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

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.