Ensemble Deep Learning Ultimate Tensile Strength Classification Model for Weld Seam of Asymmetric Friction Stir Welding
[ 1 ] Instytut Logistyki, Wydział Inżynierii Zarządzania, Politechnika Poznańska | [ P ] employee
2023
scientific article
english
- ensemble deep learning
- convolution neural network (CNN)
- nondestructive testing
- friction stir welding (FSW)
- ultimate tensile strength (UTS)
EN Friction stir welding is a material processing technique used to combine dissimilar and similar materials. Ultimate tensile strength (UTS) is one of the most common objectives of welding, especially friction stir welding (FSW). Typically, destructive testing is utilized to measure the UTS of a welded seam. Testing for the UTS of a weld seam typically involves cutting the specimen and utilizing a machine capable of testing for UTS. In this study, an ensemble deep learning model was developed to classify the UTS of the FSW weld seam. Consequently, the model could classify the quality of the weld seam in relation to its UTS using only an image of the weld seam. Five distinct convolutional neural networks (CNNs) were employed to form the heterogeneous ensemble deep learning model in the proposed model. In addition, image segmentation, image augmentation, and an efficient decision fusion approach were implemented in the proposed model. To test the model, 1664 pictures of weld seams were created and tested using the model. The weld seam UTS quality was divided into three categories: below 70% (low quality), 70–85% (moderate quality), and above 85% (high quality) of the base material. AA5083 and AA5061 were the base materials used for this study. The computational results demonstrate that the accuracy of the suggested model is 96.23%, which is 0.35% to 8.91% greater than the accuracy of the literature’s most advanced CNN model.
01.02.2023
434-1 - 434-28
Article number: 434
CC BY (attribution alone)
open journal
final published version
01.02.2023
at the time of publication
public
100
3,5 [List 2022]