A deep learning approach for log cutting pattern optimization in wood industry

This thesis describes the work done during an internship at Microtec, which is a leading company in the wood scanning industry. Industrial CT scans provide detailed information on timber quality prior to sawing. Thanks to this scanner information, logs value maps can be generated in order to optimize the cutting pattern process. However, value maps require too much time to be computed, which results in an infeasible solution. The goal of this thesis is to use a deep learning approach in order to approximate these values maps which can be used in the log cutting pattern optimization process to maximize the profit.