<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This 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).</p>
</abstract>

This 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).

An Integration of Neural Network and Shuffled Frog-Leaping Algorithm for CNC Machining Monitoring

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This paper addresses Acoustic Emission (AE) from Computer Numerical Control (CNC) machining operations. Experimental measurements are performed on the CNC...

An Integration of Neural Network and Shuffled Frog-Leaping Algorithm for CNC Machining Monitoring

Online veröffentlicht: 01. März 2021

Seitenbereich: 27 - 42

Eingereicht: 13. Dez. 2019

Akzeptiert: 14. Apr. 2020

DOI: https://doi.org/10.2478/fcds-2021-0003

Schlüsselwörter
Artificial Neural Network, Shuffled Frog-Leaping Algorithm, Simulated Annealing, Genetic Algorithm, CNC machining, Multi-sensor data fusion

© 2021 Alireza Goli et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

An Integration of Neural Network and Shuffled Frog-Leaping Algorithm for CNC Machining Monitoring

Online veröffentlicht: 01. März 2021

Seitenbereich: 27 - 42

Eingereicht: 13. Dez. 2019

Akzeptiert: 14. Apr. 2020

DOI: https://doi.org/10.2478/fcds-2021-0003

SchlüsselwörterArtificial Neural Network, Shuffled Frog-Leaping Algorithm, Simulated Annealing, Genetic Algorithm, CNC machining, Multi-sensor data fusion

© 2021 Alireza Goli et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Artificial Neural Network, Shuffled Frog-Leaping Algorithm, Simulated Annealing, Genetic Algorithm, CNC machining, Multi-sensor data fusion