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Consumer preferences for flooring in Turkey in terms of purchasing and use

Akyüz İ., Ersen N., Tiryaki S.

Drewno : prace naukowe, doniesienia, komunikaty

2016

vol. 59, nr 198

131--146

In this study, consumer preferences for flooring in terms of purchasing and use, were investigated, as well as consumer attitudes and the reasons for preferences for a particular product, and whether consumer behaviour varied according to gender, age, level of education, occupation and size of household. The study was based on a face-to-face interview of 1005 people throughout Turkey. The data obtained from the survey was analyzed using the statistical package SPSS 19.0 for Windows. According to the results of the study, it is possible to say that the customers preferred flooring which was easy to assemble, with good heat and sound insulation, resistant to physical and mechanical damage, both environmentally- and human-friendly and aesthetically pleasing. Manufacturers should therefore endeavour to meet these expectations by obtaining positive and negative feedback from users of flooring. In conclusion, it was determined that flooring preference, usage, expectations and consumer complaints may differ according to gender, age, level of education, income level, household size and occupation. This study fills an important gap regarding the investigation of consumer preferences for flooring in terms of purchasing and use in Turkey.

Artificial neural network modeling to predict optimum power consumption in wood machining

Tiryaki S., Malkoçoğlu A., Özşahin S.

Drewno : prace naukowe, doniesienia, komunikaty

2016

vol. 59, nr 196

109--125

This paper investigates and models the effects of wood species, feed rate, number of cutters and cutting depth on power consumption during the wood planing process. For this purpose, the samples were planed at a feed rate of 7 and 14 m/min, a cutting depth of 0.5, 1.5, 2.5 and 3.5 mm, and using 1, 2 and 4 cutters, with measurements taken during this process. According to the results, power consumption increased with increasing feed rate, cutting depth and number of cutters. In artificial neural network model, the mean absolute percentage error values between the actual and predicted values were 0.32% for the training data set and 1.15% for the testing data set. In addition, the values of R2 were found to be 0.99 and 0.97 in the training and testing data sets, respectively. It is evident from the results that the designed model may be used to optimize the effects of process parameters on power consumption during the planing process of different wood species. Thus, the findings of the current study can be effectively applied in the wood machining industry in order to reduce the time for further experimental investigations, to lower energy consumption and avoid high machining costs.