Work

Enhancing Product Lifecycle Efficiency: Harnessing Natural Language Processing for Materials Insight and Optimization

Year: 2024

Type: conference-paper

Abstract: This abstract was presented at the 2nd European Symposium on Artificial Intelligence in Manufacturing - ESAIM 2024 (16 October 2024 Athens, Greece), showcasing the use of Natural Language Processing techniques, including Latent Dirichlet Allocation and Large Language Models, to analyse and structure the vast amount of text-based knowledge on materials performance, focusing on polyvinyl chloride across various dimensions, to enhance data analysis in material science and manufacturing.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15321658

Authors: Pérez Couñago, Inés, Suárez Casabiell, Lara, Gregores, Andrea, Precker, Christian, Muiños Landin, Santiago

Institution: Asociación de Investigación Metalúrgica del Noroeste

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

Funder: European Commission

Award: STRENGTHENING THE INTEGRATED APPROACH OF HOLISTIC IMPACT ASSESSMENTS FOR SAFE AND SUSTAINABLE BY DESIGN PLASTIC VALUE CHAIN