Comparison of artificial neural network and multiple linear regression models to predict optimum bonding strength of heat treated woods
Source abstract
Abstract
Abstract 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.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Wood Treatment and Properties · Natural Fiber Reinforced Composites · Textile materials and evaluations
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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