0
Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Policy & Risk Sign in to save

Introduction to <scp>data‐driven</scp> systems for plastics and composites manufacturing

Polymer Composites 2023 9 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 40 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Saeed Farahani, Srikanth Pilla Yun Zhang, Yun Zhang, Fausto Tucci, Srikanth Pilla

Summary

Not relevant to microplastics — this is an introduction to a special issue on machine learning and data-driven methods for plastics and composites manufacturing.

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.

Sign in to start a discussion.

Share this paper