Open AccessApplication of Multiple Kernel Support Vector Regression for Weld Bead Geometry Prediction in Robotic GMAWProcess
Author(s) -
Nader Mollayi,
Mohammad Javad Eidi
Publication year - 2018
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v8i4.pp2310-2318
Subject(s) - generalization , kernel (algebra) , welding , support vector machine , process (computing) , computer science , gas metal arc welding , artificial intelligence , algorithm , machine learning , geometry , mathematics , mechanical engineering , engineering , laser beam welding , mathematical analysis , combinatorics , operating system
Modelling and prediction of weld bead geometry is an important issue in robotic GMAW process. This process is highly non-linear and coupled multivariable system and the relationship between process parameters and weld bead geometry cannot be defined by an explicit mathematical expression. Therefore, application of supervised learning algorithms can be useful for this purpose. Support vector machine is a very successful approach to supervised learning. In this approach, a higher degree of accuracy and generalization capability can be obtained by using the multiple kernel learning framework, which is considered as a great advantage in prediction of weld bead geometry due to the high degree of prediction accuracy required. In this paper, a novel approach for modelling and prediction of the weld bead geometry, based on multiple kernel support vector regression analysis has been proposed, which benefits from a high degree of accuracy and generalization capability. This model can be used for proper selection of welding parameters in order to obtain a desired weld bead geometry in robotic GMAW process.