Measurement of bonding strength of pine, kapur and meranti wood species as function of their surface quality

Hızıroǧlu, Salim; Zhong, Zhao; Tan, Huarong

doi:10.1016/j.measurement.2013.05.005

Cited by 26 publications

(15 citation statements)

References 5 publications

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“…Ultrasonic, optical and stylus type profilometers may be employed to quantify the sur face roughness of wood (Zhong et al 2013). According to previous studies, wood surface roughness may be influenced by grit size of sandpapers, heat treatment and machining (Hiziroglu et al 2013). Adhesion refers to the interaction between the wood substrate and the adhesive.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Bonding Strength and Surface Quality of Wood Exposed to Accelerated Soaking

Zhong¹,

Ng²,

Hızıroǧlu³

2018

JTFS

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This work investigated bonding strength of wood from pine, oak and nyatoh, glued with polyvinyl acetate and rubber-based contact adhesive, and exposed to accelerated soaking in water for 2, 4, 6, 12 and 24 hours. The bonding strength of each sample was determined using a universal testing unit. Surface roughness of the samples was also measured using a profilometer. Control samples had the highest bonding strength values for all three types of species. Soaked pine samples did not have any bonding strength for both adhesives, while the rest of the samples had bonding strength reduction. Oak samples had the highest average roughness value of 7.13 µm and the highest bonding strength in all conditions. Data from this work would provide initial information for utilisation of such species in bonding applications where possible water contact is expected.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Bonding Strength and Surface Quality of Wood Exposed to Accelerated Soaking

Zhong¹,

Ng²,

Hızıroǧlu³

2018

JTFS

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“…It was observed that using veneer with tough surfaces in plywood production reduced adhesion quality (Faust and Rice 1986). Hiziroglu et al (2013) determined surface roughness in the sanding of pine, borneo camphor, and meranti. Zhang et al (2007) evaluated surface roughness in various commercially-produced composite panels including particleboard, mediumdensity fibreboard (MDF), and plywood in addition to ten different solid wood species commonly used in furniture production.…”

Section: Introductionmentioning

confidence: 99%

Using Artificial Neural Networks to Model the Surface Roughness of Massive Wooden Edge-Glued Panels Made of Scotch Pine (Pinus sylvestris L.) in a Machining Process with Computer Numerical Control

Sofuoğlu¹

2015

BioResources

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An artificial neural network (ANN) approach was employed for the prediction and control of surface roughness (Ra and Rz) in a computer numerical control (CNC) machine. Experiments were performed on a CNC machine to obtain data used for the training and testing of an ANN. Experimental studies were conducted, and a model based on the experimental results was set up. Five machining parameters (cutter type, tool clearance strategy, spindle speed, feed rate, and depth of cut) were used. One hidden layer was used for all models, while there were five neurons in the hidden layer of the Ra and Rz models. The RMSE values were calculated as 1.05 and 3.70. The mean absolute percentage error (MAPE) values were calculated as 20.18 and 15.14, which can be considered as a good prediction. The results of the ANN approach were compared with the measured values. It was shown that the ANN prediction model obtained is a useful and effective tool for modeling the Ra and Rz of wood. The results of the present research can be applied in the wood machining industry to reduce energy, time, and cost.

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“…Reprinted by permission from Elsevier: Measurement. Hiziroglu et al 31 Copyright Elsevier (2013). Turkoglu et al 88 studied the glossiness and roughness of thermally modified Oriental beech wood samples after weathering.…”

Section: Surface Roughness Of Wood Materialsmentioning

confidence: 99%

“…The roughness of wood product surfaces after machining is one decisive property affecting their applications such as adhesive bonding strengths and painting quality. 31–34 However, compared with other engineering materials, the deviations of wood surfaces conventionally were not perceived much. 33 One drawback of wood is the desorption and absorption of moisture, and little information was available about the bonding strength and surface roughness of wood with high moisture contents.…”

Section: Introductionmentioning

confidence: 99%

Surface roughness of machined wood and advanced engineering materials and its prediction: A review

Zhong

2021

Advances in Mechanical Engineering

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This article discusses the surface roughness of wood and advanced engineering materials after machining and its prediction. The topics are surface roughness of precision-machined advanced engineering materials, machining of WC and Inconel, rapidly solidified Al alloys, surface roughness of wood materials, and prediction of surface roughness. Findings include that ductile streaks on silicon and glass surfaces ground or lapped with inexpensive machines largely reduced the polishing time to obtain the required surface roughness. Abrasive jet machining could remove the patterns from recycled wafers and improve the surface roughness. The roughness of WC-Co coatings was significantly improved by using the method of fast regime fluidized bed machining. As beryllium is a toxic element, the rapidly solidified Al alloy may be a good insert material to replace BeCu. Higher bonding strengths resulted from rougher surfaces of wood samples. Wood samples had reduced bonding strengths after soaking in water. Optimum artificial neural networks (ANNs) with necessary inputs could accurately predict the roughness values. ANNs trained using particle swarm optimization and genetic algorithms could predict surface roughness better than typical ANNs. Minimum quantity lubrication is a hot research topic to minimize the amount of the fluid for cost and environmental considerations.

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Measurement of bonding strength of pine, kapur and meranti wood species as function of their surface quality

Cited by 26 publications

References 5 publications

Evaluation of Bonding Strength and Surface Quality of Wood Exposed to Accelerated Soaking

Evaluation of Bonding Strength and Surface Quality of Wood Exposed to Accelerated Soaking

Using Artificial Neural Networks to Model the Surface Roughness of Massive Wooden Edge-Glued Panels Made of Scotch Pine (Pinus sylvestris L.) in a Machining Process with Computer Numerical Control

Surface roughness of machined wood and advanced engineering materials and its prediction: A review

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