%0 Conference Paper %1 eschelbacher2019recognition %A Eschelbacher, Sarah %A Duntschew, Jonas %A Möhring, Hans-Christian %B Production at the leading edge of technology %D 2019 %K ifw %T Recognition of wood and wood-based materials during machining using acoustic emission %X 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 de-sired cutting quality. In the processing of 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 recog-nition automatically in process. Investigations have shown that recording struc-ture-borne sound is useful to differentiate the type of wood and wood-based ma-terials. 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. %@ 978-3-662-60416-8

@inproceedings{eschelbacher2019recognition, 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 de-sired cutting quality. In the processing of 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 recog-nition automatically in process. Investigations have shown that recording struc-ture-borne sound is useful to differentiate the type of wood and wood-based ma-terials. 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.}, added-at = {2020-01-30T16:17:14.000+0100}, author = {Eschelbacher, Sarah and Duntschew, Jonas and Möhring, Hans-Christian}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2de04145efde4a7d8b5e2e5597ca62aff/j.rothmund}, booktitle = {Production at the leading edge of technology}, eventdate = {30.09.2019-02.10.2019}, eventtitle = {9th Congress of the German Academic Association for Production Technology (WGP)}, interhash = {37e08117222ef2abe80132787fb846e5}, intrahash = {de04145efde4a7d8b5e2e5597ca62aff}, isbn = {978-3-662-60416-8}, keywords = {ifw}, timestamp = {2020-01-30T15:17:14.000+0100}, title = {Recognition of wood and wood-based materials during machining using acoustic emission}, year = 2019 }