Prediction Model of Wooden Logs Cutting Patterns and Its Efficiency in Practice

Abstract

This article deals with the testing of a methodology for creating log cutting patterns. Under this methodology, programs were developed to optimize the log yield. Testing was conducted by comparing the values of the proportions of the individual products resulting from an implementation of the proposed cutting pattern of a specific log with the calculated values of these proportions of products using the tested methodology. For this test, nine pieces of logs (three pieces of oak, three pieces of beech and three pieces of spruce) were chosen, and then the proposed cutting pattern was applied on each log and the proportions of the resulting products were determined gravimetrically. The result of the statistical comparison is as follows: The prediction model that has been tested meets the basic requirement of insensitivity to the tree species. This means that the model tested does not create differences in the results based on the type of wood. In the case of timber, the model statistically significantly underestimates its proportion by 3.7%. The model underestimates the proportion of residues by 0.14%, but is not statistically significant. This model statistically significantly underestimates the proportion of sawdust by 2.25%. By evaluating the results obtained, we can conclude that the prediction model is a good basis for optimizing log yields. In its further development, it has to be supplemented with a log curvature parameter and for the most accurate yield optimization, in terms of the product quality, it must be connected with new scanning technologies as well. These will supplement the prediction model with information about internal and external wood defects and these defects will be taken into account then.

Review of the current state of application of wood defect recognition technology

“…On the one hand it is prone to leakage and mis-detection due to visual fatigue, and on the other hand the manually calibrated defect rejection scheme often does not maximise the use of wood (Lai et al 2021). At the same time, wood processing is more conservative compared to other industries, so it is necessary to develop technology to work with intelligent algorithms for wood defect identification and rejection (Gergeľ et al 2020). This has also become a new hot topic in the wood processing industry (de Geus et al 2021;Zhang et al 2018).…”

Analysis of Qualitative Features of Beech and Oak Trunks as a Determinant of the Quality Assessment

“…The recent optimization has been connected with trends leading towards automation of assessing the qualitative features, which can be seen primarily in wood processing plants [14][15][16]. Optimization approaches to assessing the qualitative features represented by expensive technologies have been unavailable in forestry field operations so far.…”