The performance of cutting machines in terms of energy consumption and vibration directly affects the production costs. In this work, our aim was to evaluate the performance of cutting machines using hybrid intelligen...The performance of cutting machines in terms of energy consumption and vibration directly affects the production costs. In this work, our aim was to evaluate the performance of cutting machines using hybrid intelligent models. For this purpose, a systematic experimental work was performed. A database of the carbonate and granite rocks was established, in which the physical and mechanical properties of these rocks (i.e., UCS, elastic modulus, Mohs hardness, and Schmiazek abrasivity factor) and the operational parameters (i.e., depth of cut and feed rate) were considered as the input parameters. The predictive models were developed incorporating a combination of the multi-layered perceptron artificial neural networks and genetic algorithm (GANN-BP) and the support vector regression method and Cuckoo optimization algorithm (COA-SVR). The results obtained indicated that the performance of the developed GANN-BP and COA-SVR models was close to each other and that these models had good agreements with the measured values. These results also showed that these proposed models were suitable tools in evaluating the performance of cutting machines.展开更多
基金Project(11039)supported by Shahrood University of Technology,Iran
文摘The performance of cutting machines in terms of energy consumption and vibration directly affects the production costs. In this work, our aim was to evaluate the performance of cutting machines using hybrid intelligent models. For this purpose, a systematic experimental work was performed. A database of the carbonate and granite rocks was established, in which the physical and mechanical properties of these rocks (i.e., UCS, elastic modulus, Mohs hardness, and Schmiazek abrasivity factor) and the operational parameters (i.e., depth of cut and feed rate) were considered as the input parameters. The predictive models were developed incorporating a combination of the multi-layered perceptron artificial neural networks and genetic algorithm (GANN-BP) and the support vector regression method and Cuckoo optimization algorithm (COA-SVR). The results obtained indicated that the performance of the developed GANN-BP and COA-SVR models was close to each other and that these models had good agreements with the measured values. These results also showed that these proposed models were suitable tools in evaluating the performance of cutting machines.
