Physics-Informed Data-Driven Models for Predicting Time- and Temperature-Dependent Viscoelastic Material Behaviors of Optical Glasses

Vu, Anh Tuan; Gulati, Shrey; Vogel, Paul-Alexander; Grunwald, Tim; Bergs, Thomas

doi:10.2139/ssrn.3822865

Cited by 2 publications

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“…It has been shown that ML for optimizing processes with the help of predictive quality models achieves good results for a wide range of production processes [9,10]. However, apart from [3,11,12], no ML models for predicting quality have been presented in the literature for the NGM process. We demonstrate the prediction quality of ML-Models capable of predicting form shapes of thin glass produced by vacuum-assisted glass molding by using different types of input data.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based predictions of form accuracy for curved thin glass by vacuum assisted hot forming process

Vogel¹,

Vu²,

Mende³

et al. 2023

Proceedings of the 18th IMEKO TC10 Conference on Measurement for Diagnostics, Optimisation and Control

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Thin glass is applied in numerous applications, appearing as three-dimensional smartphone covers, displays, and in thin batteries. Nonisothermal glass molding has been developed as a hot forming technology that enables to fulfil demands of high quality yet low-cost production. However, finding optimal parameters to a new product variant or glass material is highly demanding. Accordingly, manufacturers are striving for efficient and agile solutions that enable quick adaptations to the process. In this work, we demonstrate that machine learning (ML) can be utilized as a robust and reliable approach. ML-models capable of predicting form shapes of thin glass produced by vacuum-assisted glass molding were developed. Three types of input data were considered: set parameters, sensor values as time series, and thermographic in-process images of products. Different ML-algorithms were implemented, evaluated, and compared to reveal random forest and gradient boosting regressors as best performing on the first frame of the thermographic images.

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Section: Introductionmentioning

confidence: 99%

Machine learning-based predictions of form accuracy for curved thin glass by vacuum assisted hot forming process

Vogel¹,

Vu²,

Mende³

et al. 2023

Proceedings of the 18th IMEKO TC10 Conference on Measurement for Diagnostics, Optimisation and Control

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show abstract

“…All the models are trained with the Adam optimization method [34] with a learning rate of 0.001 and using mean squared error as the loss function [35,36]. Overfitting of the models was prevented by the early stopping method [37].…”

mentioning

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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Physics-Informed Data-Driven Models for Predicting Time- and Temperature-Dependent Viscoelastic Material Behaviors of Optical Glasses

Cited by 2 publications

References 0 publications

Machine learning-based predictions of form accuracy for curved thin glass by vacuum assisted hot forming process

Machine learning-based predictions of form accuracy for curved thin glass by vacuum assisted hot forming process

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

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