scholarly journals Properties of cork oak wood related to solid wood flooring performance

2012 ◽  
Vol 30 ◽  
pp. 569-573 ◽  
Author(s):  
Sofia Knapic ◽  
J.S. Machado ◽  
Helena Pereira
Keyword(s):  
Cork Oak ◽  
Oak Wood ◽  
2021 ◽  
pp. 45-58
Author(s):  
Goran Milic ◽  
Nebojsa Todorovic ◽  
Marko Veizovic ◽  
Ranko Popadic

The subject of this paper is to analyse the drying process of oak lamellas, which are the solid wood top layer of engineered wood flooring. The focus of the first part of the paper is on dehumidification kilns. Drying in a dehumidification kiln is an interesting alternative to conventional drying of thin solid oak wood with the aim of reaching high drying quality in a reasonable time. Drying tests were done in an industrial dehumidification kiln, and drying parameters were compared with the drying in the conventional kiln. Simultaneously, a drying test at a higher temperature was done in the programmable climate chamber. It was demonstrated that thin oak lamellas (approx. 5 mm thick) could be successfully dried in a dehumidification kiln in a relatively short time and with high drying quality. With the applied drying schedule (initial temperature of 36?C, final temperature of 46?C), the drying cycle will last 2 to 5 days, depending on the amount of wood and the initial MC. Due to the high rate of water evaporation and the inability of the kilns to remove it fast enough, the drying of lamellas in both dehumidification and conventional kilns takes place at a higher equilibrium moisture content than the set values.


2006 ◽  
Vol 41 (4) ◽  
pp. 339-350 ◽  
Author(s):  
Sofia Leal ◽  
Vicelina B. Sousa ◽  
Helena Pereira

2020 ◽  
Vol 70 (1) ◽  
pp. 122-133
Author(s):  
Ihsan Kureli ◽  
Nihat Dongel

Abstract This study aimed to determine the effect of different wood flooring layer structures and surface features on water intake, shrinkage, and swelling rates under different relative humidity and water retention conditions. Nine wood flooring sample types were tested: solid wood beech (Fagus orientalis L.) flooring covered with polyurethane varnish, four engineered wood flooring types having different core-layers (solid-wood poplar (Populus nigra L.), 2× medium-density fiberboard, and plywood) covered with ultraviolet dried polyurethane varnish on beech veneer, and four laminated wood flooring types having different core layers (high-density fiberboard, medium-density fiberboard, particleboard, and plywood). The results showed the lowest water retention increase rates for 2 and 24 hours in the high-density fiberboard and medium-density fiberboard core-layered laminated wood floorings. The lowest thickness swelling rate occurred in the laminated wood flooring with a plywood core layer during exposure to high relative humidity, whereas the lowest swelling rate in the width dimension occurred for laminated wood flooring compared with other product types. The lowest thickness shrinkage rate was in the poplar core-layered engineered wood flooring, whereas the lowest shrinkage rate in the width direction was in the medium-density fiberboard core-layered engineered wood flooring and plywood core-layered laminated wood flooring at lower relative humidities. In conclusion, high-density fiberboard and medium-density fiberboard core-layered laminated wood floorings are advisable for flooring exposed to a humid environment. All laminated wood flooring types provided good resistance to swelling. The plywood core-layered laminated wood floorings, poplar, and medium-density fiberboard core-layered engineered wood flooring types performed the best for low-humidity environments.


IAWA Journal ◽  
2009 ◽  
Vol 30 (2) ◽  
pp. 149-161 ◽  
Author(s):  
Vicelina B. Sousa ◽  
Sofia Leal ◽  
Teresa Quilhó ◽  
Helena Pereira

The cork oak (Quercus suber L.) is important for ecological and socioeconomic sustainability and nature conservation in the Mediterranean area. Anatomical and structural features of cork oak wood were characterized at two sites in Portugal, including never-debarked trees and trees under cork production. Cork oak wood showed semi-ring porosity, solitary vessels with simple perforation plates, and large rays. Vessels were arranged in a diagonal to radial pattern, larger and more abundant in earlywood, and gradually decreasing in intermediate and latewood. In trees under cork production vessel distribution and frequency were altered, with more frequent and smaller pores, and a less distinct porosity pattern. Vessel diameter, element length and frequency were 133 ± 49 μm, 433 ± 103 μm and 2.9 ± 0.5 vessels/mm2 for never-debarked trees and 139 ± 50 μm, 341 ± 100 μm and 5.1 ± 1.5 vessels/mm2 for debarked trees. Multiseriate ray width ranged 0.15–1.04 mm, and uniseriate ray height 9.1–791.3 μm. Fibres had a mean length of 1.15 ± 0.20 mm. Vasicentric tracheids were frequent. Tyloses and crystals were commonly present. The anatomical features of cork oak wood favour water conduction and mechanisms of drought adaptation to the Mediterranean climate. The wood can also adapt to cork removal.


Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 336
Author(s):  
Zilong Zhuang ◽  
Ying Liu ◽  
Fenglong Ding ◽  
Zhengguang Wang

Solid wood flooring has good esthetic properties and is an excellent material for interior decoration. To meet the artistic effects of specific interior decoration requirements, the color of solid wood flooring needs to be coordinated. Thus, the color of the produced solid wood flooring needs to be sorted to meet the individual needs of customers. In this work, machine vision, deep learning methods, and ensemble learning methods are introduced to reduce the cost of manual sorting and improve production efficiency. The color CCD camera was used to collect 108 solid wood floors of three color grades provided by the company and obtained 108 18,000 × 2048 pixel wood images. A total of 432 images were obtained after data expansion. Deep learning methods, such as VGG16, DenseNet121, and XGBoost, were compared. After using XGBoost to filter the features, the accuracy of solid wood flooring color classification was 97.22%, the training model time was 5.27 s, the average test time for each picture was 51 ms, and a good result was achieved.


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