuan; Mende, Hendrik; Grunwald, Tim; Bergs, Thomas; Schmitt, Robert

doi:10.1117/12.2536431

Cited by 4 publications

(5 citation statements)

References 14 publications

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“…Another challenge is the control of shrinkage, which is also significantly influenced by thermal and system parameters [23]. The consequence of uncontrolled or excessive shrinkage can be glass breakage [24,25]. In addition, in the wafer-scale process exists an increased risk of adhesion when the hot glass blank first comes into contact with the mold due to the more complex multi-cavity geometry.…”

Section: Fig 4 Process Chain Of the Wafer Scale Processmentioning

confidence: 99%

Enabling Sustainability in Glass Optics Manufacturing by Wafer Scale Molding

Strobl

Vogel

et al. 2022

KEM

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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.

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Section: Fig 4 Process Chain Of the Wafer Scale Processmentioning

confidence: 99%

Enabling Sustainability in Glass Optics Manufacturing by Wafer Scale Molding

Strobl

Vogel

et al. 2022

KEM

Self Cite

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“…Precision-formed glass is important for a wide range of applications, such as optics for lighting and photonic applications, as well as haptic elements, serving as a durable and resistant alternative to polymers. 1 The manufacturing of optical components from glass can generally be achieved through two primary processes: direct processes and replicative processes. 2 Direct processes involve the utilization of grinding and polishing techniques to attain the desired shape of the glass, making them particularly suitable for planar or spherical products.…”

Section: Introductionmentioning

confidence: 99%

“…To tackle this problem, prior research has explored various methods for process development, including experimental, simulation, and machine learning approaches. 1 Machine learning (ML) has emerged as a powerful tool for predicting product quality in manufacturing processes, thanks to advancements in data collection, computing power, and algorithm development. 6,7 In the context of NGM vacuum-assisted slumping, some notable work has been done.…”

Section: Introductionmentioning

confidence: 99%

Multi-target regression and cross-validation for non-isothermal glass molding experiments with small sample sizes

Mende,

Kiroriwal,

Pfrommer

et al. 2023

Optifab 2023

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“…Instead, a replicative manufacturing, namely Nonisothermal Glass Molding (NGM), has been developed and becomes a viable technology for the cost-efficient production of glass optics [4,5]. In more recent years, the nonisothermal molding principle has been extended for thin glass forming by applying suitable thermal-mechanical loading means such as thermal slumping, vacuum pressure, press bending, and deep drawing [6]. The implementation of various loading enablers allows this newly developed glass forming process for the production of complex, precision, [7][8][9] and microstructureintegrated optical components [10][11][12][13].…”

Section: Introductionmentioning

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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Approaches and methodologies for process development of thin glass forming

Cited by 4 publications

References 14 publications

Enabling Sustainability in Glass Optics Manufacturing by Wafer Scale Molding

Enabling Sustainability in Glass Optics Manufacturing by Wafer Scale Molding

Multi-target regression and cross-validation for non-isothermal glass molding experiments with small sample sizes

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

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