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Defectless

If a piece of valuable wood has some defects, it cannot be used to make certain objects, but some others can be made avoiding the defect zone.

Goals: 
Aim of the project is to utilize at best the wood blocks by reducing discards. This is performed by introducing a new software module in the mechanical process that from walnut blanks conducts to the production of specific pieces. The software receives in input the slices acquired by a tomograph through which the wood transits before to be passed to the next machine of the production line. A 3D shape of the wood block is reconstructed and a deep-learning neural net classifies the voxels into good or defect class. A priori knowledge of the 3D designs of each single product and its characteristics permits to choice the most suitable one to be built from that block.
Date: 
Monday, 26 November, 2018 to Saturday, 25 January, 2020
Duration: 
14m
Partners: 

The client Meccanica del Sarca S.p.A

Publications:

  • Davide Boscaini (FBK), Fabio Poiesi (FBK), Stefano Messelodi (FBK), Ayman Younes (Meccanica del Sarca S.p.A.), Donato Antonio Grande (Meccanica del Sarca S.p.A.)
    Localisation of defects in volumetric Computed Tomography scans of valuable wood logs
    1st International Workshop on Industrial Machine Learning, 25th International Conference on Pattern Recognition - ICPR, Milano, Italy, January 2021
Funding: 
LP 6
Unit role: 

Develop software to detect and classify defects in wood and optimize the position of the most suitable model inside it.