Artificial neural network application to predict the sawability performance of large diameter circular saws

• Published in: Measurement : journal of the International Measurement Confederation 2016-02, Vol.80, p.12-20
• Main Author: Tumac, Deniz
• Format: Article
• Language: English
• Subjects: Artificial neural network; Large diameter circular saws; Performance prediction; Physical and mechanical properties
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2015.11.025

•The performance of the LDCS was measured in stone processing plants.•ANN is used to predict the areal slab production rate of LDCS.•UCS, BTS, CAI, density and porosity are the selected input parameters.•The Levenberg–Marquardt propagation algorithm is used for training the ANN.•An ANN with five inputs and one output is designed for ASPR prediction. To predict the performance of a large diameter circular saw (LDCS) is among the fundamental steps that are required for determining the practicability of stone production. Natural stone processing plants were visited to measure the areal slab production rate (ASPR) of LDCS in different operational conditions. Neural network toolbox in MATLAB is applied in order to develop a model to predict ASPR of LDCS. An artificial neural network is trained with physical and mechanical properties of eleven stones as input parameters and their associated ASPR values as the target. Uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), Cerchar abrasivity index (CAI), porosity, and density are the physical and mechanical properties that are used as input parameters. In view of its speed, robustness, and the fact that it is very well renowned compared to the other learning algorithms, the Levenberg–Marquardt propagation algorithm is used to train the network. It is explained in detail that a neural network with the previously mentioned input parameters and only one hidden-layer can successfully estimate ASPR for LDCS. It is noticed that, while the number of neurons is less than eight in the single hidden-layer, the network generalizes better than when the number of neurons increases. However, beyond that point, not only the number of neurons does not have any positive effect on performance of the network, but it may also cause the network to memorize the results instead of generalizing them. It can be declared that using ANN to predict ASPR of LDCS may lead the engineers toward a more reliable design and planning.