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

• Published in: Foundations of computing and decision sciences Vol. 46; no. 1; pp. 27 - 42
• Main Authors: Goli, Alireza, Tirkolaee, Erfan Babaee, Weber, Gerhard-Wilhelm
• Format: Journal Article
• Language: English
• Published: Poznan Sciendo 01.03.2021; De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
• Subjects: Artificial Neural Network; CNC machining; Genetic Algorithm; Multi-sensor data fusion; Shuffled Frog-Leaping Algorithm; Simulated Annealing
• ISSN: 2300-3405; 0867-6356; 2300-3405
• DOI: 10.2478/fcds-2021-0003

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).