Comparison of artificial neural network and multiple linear regression models to predict optimum bonding strength of heat treated woods

Tiryaki, Sebahattin; Özşahin, Şükrü; Yıldırım, İbrahim

doi:10.1016/j.ijadhadh.2014.07.005

Cited by 36 publications

(19 citation statements)

References 30 publications

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“…S mean, Ameanare the mean value of the series. Chokmani, et al [17]; Teryaki, et al [18] the best value of R 2 is 1.0 while . The optimum value of RBias is 0.0 and a better description of RBias and ENash was given also Moriasi, et al [19]; Meral and Cheleng [20].…”

Section: Model Verificationmentioning

confidence: 95%

Weather Forecasting Models Using Neural Networks and Adaptive Neuro Fuzzy Inference for Two Case Studies at Huoston, Texas and Dallas States

Arslan

Kayis

2018

Journal of Asian Scientific Research

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Article History Precipitation.Forecasting of precipitation is one of the most challenging operational tasks done by hydrologists. This operation can be described as most complicated procedure that includes multiple specialized fields of expertise. In this research a comprehensive study was employed to forecast daily precipitation depending on different weather parameters. This was done by using two different methods which are back propagation neural networks BPNN and adaptive neuro inference system ANFIS. Two case studies were selected for this operation which are Huoston, Texas and Dallas, Texas. The high performance of the applied models in forecasting the daily precipitation was concluded especially by using auxiliary weather data with the lagged day precipitation values since the BPNN and ANFIS were able to learn from continuous input data Contribution/ Originality: This study uses new estimation methodology in forecasting the daily precipitation by using different weather parameters for two regions at United states of America. The study can be considered as a comparison between the two well-known methods which are artificial networks and Adaptive Neuro-Fuzzy Inference System.

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Section: Model Verificationmentioning

confidence: 95%

Weather Forecasting Models Using Neural Networks and Adaptive Neuro Fuzzy Inference for Two Case Studies at Huoston, Texas and Dallas States

Arslan

Kayis

2018

Journal of Asian Scientific Research

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“…Odunun yüzey pürüzlülüğünün tahmini için bazı YSA modelleri geliştirilmiş olsa da, daha fazla araştırmaya ihtiyaç duyulduğu açıktır. Öte yandan, ilgili literatürde yapışma mukavemetinin YSA ile tahmini için yapılan çalışmalarda birbirine tutkal vasıtasıyla yapıştırılan ahşap veya ahşap esaslı malzemelerin yapışma direnci deneylerinden elde edilen sonuçların modellenmesi gerçekleştirilmiştir [22,23]. Yürütülen çalışmalar ağaç malzemede vernik katmanı tutunma direncinin YSA ile modellenmesinden farklıdır.…”

Section: Gi̇ri̇ş (Introduction)unclassified

Odunun Yüzey Pürüzlülüğünün ve Adezyon Direncinin Yapay Sinir Ağları ile Tahmini

Özşahin

Singer

2019

Politeknik Dergisi

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ÖZMobilya ve dekorasyon elemanlarının üretiminde kullanılan ağaç malzemelerin yüzey pürüzlülüğünün ve adezyon direncinin belirlenmesi, nihai ürünün kalitesinin değerlendirilmesi bakımından çok önemlidir. Bu makalede ilk olarak, odun türü, kesme yönü ve zımpara kağıdı türünün yüzey pürüzlülüğü üzerine etkilerini incelemek için yapay sinir ağı (YSA) ile yüzey pürüzlülüğü tahmin modeli geliştirilmiştir. Daha sonra, vernik türü, odun türü, kesme yönü ve yüzey pürüzlülüğünün adezyon direnci üzerine etkileri YSA ile geliştirilen adezyon direnci tahmin modeliyle araştırılmıştır. En iyi performansa sahip tahmin modelleri istatistiksel ve grafiksel karşılaştırmalar yoluyla belirlenmiştir. YSA modellerinin kabul edilebilir sapmalarla oldukça tatmin edici neticeler elde ettiği görülmüştür. Sonuç olarak bu çalışmanın bulguları, deneysel araştırmalar için zaman, enerji ve maliyeti azaltmak amacıyla mobilya ve dekorasyon endüstrisinde etkili bir şekilde uygulanabilir. ABSTRACTDetermining the surface roughness and adhesion strength of wood materials used in the manufacturing of furniture and decoration elements is very crucial in terms of evaluating the quality of the final product. In this article, firstly, the surface roughness prediction model was developed with the artificial neural network (ANN) to examine the effects of wood species, cutting direction and sandpaper type on surface roughness. Then, the effects of varnish type, wood species, cutting direction and surface roughness on adhesion strength were investigated with the adhesion strength prediction model developed with ANN. The prediction models with the best performance were determined by statistical and graphical comparisons. It has been observed that ANN models yielded very satisfactory results with acceptable deviations. As a result, the findings of this study could be employed effectively into the furniture and decoration industry to reduce time, energy and cost for empirical investigations.

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“…Machine learning techniques have shown to be an effective tool for modelling and predicting natural parameters in different fields. Among the examples in the literature, these techniques were used to evaluate manufactured paper [17], to obtain the mechanical properties of wood and cork from physical properties [18][19][20], to predict paper properties from its density [5], to discriminate wood types [21] and to evaluate tree growth [22], to name a few.…”

Section: Introductionmentioning

confidence: 99%

Influence of Heartwood on Wood Density and Pulp Properties Explained by Machine Learning Techniques

Iglesias

Santos

Martínez

et al. 2017

Forests

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Abstract:The aim of this work is to develop a tool to predict some pulp properties e.g., pulp yield, Kappa number, ISO brightness (ISO 2470(ISO :2008, fiber length and fiber width, using the sapwood and heartwood proportion in the raw-material. For this purpose, Acacia melanoxylon trees were collected from four sites in Portugal. Percentage of sapwood and heartwood, area and the stem eccentricity (in N-S and E-W directions) were measured on transversal stem sections of A. melanoxylon R. Br. The relative position of the samples with respect to the total tree height was also considered as an input variable. Different configurations were tested until the maximum correlation coefficient was achieved. A classical mathematical technique (multiple linear regression) and machine learning methods (classification and regression trees, multi-layer perceptron and support vector machines) were tested. Classification and regression trees (CART) was the most accurate model for the prediction of pulp ISO brightness (R = 0.85). The other parameters could be predicted with fair results (R = 0.64-0.75) by CART. Hence, the proportion of heartwood and sapwood is a relevant parameter for pulping and pulp properties, and should be taken as a quality trait when assessing a pulpwood resource.

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Comparison of artificial neural network and multiple linear regression models to predict optimum bonding strength of heat treated woods

Cited by 36 publications

References 30 publications

Weather Forecasting Models Using Neural Networks and Adaptive Neuro Fuzzy Inference for Two Case Studies at Huoston, Texas and Dallas States

Weather Forecasting Models Using Neural Networks and Adaptive Neuro Fuzzy Inference for Two Case Studies at Huoston, Texas and Dallas States

Odunun Yüzey Pürüzlülüğünün ve Adezyon Direncinin Yapay Sinir Ağları ile Tahmini

Influence of Heartwood on Wood Density and Pulp Properties Explained by Machine Learning Techniques

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