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

Goli A., Tirkolaee E. B., Weber G.

FOUNDATIONS OF COMPUTING AND DECISION SCIENCES, vol.46, no.1, pp.27-42, 2021 (ESCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 46 Issue: 1
  • Publication Date: 2021
  • Doi Number: 10.2478/fcds-2021-0003
  • Journal Name: FOUNDATIONS OF COMPUTING AND DECISION SCIENCES
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH, Directory of Open Access Journals
  • Page Numbers: pp.27-42
  • Keywords: Artificial Neural Network, Shuffled Frog-Leaping Algorithm, Simulated Annealing, Genetic Algorithm, CNC machining, Multi-sensor data fusion, PREDICTION, SYSTEM, ENERGY, SENSOR, ANN
  • Middle East Technical University Affiliated: No

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).