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

Hendrik Mende; Saksham Kiroriwal; Julius Pfrommer; Robert H. Schmitt; Jürgen Beyerer

doi:10.1117/12.2685230

29 November 2023 Multi-target regression and cross-validation for non-isothermal glass molding experiments with small sample sizes

Hendrik Mende, Saksham Kiroriwal, Julius Pfrommer, Robert H. Schmitt, Jürgen Beyerer

Author Affiliations +

Proceedings Volume 12778, Optifab 2023; 127780S (2023) https://doi.org/10.1117/12.2685230
Event: SPIE Optifab, 2023, Rochester, New York, United States

Abstract

Machine learning has become a core part of smart factories and Industry 4.0. In our work, we extend the use of machine learning for quality prediction of a thin glass product formed using a Non-isothermal Glass Moulding (NGM) process. As the form shape of a glass lens requires multiple variables to describe, Multi-Target Regression (MTR) is suitable for the same. Many MTR models are able to provide intuitive insights into the prediction target(s). We present a data pipeline that employs bootstrapping-inspired sampling for robust feature selection, modelling and validation for small dataset. The results demonstrate how MTR models can be used for prediction with dataset with high dimensional time series input and multiple targets.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hendrik Mende, Saksham Kiroriwal, Julius Pfrommer, Robert H. Schmitt, and Jürgen Beyerer "Multi-target regression and cross-validation for non-isothermal glass molding experiments with small sample sizes", Proc. SPIE 12778, Optifab 2023, 127780S (29 November 2023); https://doi.org/10.1117/12.2685230

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