Resumen
It has been determined that the most expedient and economically beneficial flaw detection method for plywood is the impact method. Samples of plywood with and without a defect were used for the research. Among the acoustic techniques, the most accurate is considered to be ultrasound; its implementation, however, requires that the surface of plywood should be treated with a special substance, which makes the method impractical for plywood raw materials. Nevertheless, given the accuracy of the method, we have performed a correlation analysis involving the impact method. The chosen initial parameters for the shock method were the number of pulsations, the oscillation frequency and the coefficient of harmonic distortions of a shock sensor's signal. The ultrasound study has been found to produce almost identical results with previous experiments, especially regarding a harmonic distortion coefficient (Kh=0.84). This allows us to argue that the selected parameters make it possible to reliably detect a defect in plywood. Solutions for automating the flaw detection process have been suggested. A device has been designed to control quality and to enable the automated selective sorting of plywood, as well as a multichannel automated quality control system for plywood to be installed at a production line. The proposed systems would make it possible to perform the automated flaw detection in plywood, both in the form of finished products and at the production stage. Information on the quality of plywood can be transferred both to workers at a warehouse and to a transportation robot, as well as to a production line in order to run an analysis and identify causes of defects and to correct the technological process parameters. Automating the flaw detection process would improve its speed and accuracy. We have proposed an easy-to-use relative criterion of plywood quality, which makes it possible to eliminate measurement errors caused by instability in the plywood oscillation amplitude at a sensor's impact. That makes it possible to significantly improve the accuracy of detecting internal defects in the non-destructive quality control