An AI-driven multiscale methodology to develop transparent wood as sustainable functional material by using the SSbD concept

Efficient design, production, and optimization of new safe and sustainable by design materials for various industrial sectors is an on-going challenge for our society, poised to escalate in the future. Wood-based composite materials offer an attractive sustainable alternative to high impact materials such as glass and polymers and have been the focus of experimental research and development for years. Computational and AI-based materials design provides significant speed-up the development of these materials compared to traditional methods of development. However, reliable numerical models are essential for achieving this goal. The AI-TranspWood project, recently funded by the European Commission, has the ambition to develop such computational and AI-based tools in the context of transparent wood (TW), a promising composite with potential applications in various industrial fields. In this project we advance the development specifically by using an Artificial Intelligence (AI)-driven multiscale methodology.

Authors

Institutions

Publication Details

Journal
Computational and Structural Biotechnology Journal
Published
2024-10-18
DOI
https://doi.org/10.1016/j.csbj.2024.10.022
Citations
5
Primary Topic
Additive Manufacturing and 3D Printing Technologies
Type
article
Field-Weighted Citation Impact
1.02

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

An AI-driven multiscale methodology to develop transparent wood as sustainable functional material by using the SSbD concept

Alberto Mariani, Daniele Nuvoli, Antti Puisto, Alexey Khakalo et al.
5 citations
Computational and Structural Biotechnology Journal
Additive Manufacturing and 3D Printing Technologies
1.02
article

An AI-driven multiscale methodology to develop transparent wood as sustainable functional material by using the SSbD concept

Alberto Mariani, Daniele Nuvoli, Antti Puisto, Alexey Khakalo, Päivi Kivikytö-Reponen, Stefania Fortino, Veera Marttila, Kari Kolari
article en
5 citations

Abstract

Efficient design, production, and optimization of new safe and sustainable by design materials for various industrial sectors is an on-going challenge for our society, poised to escalate in the future. Wood-based composite materials offer an attractive sustainable alternative to high impact materials such as glass and polymers and have been the focus of experimental research and development for years. Computational and AI-based materials design provides significant speed-up the development of these materials compared to traditional methods of development. However, reliable numerical models are essential for achieving this goal. The AI-TranspWood project, recently funded by the European Commission, has the ambition to develop such computational and AI-based tools in the context of transparent wood (TW), a promising composite with potential applications in various industrial fields. In this project we advance the development specifically by using an Artificial Intelligence (AI)-driven multiscale methodology.

Computational and Structural Biotechnology JournalVol. 25
University of Sassari (IT), VTT Technical Research Centre of Finland (FI)
European Commission
Responsible consumption and production
Openalex Percentile: Top 25%
Additive Manufacturing and 3D Printing Technologies
1.02
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

An AI-driven multiscale methodology to develop transparent wood as sustainable functional material by using the SSbD concept — Alberto Mariani, Daniele Nuvoli, et al. · Computational and Structural Biotechnology Journal (2024) | TGRS Research Map | TGRS