Laminate flooring selection is an important phase in the design and construction of a building. Owing to the subjectivity and conflicting factors, the fuzzy multicriteria decision-making technique ...can be used for handling the problem. In this study, it is aimed to prioritize laminate flooring selection criteria from experts' perspectives. Five main criteria and twenty subcriteria are determined with the help of experts. A three-level hierarchy is devised for comparisons. The spherical fuzzy analytic hierarchy process is used to determine the importance of each criterion. According to the results, 'safety and health properties' (29.0%) and 'durability properties' (20.6%) are the most significant main criteria. Furthermore, 'walking safety' (10.7%), 'free of harmful substances' (8.6%), and 'scratch resistance' (6.7%) are found as the most important subcriteria. The proposed approach presents a different view because it contributes to analyzing the key attributes of laminate flooring. The findings of this study will help building owners, architects, and designers in making informed choices.
In this study, the fuzzy analytic hierarchy process was employed to prioritize some factors affecting the surface roughness of wood and wood-based materials in CNC machining. Within the model, four ...main factors and eighteen subfactors were defined. After constructing the hierarchical structure, the factors were analyzed through experts' opinions. The results demonstrated that wood properties and machining parameters were the most significant main factors. Furthermore, density was found to be the most important subfactor. Consequently, the findings of this study will help the wood industry in enhancing the surface quality of final products.
Wooden outdoor furniture is a worthwhile investment. When evaluating outdoor furniture, it is important to weigh up decision factors. In situations where there are many conflicting factors, ...decision-makers become undecided about determining the best furniture option. Hence, this study proposes an interval-valued Pythagorean fuzzy analytic hierarchy process-based model to prioritise the key factors influencing wooden outdoor furniture selection. In light of the aim, five main factors were determined: "safety and health properties", "comfort and appearance properties", "durability and mechanical properties", "economic aspects", and "environmental aspects". Each main factor was then subdivided into various subfactors. Pairwise comparisons were performed to obtain the priorities of the factors. According to the results, "safety and health properties" was the most significant main factor. The most important subfactors were found as "tipping resistance", "environmental friendliness", and "free of harmful substances".
The evaluation of wooden toys is a complicated process and can be overwhelming for decisionmakers in the presence of many conflicting criteria. Hence, this study proposes a fuzzy decision-making ...model to identify and prioritize the key attributes of wooden toys. For this purpose, the interval-valued spherical fuzzy analytic hierarchy process (AHP), which is one of the fuzzy multicriteria decision-making methods, is applied to obtain weight vectors. Firstly, the wooden toy evaluation problem is formulated as a multicriteria decision-making problem. Then five main criteria and twenty subcriteria are defined with the help of experts. The decision-making team carries out the pairwise comparisons of the criteria. As a result, the priority weights are computed and the ranking order of the criteria is revealed. Additionally, the validity of the obtained results is supported by conducting a comparative analysis between other popular fuzzy methods: interval type-2 fuzzy AHP, interval-valued Pythagorean fuzzy AHP, and spherical fuzzy AHP. According to the modeling results, the most important criteria are “absence of small parts and sharp edges”, “free of harmful wood preservatives and paints”, “workmanship quality”, “contribution to psychomotor development”, and “contribution to cognitive development”. The proposed framework can be adapted to similar decision processes for the evaluation or improvement of toys. Consequently, the findings of this research will help manufacturers, designers, and consumers in making conscious decisions.
Ocjenjivanje drvenih igračaka složen je proces i za donositelje odluka može biti vrlo težak ako postoji mnogo proturječnih kriterija. Stoga je u ovom istraživanju predložen neizraziti model donošenja odluka za prepoznavanje i određivanje ključnih svojstava drvenih igračaka. Pritom je za dobivanje pondera primijenjen sferni neizraziti analitički hijerarhijski proces (AHP), koji je jedna od neizrazitih višekriterijskih metoda odlučivanja. Problem vrednovanja drvene igračke najprije je formuliran kao višestruki problem odlučivanja. Zatim je uz pomoć stručnjaka definirano pet glavnih kriterija i 20 potkriterija. Tim za donošenje odluka proveo je usporedbu kriterija u parovima. Kao rezultat toga izračunani su ponderi prioriteta i definiran redoslijed kriterija. Komparativnom analizom dodatno je provedena provjera rezultata s rezultatima dobivenim drugim dvjema popularnim neizrazitim metodama: intervalnim tip 2 neizrazitim AHP-om i Pitagorinim neizrazitim AHP-om s intervalnim vrijednostima. Prema rezultatima modeliranja, najvažnijim su se pokazali kriteriji „bez sitnih dijelova i oštrih rubova”, „bez štetnih premaznih materijala”, „kvaliteta izrade”, „doprinos psihomotoričkom razvoju” i „doprinos kognitivnom razvoju”. Predočeni se okvir može prilagoditi za slične procese odlučivanja u ocjenjivanju i poboljšanju igračaka. Slijedom toga, rezultati ovog istraživanja pomoći će proizvođačima, dizajnerima i korisnicima igračaka u donošenju ispravnih odluka.
This study deals with the modeling of the energy consumption in Turkey in order to forecast future projections based on socio-economic and demographic variables (gross domestic product-GDP, ...population, import and export amounts, and employment) using artificial neural network (ANN) and regression analyses. For this purpose, four diverse models including different indicators were used in the analyses. As the result of the analyses, this research proposes Model 2 as a suitable ANN model (having four independent variables being GDP, population, the amount of import and export) to efficiently estimate the energy consumption for Turkey. The proposed model predicted the energy consumption better than the regression models and the other three ANN models. Thus, the future energy consumption of Turkey is calculated by means of this model under different scenarios. The predicted forecast results by ANN were compared with the official forecasts. Finally, it was concluded that all the scenarios that were analyzed gave lower estimates of the energy consumption than the MENR projections and these scenarios also showed that the future energy consumption of Turkey would vary between 117.0 and 175.4
Mtoe in 2014.
•Effects of machining parameters on surface roughness of wood were studied.•Surface roughness decreased with increasing grit number and number of cutter.•Surface roughness increased with increasing ...cutting depth and feed rate.•Experimental results obtained were modeled by artificial neural network (ANN).•It was shown that ANN can be used successfully for modeling surface roughness.
Surface quality of solid wood is very important for its effective utilization in further manufacturing processes. In this study, the effects of wood species, feed rate, number of cutter, cutting depth, wood zone (earlywood–latewood) and grain size of abrasives on surface roughness were investigated and modeled by artificial neural networks. It was shown that the artificial neural network prediction model obtained is a useful, reliable and quite effective tool for modeling surface roughness of wood. Thus, the results of the present research can be successfully applied in the wood industry to reduce the time, energy and high experimental costs.
In this study, an artificial neural network (ANN) approach was employed for modeling the moisture absorption (MA) and thickness swelling (TS) properties of oriented strand board (OSB) in various ...applications. A series of ANN models were developed for the analysis and prediction of correlations between processing parameters and MA and TS of OSB. An ANN model was found for modeling the effects of OSB treatment variables on the MA and TS. The required data for training and testing of the model were obtained from the experimental results of Salay (2010). In designing this model, the MA and TS of the OSB were determined using OSB treatment variables, including board layup type, resin type, application rate of resin, and wax content. When experimental data and results obtained from the ANN were compared by regression analysis using Matlab, it was determined that both groups of data (test and train) were consistent. It was demonstrated that the well-trained feed forward and back propagation multilayer ANN model is a powerful and sufficient tool for the prediction of MA and TS; therefore, by using ANN outputs, satisfactory results can be estimated, rather than measured and hence time and cost are reduced in all the required experimental activities.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
In this study, an artificial neural network (ANN) model was developed for predicting an optimum bonding strength of heat treated woods. The MATLAB Neural Network Toolbox was used for the training and ...optimization of the ANN model. The ANN model having the best prediction performance was detected by trying various networks. Then, the ANN results were compared with the results of multiple linear regression (MLR) model. It was shown that the ANN model produced more successful results compared to MLR model in all cases. The mean absolute percentage errors (MAPE) were found as 1.49% and 3.06% in the prediction of bonding strength values for training and testing data sets, respectively. Determination coefficient (R2) values for training and testing data sets in the prediction of bonding strength by ANN were 0.997 and 0.986, respectively. The results also indicated that the designed model is a useful, reliable and quite effective tool for optimizing the effects of heat treatment conditions on bonding strength of wood. Thanks to using optimum bonding strength values obtained by the model, the increase of the bonding quality of wood products can be provided and the costs for experimental material and energy can be reduced.
ABSTRACT This paper presents a study of the fuzzy analytical hierarchy process (FAHP) for the prioritization of factors having important effects on the surface roughness of wood and wood-based ...materials in the planing process. Firstly, a three-level hierarchical model was devised. Secondly, the FAHP method was employed to determine the weights of the factors. Finally, the prioritization of the factors was carried out taking into account the weights. The results showed that the most significant factors are feed speed (0.300), tool geometry (0.222), and material defect (0.107). Consequently, this study provides a valuable guide to the wood industry to improve the surface quality of wood and wood-based products.
PDF
