This paper introduces an innovative approach for the efficient analysis of composites manufacturing processes and phenomena. The method combines low- and high-fidelity simulation schemes with limited amounts of experimental data to train surrogate machine learning (ML) models. Guided by a novel approach, Spatially Weighted Gaussian Process Regression (SWGPR), a predictive model is efficiently constructed and calibrated by assigning datapoint-dependent noise levels to simulation points, establishing a multi-scale data-driven uncertainty structure. This study demonstrates the effectiveness of the method in accurately predicting process- induced deformations (PIDs) for L-shaped cross-ply laminates using minimal experimental efforts. The presented method aims to provide a cost-effective and broadly applicable framework for understanding and improving the design, development, and manufacturing of composites.

Efficient analysis of composites manufacturing using multi-fidelity simulation and probabilistic machine learning / Schoenholz, C.; Zappino, E.; Petrolo, M.; Zobeiry, N.. - In: COMPOSITES. PART B, ENGINEERING. - ISSN 1359-8368. - ELETTRONICO. - 280:(2024). [10.1016/j.compositesb.2024.111499]

Efficient analysis of composites manufacturing using multi-fidelity simulation and probabilistic machine learning

E. Zappino;M. Petrolo;
2024

Abstract

This paper introduces an innovative approach for the efficient analysis of composites manufacturing processes and phenomena. The method combines low- and high-fidelity simulation schemes with limited amounts of experimental data to train surrogate machine learning (ML) models. Guided by a novel approach, Spatially Weighted Gaussian Process Regression (SWGPR), a predictive model is efficiently constructed and calibrated by assigning datapoint-dependent noise levels to simulation points, establishing a multi-scale data-driven uncertainty structure. This study demonstrates the effectiveness of the method in accurately predicting process- induced deformations (PIDs) for L-shaped cross-ply laminates using minimal experimental efforts. The presented method aims to provide a cost-effective and broadly applicable framework for understanding and improving the design, development, and manufacturing of composites.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2988922