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.
Highlights
The need to reduce operating costs by optimizing production processes in order to maximize the yield of the input raw material has not been avoided in the wood processing industry
We can conclude from the results that the tested prediction model fulfils the basic requirement of insensitivity to the tree species
This means that the model tested does not produce differences in the result based on the type of wood. It would mean either favoring or disadvantaging some kinds of wood, which would be a negative effect; The use of this prediction model to calculate the quantity of products from the proposed cutting pattern is appropriate because the low values of the percentage differences of the individual products were confirmed by comparing the real values of the percentages of the resulting products with the calculated values; In the case of timber products, the model significantly underestimates this proportion by 3.7%
Summary
The need to reduce operating costs by optimizing production processes in order to maximize the yield of the input raw material has not been avoided in the wood processing industry. In order to make the wood processing industry more competitive in the future, an increase in the technological level of industry leaders is a priority, but on the other hand, this industry is very conservative, and innovations must be introduced gradually and be presented through demonstration examples and objects [4]. It is clear from the literature collected that many authors have been involved in the creation of cutting patterns of logs.
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