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

Date

2021-03-01

Author

Goli, Alireza
Tirkolaee, Erfan Babaee
Weber, Gerhard Wilhelm

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

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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).

URI

https://hdl.handle.net/11511/89757

Journal

FOUNDATIONS OF COMPUTING AND DECISION SCIENCES

DOI

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

Collections

Graduate School of Applied Mathematics, Article

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Citation Formats

A. Goli, E. B. Tirkolaee, and G. W. Weber, “An Integration of Neural Network and Shuffled Frog-Leaping Algorithm for CNC Machining Monitoring,” FOUNDATIONS OF COMPUTING AND DECISION SCIENCES, pp. 27–42, 2021, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/89757.