Open AccessEstimating modulus of elasticity (MOE) of particleboards using artificial neural networks to reduce quality measurements and costs
Author(s) -
Rıfat Kurt,
Selman Karayılmazlar
Publication year - 2019
Publication title -
drvna industrija
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.287
H-Index - 19
eISSN - 1847-1153
pISSN - 0012-6772
DOI - 10.5552/drvind.2019.1840
Subject(s) - mean absolute percentage error , mean squared error , artificial neural network , young's modulus , elasticity (physics) , absolute deviation , statistics , mathematics , reliability engineering , computer science , engineering , materials science , composite material , artificial intelligence
There are a large number of costs that enterprises need to bear in order to produce the same product at the same quality for a more affordable price. For this reason, enterprises have to minimize their expenses through a couple of measures in order to offer the same product for a lower price by minimizing these costs. Today, quality control and measurements constitute one of the major cost items of enterprises. In this study, the modulus of elasticity values of particleboards were estimated by using Artificial Neural Networks (ANN) and other mechanical properties of particleboards in order to reduce the measurement costs in particleboard enterprises. In addition to that, the future values of modulus of elasticity were also estimated using the same variables with the purpose of monitoring the state of the process. For this purpose, data regarding the mechanical properties of the boards were randomly collected from the enterprise for three months. The sample size (n) was: 6 and the number of samples (m): 65 and a total of 65 average measurement values were obtained for each mechanical property. As a result of the implementation, the low Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) and Mean Squared Error (MSE) performance measures of the model clearly showed that some quality characteristics could easily be estimated by the enterprises without having to make any measurements by ANN.