MATEC Web Conf.
Volume 269, 2019IIW 2018 - International Conference on Advanced Welding and Smart Fabrication Technologies
|Number of page(s)||6|
|Section||Welding Design, Automation, and Simulation|
|Published online||22 February 2019|
Estimation of Contact Tip to Work Distance (CTWD) using Artificial Neural Network (ANN) in GMAW
Department of Industrial engineering, Faculty of Engineering, Mahidol Engineering, Thailand
2 Material Properties and Failure Analysis Laboratory, Material Properties Analysis and Development Centre, Thailand Institute of Scientific and Technological Research, Thailand
Corresponding author: firstname.lastname@example.org
A method for optimizing monitoring by using Artificial Neural Network (ANN) technique was proposed based on instability of arc voltage signal and welding current signal of solid wire electrode (GMAW). This technique is not only for effective process modeling, but also to illustrate the correlation between the input and output parameters responses. The algorithms of monitoring were developed in time domain by carrying out the Moving Average (M.A) and Root Mean Square (RMS) based on the welding experiment parameters such as travel speed, thickness of specimen, feeding speed, and wire electrode diameter to detect and estimate with a satisfactory sample size. Experiment data was divided into three subsets: train (70%), validation (15%), and test (15%). Error back-propagation of Levenberg-Marquardt algorithm was used to train for this algorithm. The proposed algorithms on this paper were used to estimate the variety the Contact Tip to Work Distance (CTWD) through Mean Square Error (MSE). Based on the results, the algorithms have shown that be able to detect changes in CTWD automatically and real time with takes 0.147 seconds (MSE 0.0087).
© The Authors, published by EDP Sciences, 2019
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.