How Deep Learning is Used to Increase the Quality Control of Wood Chips by Classification

The greatest expense to fully integrated papermills are the raw material wood chips that make up the slurry that later becomes paper at the reel of the machine. Random and off-line sampling of the chips provides less than .01% classification of the material. Chips that are off specification in size, wrong species or contaminated with bark and other foreign matter can greatly impact the subsequent paper making process. Additionally, vendors or internal suppliers that sell or provide these chips to the mill cannot be held to any penalty or feedback loop for supplying chips outside set quality standards. This paper discusses how camera-based imaging with deep learning (artificial intelligence) is used to provide real-time classification of wood chips on the conveyer line.

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