Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Estimation of the Ampere Consumption of Dimension Stone Sawing Machine Using of Artificial Neural Networks
Nowadays, estimating the ampere consumption and achieve to the optimum condition from the perspective of energy consumption is one of the most important steps to reduce the production costs. In this research it is tried to develop an accurate model for estimating the ampere consumption by using the artificial neural networks (ANN).In the first step, experimental studies were carried out on 7 carbonate rock samples in different conditions at particular feed rates (100, 200, 300and 400) and depth of cuts (15, 22, 30and 35mm) using a fully instrumented laboratory rig that is enable to change the machine parameters and measure the ampere consumption. In next step, a back propagation neural network was designed for modelling the sawing process for predicting the ampere consumption. The input network consisting of two parts: machine, work piece characteristics and the output of neural network was ampere consumption. This research evaluated the competencies of neural networks to estimate the ampere consumption in sawing process. The correlation coefficient between measured and predicted data in training and testing data is 0.95 and 0.97 respectively. The root mean square error (RMSE) for train and test data is 1.2 and 0.7 respectively. The results of this study showed that the ANNs can be used to estimate the ampere consumption with high ability and low error for industrial applications. Moreover, the cost of sawing machine ampere consumption can be accurately estimated using this neural model from some important physical and mechanical properties of rock.
Estimation of the Ampere Consumption of Dimension Stone Sawing Machine Using of Artificial Neural Networks
Nowadays, estimating the ampere consumption and achieve to the optimum condition from the perspective of energy consumption is one of the most important steps to reduce the production costs. In this research it is tried to develop an accurate model for estimating the ampere consumption by using the artificial neural networks (ANN).In the first step, experimental studies were carried out on 7 carbonate rock samples in different conditions at particular feed rates (100, 200, 300and 400) and depth of cuts (15, 22, 30and 35mm) using a fully instrumented laboratory rig that is enable to change the machine parameters and measure the ampere consumption. In next step, a back propagation neural network was designed for modelling the sawing process for predicting the ampere consumption. The input network consisting of two parts: machine, work piece characteristics and the output of neural network was ampere consumption. This research evaluated the competencies of neural networks to estimate the ampere consumption in sawing process. The correlation coefficient between measured and predicted data in training and testing data is 0.95 and 0.97 respectively. The root mean square error (RMSE) for train and test data is 1.2 and 0.7 respectively. The results of this study showed that the ANNs can be used to estimate the ampere consumption with high ability and low error for industrial applications. Moreover, the cost of sawing machine ampere consumption can be accurately estimated using this neural model from some important physical and mechanical properties of rock.
Estimation of the Ampere Consumption of Dimension Stone Sawing Machine Using of Artificial Neural Networks
Ahmad Aryafar (Autor:in) / Reza Mikaeil (Autor:in)
2016
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
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Sawing of blocks of siliceous dimension stone: influence of texture and mineralogy
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