Applications of high-performance plastics and composites have widely been expanded to various industries due to their superior properties, such as high strength-to-weight ratio, chemical resistance, and thermal/electrical insulation. However, the numerous possible combinations of polymers and reinforcements/fillers, the variability of these materials, and their complex manufacturing processes pose challenges in terms of efficiently developing new plastics and composites, accurately modeling their properties, and effectively monitoring and controlling their manufacturing processes. Integrating data-driven techniques, such as machine learning, artificial intelligence, and big data analytics, is a promising pathway to overcome these challenges as it is demonstrated by the state-of-the-art research works presented in this special issue. This article provides background to the readers and introduces the range of topics covered by the articles in this special issue.

Introduction to data-driven systems for plastics and composites manufacturing

Tucci, F
2023-01-01

Abstract

Applications of high-performance plastics and composites have widely been expanded to various industries due to their superior properties, such as high strength-to-weight ratio, chemical resistance, and thermal/electrical insulation. However, the numerous possible combinations of polymers and reinforcements/fillers, the variability of these materials, and their complex manufacturing processes pose challenges in terms of efficiently developing new plastics and composites, accurately modeling their properties, and effectively monitoring and controlling their manufacturing processes. Integrating data-driven techniques, such as machine learning, artificial intelligence, and big data analytics, is a promising pathway to overcome these challenges as it is demonstrated by the state-of-the-art research works presented in this special issue. This article provides background to the readers and introduces the range of topics covered by the articles in this special issue.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4828752
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