#46 Data analytics for structural fibre resources optimisation

The ongoing digitalisation of manufacturing companies enables new potential for optimisation of their processes. With a growing number of sensors implemented in manufacturing systems such as modern large sawmills, a huge volume of data is generated. This data is an important resource to maintain competitiveness.

This project used data analytics approaches to investigate relationships in the production database provided by Hyne Timber. We considered data at 3 different stages of the sawmilling process: the log merchandiser, the green mill, and the dry mill. We also considered how to link data across the various stages.

To develop predictive capabilities of key wood properties, we implemented 2 different machine learning models, and these approaches show promise. This scoping study uncovered a number of possible avenues for future investigations.

Researchers include: Dr Steven Psaltis, Dr Xiaoyu Wang, Prof Ian Turner & Rebecca Cherry.