Presently there are no suitable non-invasive methods for precisely detecting the subsurface defects in logs in real time. Internal defects such as knots, decays, and embedded metals are of greatest concern for lumber production. While defects such as knots and decays (rots) are of major concern related to productivity and yield of high value wood products, embedded metals can damage the saw blade and significantly increase the down time and maintenance costs of saw mills. Currently, a large number of logs end up being discarded by saw mills, or result in low value wood products since they include defects.
Nondestructive scanning of logs using techniques such as Ground Penetrating Radar (GPR) prior to sawing can greatly increase the productivity and yield of high value lumber. In this research, the GPR scanned data has been analyzed to differentiate the defective part of the wooden log from the good part. The location and size of the defect has been found in the GPR scanned data using the MATLAB algorithm. The output of this algorithm can be used as an input for generating operating instructions for a CNC sawing machine.
This paper explains the advantages of the GPR technique, experimental setup and parameters used, data processing using RADAN software for detection of subsurface defects in logs, GPR data processing and analysis using MATLAB algorithm for automated defect detection, and comparison of results between the two processing methods. The results show that GPR in conjunction with the proposed algorithm provides a very promising technique for future on-line implementation in saw mills.