Goli, AlirezaTirkolaee, Erfan BabaeeWeber, Gerhard Wilhelm2021-03-222021-03-222021Goli, A., Tirkolaee, E. B., & Weber, G. W. (2021). An Integration of Neural Network and Shuffled Frog-Leaping Algorithm for CNC Machining Monitoring. Foundations of Computing and Decision Sciences, 46(1), 27-42.0867-6356https://doi.org/10.2478/fcds-2021-0003https://hdl.handle.net/20.500.12713/1624This paper addresses Acoustic Emission (AE) from Computer Numerical Control (CNC) machining operations. Experimental measurements are performed on the CNC lathe sensors to provide the power consumption data. To this end, a hybrid methodology based on the integration of an Artificial Neural Network (ANN) and a Shuffled Frog-Leaping Algorithm (SFLA) is applied to the data resulting from these measurements for data fusion from the sensors which is called SFLA-ANN. The initial weights of ANN are selected using SFLA. The goal is to assess the potency of the signal periodic component among these sensors. The efficiency of the proposed SFLA-ANN method is analyzed compared to hybrid methodologies of Simulated Annealing (SA) algorithm and ANN (SA-ANN) and Genetic Algorithm (GA) and ANN (GA-ANN).eninfo:eu-repo/semantics/openAccessArtificial Neural NetworkCNC Machining; Multi-Sensor Data FusionGenetic AlgorithmShuffled Frog-Leaping AlgorithmSimulated AnnealingArticle4612742WOS:0006261591000032-s2.0-85102559472N/A10.2478/fcds-2021-0003Q3