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Deep Convolutional Neural Network Modeling and Laplace Transformation Algorithm for the Analysis of Surface Quality of Friction Stir Welded Joints
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Deep Convolutional Neural Network Modeling and Laplace Transformation Algorithm for the Analysis of Surface Quality of Friction Stir Welded Joints
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Journal Article
Deep Convolutional Neural Network Modeling and Laplace Transformation Algorithm for the Analysis of Surface Quality of Friction Stir Welded Joints
2021
Overview
The quality of Friction Stir Welded joint depends on the input parameters like tool rotational speed, tool traverse speed (mm/min), tool tilt angle, and an axial plunge force. If there is any variation in these input parameters then there will be a chance of formation of various surface defects such as groovy edges, flash formation, and non-homogeneous mixing of alloys. The main objective of the present work is to use machine learning algorithms such as Deep Convolutional Neural Network (DCNN) and Laplace transformation algorithm to detect these surface defects present on the Friction Stir Welded joint. The results showed that the used algorithms can easily detect such surface defects with good accuracy.
Publisher
Ediciones Universidad de Salamanca
