Wavelets Application in Prediction of Friction Stir Welding Parameters of Alloy Joints from Vibroacoustic ANN-Based Model
Year: 2014
Type: article
Abstract: 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 inZandYdirections 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.
Source: Abstract and Applied Analysis
DOI: 10.1155/2014/728564
Institutions: Universidad de La Rioja, Universidad de Oriente
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,130
OpenAlex ID: W2059958348