Its natural aesthetics make wood an attractive material for construction and design. However, there is no detailed understanding of the relationships between human perception of the appearance and measurable features of wood surfaces that could be used for controlling sawn timber production. This study investigated whether wood surfaces can be classified according to their visual appearance on the basis of wood feature measurements. Cluster analysis was used to discover a classification based on a set of feature pattern variables in a sample of 300 softwood floorboards. A finely graded visual appearance sorting provided a reference. Discriminant analysis was applied to identify the relevant variables from the tested set and to assess predictability of the classification. The results indicated that visual appearance sorting could be approximated quite well by the variable-based classification after pregrouping according to board position in the log. Ambivalent results were obtained for group prediction within the validation sample. While for boards from some groups prediction was mostly or entirely correct, boards from other groups were largely misclassified. An effect of the available sample was one of the surmised causes, making repetition of the analysis based on a larger sample a desirable focus of further research.
Fully mechanized timber harvesting systems are well established in forest operations worldwide. In cut-to-length (CTL) systems, forwarders are used for extracting logs from the stand. The productivity of a forwarder is related to site- and stand-specific characteristics, technical parameters, organizational aspects, and the individual skills of the operator. The operator’s performance during “loading” considerably affects forwarder productivity, since this element occupies nearly 50% of forwarding cycle time in CTL operations. When positioning the forwarder for loading, different loading angles and loading distances arise. Additionally, different log orientation angles in relation to the machine operating trail can be observed. Therefore, an in-depth analysis of loading conditions was conducted. The goal of this pilot case study was to explore the potential impact of different loading angles and distances, and log orientation angles, on time consumption per loading cycle in order to derive indications for more efficient work practices. Therefore, controlled loading sequences were tested on a physical Rottne-F10-based forwarder simulator with an experienced forest machine operator. Three loading angles (45°, 90° and 135° azimuthal to the machine axis) with five loading distances (3, 4, 5, 6 and 7 m), and three log orientation angles (45°, 90°, 135°), resulted in a total of 45 settings, which were tested in 10 repetitions each. The time required for a loading cycle was captured in a time study, applying the snap-back method. Results showed that all three tested variables had a significant influence on time consumption per loading cycle. Loading at an angle of 135°, and from a close (3 m) or far distance (7 m) led to especially increased cycle times. Loading from 4 to 6 m distance could be detected as an optimal loading range. Additionally, log orientation angles of 45° and 90° led to increased loading efficiency. Even if the validity of the results may be limited due to different conditions and influencing factors in field forwarding operations, these data can contribute to a better understanding of the loading element and, in particular, to productivity determining factors of forwarder work.
Abstract:The use of fully-mechanized operations, normally targeted at coniferous species, has also been on the rise in mixed-species and continuous-cover forests comprised of a strong share of deciduous species. With special form characteristics (complex crowns, large-diameter branches, forks and sweeps, high wood density, etc.), deciduous trees can lead to wide-ranging harvesting productivities, often divergent from those originally derived from coniferous species. Due to the importance and growing interest in mechanizing operations in close-to-nature mixedwood and deciduous stands, obtaining insight on harvesting productivity in large-diameter deciduous trees was of interest. This study located in Bavaria, Germany, monitored four harvesters (two wheeled and two tracked machines) operated in four distinct harvest blocks (case studies), all of which had a high percentage of large-diameter European beech and oak trees. Harvesting productivity and volume recovery was assessed and quantified. Based on the field inventory of European beech and oak trees and continuous time-and-motion study, average harvesting productivity ranged from 29 to 43 m 3 /PMH 0 (productive machine hours without delay), whereas volume recovery fluctuated between 73% and 85% for trees that were completely felled and processed by machines. Because of the rather limited sample size and the variable conditions between case studies, results should only be used as general orientation on the performance of the tested machines and additional research is suggested to further understand the influence of tree form characteristics on impediments to mechanized processing.
Wood, as a natural material, has favourable properties in both technical and aesthetic aspects. Due to its inherent variability, production of high-quality sawn timber demands adequate control of log conversion, which is feasible with computed tomography (CT) log scanning. Existing appearance grading rules for sawn timber might not fully reflect people's visual perception of wood surfaces, and therefore, an alternative, more perception-oriented appearance classification could be beneficial. An appearance classification of sawn timber based on partial least squares discriminant analysis (PLS-DA) of knot-pattern variables was developed and tested. Knot-pattern variables derived from images of board faces were used in training PLS-DA models against an initial classification of the board faces previously established by aid of cluster analysis. Virtual board faces obtained from simulated breakdown of 57 CT-scanned Norway spruce logs were graded according to the developed classification. Visual assessment of the grading results indicated that the classification was largely consistent with human perception of board appearance. An initial estimation of the potential to optimize log rotation, based on CT data, for the established appearance grades was derived from the simulations. Considerable potential to increase the yield of a desired appearance grade, compared to conventional log positioning, was observed.
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