Document Type
Article
Publication Date
5-11-2023
Publication Title
Polymer Composites
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.
Recommended Citation
Farahani, Saeed; Pilla, Srikanth; Zhang, Yun; and Tucci, Fausto, "Introduction to Data-driven Systems for Plastics and Composites Manufacturing" (2023). Mechanical Engineering Faculty Publications. 430.
https://engagedscholarship.csuohio.edu/enme_facpub/430
DOI
10.1002/pc.27414
Version
Publisher's PDF
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
