scholarly journals Tannin‐based bio‐adhesives for the wood panel industry as sustainable alternatives to petrochemical resins

2021 ◽  
Author(s):  
Ana Arias ◽  
Sara González‐García ◽  
Gumersindo Feijoo ◽  
Maria Teresa Moreira
Keyword(s):  
Wood Panel

scholarly journals Energetic characterization and radiographic analysis of torrefied coated MDF residues

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Paula Gabriella Surdi de Castro ◽  
Vinícius Resende de Castro ◽  
Antonio José Vinha Zanuncio ◽  
José Cola Zanuncio ◽  
Angélica de Cássia Oliveira Carneiro ◽  
...  
Keyword(s):  
Moisture Content ◽  
Energy Production ◽  
Medium Density Fiberboard ◽  
Volatile Matter ◽  
Density Variation ◽  
Low Oxygen ◽  
Wood Panel ◽  
X Ray ◽  
Energetic Characterization ◽  
Elementary Chemical

AbstractThe use of wood panel residues as biomass for energy production is feasible. Heat treatments can improve energy properties while minimizing the emission of toxic gases due to thermoset polymers used in Medium Density Fiberboard (MDF) panels. Torrefaction or pre-carbonization, a heat treatment between 200 and 300 °C with low oxygen availability accumulates carbon and lignin, decreases hygroscopicity, and increases energy efficiency. The objective of this work was to evaluate the energy parameters (immediate, structural, and elementary chemical composition, moisture content, and yield) and density in torrefied MDF panels. The torrefaction improved the energetic features of coated MDF, decreasing the moisture content, volatile matter, and consequently, concentrating the carbon with better results in the samples torrefied for 40 min. The densitometric profiles of the torrefied MDF, obtained by X-ray densitometry, showed a decrease in the apparent density as torrefaction time increased. The digital X-ray images in gray and rainbow scale enabled the most detailed study of the density variation of MDF residues.

scholarly journals Tests and Analyses of Slotted-In Steel-Plate Connections in Composite Timber Shear Wall Panels

2017 ◽  
Vol 2017 ◽  
pp. 1-20
Author(s):  
Ulf Arne Girhammar ◽  
Bo Källsner
Keyword(s):  
Bearing Capacity ◽  
Failure Modes ◽  
Design Method ◽  
Shear Walls ◽  
Test Results ◽  
Horizontal Shear ◽  
Load Bearing Capacity ◽  
Wood Panel ◽  
Load Bearing ◽  
Plate Connections

The authors present an experimental and analytical study of slotted-in connections for joining walls in the Masonite flexible building (MFB) system. These connections are used for splicing wall elements and for tying down uplifting forces and resisting horizontal shear forces in stabilizing walls. The connection plates are inserted in a perimeter slot in the PlyBoard™ panel (a composite laminated wood panel) and fixed mechanically with screw fasteners. The load-bearing capacity of the slotted-in connection is determined experimentally and derived analytically for different failure modes. The test results show ductile postpeak load-slip characteristics, indicating that a plastic design method can be applied to calculate the horizontal load-bearing capacity of this type of shear walls.

Physical–mechanical properties of wood panel composites produced with Qualea sp. sawdust and recycled polypropylene

2019 ◽  
Vol 27 (5) ◽  
pp. 4858-4865
Author(s):  
Daiane Cristina Lima ◽  
Rafael Rodolfo de Melo ◽  
Alexandre Santos Pimenta ◽  
Talita Dantas Pedrosa ◽  
Maila Janaína Coelho de Souza ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Wood Panel ◽  
Recycled Polypropylene

scholarly journals The Buffer Effect of Different Wood Species and the Influence of Oak on Panel Composites Binders

Polymers ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1540 ◽  
Author(s):  
Franco Policardi ◽  
Marion Thebault
Keyword(s):  
Present Article ◽  
Wood Species ◽  
Internal Bond ◽  
Buffer Capacity ◽  
Urea Formaldehyde ◽  
Buffering Capacity ◽  
Buffer Effect ◽  
Wood Panel ◽  
Oak Chips ◽  
Industrial Operations

The buffer action of certain wood species can intensely affect the curing and hardening of some thermosetting wood adhesives. The present article presents a quantification of such buffering effects, determined under controlled conditions, in various wood species. The buffer capacity of oak has been found to be rather extreme and is likely to affect quite heavily the ability of urea-formaldehyde (UF) and melamine-urea-formaldehyde (MUF) wood panel adhesives in industrial operations. A variation of the buffer capacity of furnishes containing between 0% and 30% oak chips has been investigated. This was correlated with the internal bond (IB) strength of MUF bonded laboratory particleboards. The wood mixture buffering capacity increases with the oak content, while the panel IB strength decreases.

scholarly journals Modeling Indoor Relative Humidity and Wood Moisture Content as a Proxy for Wooden Home Fire Risk

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5050 ◽  
Author(s):  
Torgrim Log
Keyword(s):  
Relative Humidity ◽  
Moisture Content ◽  
Moisture Sorption ◽  
Fire Risk ◽  
Cold Weather ◽  
Indoor Climate ◽  
Ambient Conditions ◽  
Wood Panel ◽  
Fire Disaster ◽  
Strong Winds

Severe wooden home conflagrations have previously been linked to the combination of very dry indoor climate in inhabited buildings during winter time, resulting in rapid fire development and strong winds spreading the fire to neighboring structures. Knowledge about how ambient conditions increase the fire risk associated with dry indoor conditions is, however, lacking. In the present work, the moisture content of indoor wooden home wall panels was modeled based on ambient temperature and relative humidity recorded at meteorological stations as the climatic boundary conditions. The model comprises an air change rate based on ambient and indoor (22 °C) temperatures, indoor moisture sources and wood panel moisture sorption processes; it was tested on four selected homes in Norway during the winter of 2015/2016. The results were compared to values recorded by indoor relative humidity sensors in the homes, which ranged from naturally ventilated early 1900s homes to a modern home with balanced ventilation. The modeled indoor relative humidity levels during cold weather agreed well with recorded values to within 3% relative humidity (RH) root mean square deviation, and thus provided reliable information about expected wood panel moisture content. This information was used to assess historic single home fire risk represented by an estimated time to flashover during the studied period. Based on the modelling, it can be concluded that three days in Haugesund, Norway, in January 2016 were associated with very high conflagration risk due to dry indoor wooden materials and strong winds. In the future, the presented methodology may possibly be based on weather forecasts to predict increased conflagration risk a few days ahead. This could then enable proactive emergency responses for improved fire disaster risk management.

scholarly journals Automated Identification of Wood Veneer Surface Defects Using Faster Region-Based Convolutional Neural Network with Data Augmentation and Transfer Learning

2019 ◽  
Vol 9 (22) ◽  
pp. 4898 ◽  
Author(s):  
Augustas Urbonas ◽  
Vidas Raudonis ◽  
Rytis Maskeliūnas ◽  
Robertas Damaševičius
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Visual Inspection ◽  
Surface Defects ◽  
Inspection System ◽  
Wood Veneer ◽  
Wood Panel ◽  
Wood Processing ◽  
Panel Surface

In the lumber and wood processing industry, most visual quality inspections are still done by trained human operators. Visual inspection is a tedious and repetitive task that involves a high likelihood of human error. Currently, new automated solutions with high-resolution cameras and visual inspection algorithms are being tested, but they are not always fast and accurate enough for real-time industrial applications. This paper proposes an automatic visual inspection system for the location and classification of defects on the wood surface. We adopted a faster region-based convolutional neural network (faster R-CNN) for the identification of defects on wood veneer surfaces. Faster R-CNN has been successfully used in medical image processing and object tracking before, but it has not yet been applied for wood panel surface quality assurance. To improve the results, we used pre-trained AlexNet, VGG16, BNInception, and ResNet152 neural network models for transfer learning. The results of the experiments using a synthetically augmented dataset are presented. The best average accuracy of 80.6% was obtained using the pretrained ResNet152 neural network model. By combining all the defect classes, a 96.1% accuracy of finding wood panel surface defects was achieved.

Unidentified portrait on wood panel

1922 ◽  
Vol s12-X (202) ◽  
pp. 150-150
Author(s):  
A. O'c.
Keyword(s):  
Wood Panel
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