Comparison results of visual and machine strength grading of Scots pine sawn timber from the Silesian Forestry Region in Poland

Comparison results of visual and machine strength grading of Scots pine sawn timber from the Silesian Forestry Region in Poland. The paper presents an analysis of the strength grading results performed by two methods – visual (appearance) and machine, carried out for sawn timber obtained from the Silesian Forestry Region in Poland. Visual strength grading was performed in accordance with PN-D-94021:2013, while the machine strength grading with the use of MTG device from Brookhuis Electronics BV. As a result of the tests, it was confirmed that the machine grading results in a very small share of sawn timber classified as rejects. At the same time, during machine strength grading there were some sawn timber pieces that were not classified for any class or a reject. Based on its visual appearance, such timber elements should be graded as rejects.

Visual and machine strength grading of European ash and maple for glulam application

Medium dense hardwoods (HWs) show higher tensile strength (TS) values than softwoods (SWs). These advantages cannot be utilised effectively because HW grading is not well developed. The aim of the present paper was to analyse the utilisation potential of European ash (Fraxinus spp.) and maple (Acer spp.) grown in Central Europe, which were graded by different methods. The visual grading characteristics of 869 HW boards were determined and the dynamic modulus of elasticity (MOEdyn) and X-ray attenuation (XRA) were measured by an industrial scanner. The specimens were subsequently tested in tension according to EN 408:2010 and according to German visual grading rules show strength values of 28 MPa and 30 MPa, respectively. Machine strength grading and for a combination of manually assessed boards and MOEdyn give rise to higher strength data. MOEdyn, in particular, results in lamella data with 62 MPa for ash and 42 MPa for maple. There is good agreement with recently presented HW tensile profiles. Machine grading with a multisensor system allows better strength prediction compared to the MOEdyn or visual strength grading. Best performance is achieved by a combined grading approach.