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

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.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-10-16
DOI
https://doi.org/10.5281/zenodo.15321659
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
article

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

article en

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.

Zenodo (CERN European Organization for Nuclear Research)
Asociación de Investigación Metalúrgica del Noroeste (ES)
Openalex Percentile: Top 79%
Machine Learning in Materials Science
DOIOpen Access PDFOpenAlex
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Enhancing Product Lifecycle Efficiency: Harnessing Natural Language Processing for Materials Insight and Optimization · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS