Wavelets application in prediction of friction stir welding parameters of alloy joints from vibroacoustic ANN-based model

  1. Jiménez-Macías, E. 1
  2. Sánchez-Roca, A. 2
  3. Carvajal-Fals, H. 2
  4. Blanco-Fernández, J. 1
  5. Martínez-Cámara, E. 1
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

  2. 2 Universidad de Oriente - Santiago de Cuba
    info

    Universidad de Oriente - Santiago de Cuba

    Santiago de Cuba, Cuba

    ROR https://ror.org/03kqap970

Revista:
Abstract and Applied Analysis

ISSN: 1085-3375

Any de publicació: 2014

Volum: 2014

Tipus: Article

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DOI: 10.1155/2014/728564 SCOPUS: 2-s2.0-84902177105 WoS: WOS:000336589700001 GOOGLE SCHOLAR lock_openAccés obert editor

Altres publicacions en: Abstract and Applied Analysis

Repositori institucional: lock_openAccés obert Editor

Objectius de Desenvolupament Sostenible

Projectes relacionats

Soldadura por fricción-agitación de materiales disimilares. Caracterización por técnicas de Emisión Acústica e Inteligencia Artificial.

2012/00012/001

Resum

This paper analyses the correlation between the acoustic emission signals and the main parameters of friction stir welding process based on artificial neural networks (ANNs). The acoustic emission signals in Z and Y directions have been acquired by the AE instrument NI USB-9234. Statistical and temporal parameters of discomposed acoustic emission signals using Wavelet Transform have been used as input of the ANN. The outputs of the ANN model include the parameters of tool rotation speed and travel speed, and tool profile, as well as the tensile strength. A multilayer feed-forward neural network has been selected and trained, using Levenberg-Marquardt algorithm for different network architectures. Finally, an analysis of the comparison between the measured and the calculated data is presented. The model obtained can be used to model and develop an automatic control of the parameters of the process and mechanical properties of joint, based on the acoustic emission signals. © 2014 Emilio Jiménez-Macías et al.