Recognition of wood and wood-based materials during machining using acoustic emission

Abstract

With increasing automation and the striving for individual products with highest quality requirements, the demand for self-regulating processes in wood processing has increased. The recognition of the material must be taken into account when adjusting the process parameters in order to achieve the desired cutting quality. In the processing of wood and wood-based materials, inhomogeneity and batch scattering are challenges in terms of process monitoring and control. In order to achieve a reliable quality, it is necessary to carry out material recognition automatically in process. Investigations have shown that recording structure-borne sound is useful to differentiate the type of wood and wood-based materials. On the basis of, e.g. image recognition and the use of machine learning methods, the material can be identified within a very short time. This information can be used for setting the optimum process parameters.