Using artificial neural networks for modeling surface roughness of wood in machining process

Tiryaki, Sebahattin; Malkoçoğlu, Abdülkadir; Özşahin, Şükrü

doi:10.1016/j.conbuildmat.2014.05.098

Cited by 50 publications

(17 citation statements)

References 26 publications

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“…When the results given in Table 8 are examined, it is seen that feed speed (0.169), tooth shape and geometry (0.083), and cutting speed (0.076) are the most important factors. Many researchers reported the effect of feed speed on the surface roughness of wood and wood-based materials, and the results showed that feed speed is an important factor in achieving a smooth surface (Hernández and Cool, 2008;Iskra and Hernández, 2009;Prakash and Palanikumar, 2011;Tiryaki et al, 2014). Several researchers stated that tooth shape and geometry is directly responsible for the surface quality of the final product (Budakçı et al, 2011;Kminiak et al, 2015).…”

Section: Resultsmentioning

confidence: 99%

Employing an analytic hierarchy process to prioritize factors influencing surface roughness of wood and wood-based materials in the sawing process

Singer¹,

Özşahin²

2018

Turk J Agric For

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This paper focuses on the prioritization of factors having substantial effects on the surface roughness of wood and wood-based materials in the sawing process. Within the model, four main factors were defined: cutting tool properties, machining parameters, wood structure and properties, and cutting phenomena. Furthermore, each main factor was subdivided into various subfactors. The analytic hierarchy process method was proposed to obtain the priorities of the factors. The results showed that feed speed, tooth shape and geometry, and cutting speed are the most important factors. Based on the obtained results, it can be said that the most important factors can be easily determined by the proposed method. Consequently, this study presents a road map for the wood industry to achieve a high quality surface.

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Section: Resultsmentioning

confidence: 99%

Employing an analytic hierarchy process to prioritize factors influencing surface roughness of wood and wood-based materials in the sawing process

Singer¹,

Özşahin²

2018

Turk J Agric For

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“…Sofuoglu 101 predicted the surface roughness using an ANN, and demonstrated that the ANN method could model the roughness of the machined wood surfaces. Tiryaki et al 102 also reported that using ANNs could predict the roughness of their machined wood surfaces.…”

Section: Prediction Of Surface Roughnessmentioning

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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“…The value of F T is not given as an input to the algorithm and its value is randomly decided by the algorithm using Eq. (30). After conducting a number of experiments on many benchmark functions it is concluded that the algorithm performs better if its value is between 1 and 2, however, the algorithm is found to perform much better if the value of F T is either 1 or 2 and hence to simplify the algorithm, the teaching factor is suggested to take either 1 or 2 depending on the rounding up criteria given by Eq.…”

Section: 1 Teacher Phasementioning

confidence: 99%

Optimization of Tool Geometry Parameters for Rotary Peeling Veneer Process Based on TLBO Algorithm

Fatma-zohra

Aknouche

Hamrani

2018

Proceedings of the Third International Symposium on Materials and Sustainable Development

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Using artificial neural networks for modeling surface roughness of wood in machining process

Cited by 50 publications

References 26 publications

Employing an analytic hierarchy process to prioritize factors influencing surface roughness of wood and wood-based materials in the sawing process

Employing an analytic hierarchy process to prioritize factors influencing surface roughness of wood and wood-based materials in the sawing process

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

Optimization of Tool Geometry Parameters for Rotary Peeling Veneer Process Based on TLBO Algorithm

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