Neural networks for X-ray scattering analysis of wood materials

Project Details

Description

The aim of the project is to develop new machine learning based methods for analysing X-ray scattering data from wood materials. Wood and other plant-based renewable resources play an important role in sustainable development. More detailed information on the structure of wood and its cell walls would support its use in the new applications. Methods based on X-ray scattering and imaging can create a more accurate image of wood structure than seen before, but analysing the enormous amounts of data produced by them is not possible without automization. In this project, we develop data analysis methods to efficiently analyse large quantities of X-ray scattering data from wood samples using machine learning and especially neural networks. Our target is to find new interpretations of X-ray scattering data measured from wood materials, and to understand structural differences between different types of wood tissue and cells as well as how they connect with the moisture behaviour of wood.
AcronymNNxWOOD/Penttilä
StatusActive
Effective start/end date01/09/202131/08/2026

Collaborative partners

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 7 - Affordable and Clean Energy
  • SDG 12 - Responsible Consumption and Production

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