Classification and identification of surface defects in friction stir welding: An image processing approach

2016 ◽  
Vol 22 ◽  
pp. 237-253 ◽  
Author(s):  
Ravi Ranjan ◽  
Aaquib Reza Khan ◽  
Chirag Parikh ◽  
Rahul Jain ◽  
Raju Prasad Mahto ◽  
...  
Keyword(s):  
Image Processing ◽  
Friction Stir Welding ◽  
Surface Defects ◽  
Friction Stir ◽  
Processing Approach

scholarly journals Friction Stir Welding of T-Joints: Experimental and Statistical Analysis

2019 ◽  
Vol 3 (2) ◽  
pp. 38 ◽  
Author(s):  
Ibrahim Sabry ◽  
Ahmed M. El-Kassas ◽  
Abdel-Hamid I. Mourad ◽  
Dinu Thomas Thekkuden ◽  
Jaber Abu Qudeiri
Keyword(s):  
Friction Stir Welding ◽  
Heat Affected Zone ◽  
Surface Defects ◽  
Welding Process ◽  
Travel Speed ◽  
Percentage Error ◽  
Nugget Zone ◽  
T Joints ◽  
Friction Stir ◽  
Full Factorial

T-welded joints are commonly seen in various industrial assemblies. An effort is made to check the applicability of friction stir welding for producing T-joints made of AA6063-T6 using a developed fixture. Quality T-joints were produced free from any surface defects. The effects of three parameters, such as the speed of rotation of the tool, axial force, and travel speed were analyzed. Correspondingly, mechanical characteristics such as tensile strength, hardness in three zones (thermal heat affected zone, heat affected zone, and nugget zone) and temperature distribution were measured. The full factorial analysis was performed with various combinations of parameters generated using factorial design and responses. Evident changes in the strength, hardness, and temperature profile were noticed for each combination of parameters. The three main parameters were significant in every response with p-values less than 0.05, indicating their importance in the friction stir welding process. Mathematical models developed for investigated responses were satisfactory with high R-sq and least percentage error.

scholarly journals Process Monitoring in Friction Stir Welding Using Convolutional Neural Networks

Metals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 535
Author(s):  
Roman Hartl ◽  
Andreas Bachmann ◽  
Jan Bernd Habedank ◽  
Thomas Semm ◽  
Michael F. Zaeh
Keyword(s):  
Neural Networks ◽  
Friction Stir Welding ◽  
Convolutional Neural Networks ◽  
Process Monitoring ◽  
Surface Defects ◽  
Network Architectures ◽  
Process Data ◽  
Friction Stir ◽  
Measured Process

Preliminary studies have shown the superiority of convolutional neural networks (CNNs) compared to other network architectures for determining the surface quality of friction stir welds. In this paper, CNNs were employed to detect cavities inside friction stir welds by evaluating inline measured process data. The aim was to determine whether CNNs are suitable for identifying surface defects exclusively, or if the approach is transferable to internal weld defects. For this purpose, 120 welds were produced and examined by ultrasonic testing, which was the basis for labeling the data as “good” or “defective.” Different types of artificial neural network were tested for predicting the placement of the welds into the defined classes. It was found that the way of labeling the data is significant for the accuracy achievable. When the complete welds were uniformly labeled as “good” or “defective,” an accuracy of 98.5% was achieved by a CNN, which was a significant improvement compared to the state of the art. When the welds were labeled segment-wise, an accuracy of 79.2% was obtained by using a CNN, showing that a segment-wise prediction of the cavities is also possible. The results confirm that CNNs are well suited for process monitoring in friction stir welding and their application enables the identification of various defect types.

Microstructural and Mechanical Behaviour Evaluation of Mg-Al-Zn Alloy Friction Stir Welded Joint

2020 ◽  
Vol 17 (3) ◽  
pp. 8150-8159
Author(s):  
Kulwant Singh ◽  
Gurbhinder Singh ◽  
Harmeet Singh
Keyword(s):  
Heat Treatment ◽  
Friction Stir Welding ◽  
Magnesium Alloys ◽  
Mechanical Performance ◽  
Fuel Efficiency ◽  
Research Work ◽  
Grain Structure ◽  
Tensile Fracture ◽  
Friction Stir ◽  
The Impact

The weight reduction concept is most effective to reduce the emissions of greenhouse gases from vehicles, which also improves fuel efficiency. Amongst lightweight materials, magnesium alloys are attractive to the automotive sector as a structural material. Welding feasibility of magnesium alloys acts as an influential role in its usage for lightweight prospects. Friction stir welding (FSW) is an appropriate technique as compared to other welding techniques to join magnesium alloys. Field of friction stir welding is emerging in the current scenario. The friction stir welding technique has been selected to weld AZ91 magnesium alloys in the current research work. The microstructure and mechanical characteristics of the produced FSW butt joints have been investigated. Further, the influence of post welding heat treatment (at 260 °C for 1 h) on these properties has also been examined. Post welding heat treatment (PWHT) resulted in the improvement of the grain structure of weld zones which affected the mechanical performance of the joints. After heat treatment, the tensile strength and elongation of the joint increased by 12.6 % and 31.9 % respectively. It is proven that after PWHT, the microhardness of the stir zone reduced and a comparatively smoothened microhardness profile of the FSW joint obtained. No considerable variation in the location of the tensile fracture was witnessed after PWHT. The results show that the impact toughness of the weld joints further decreases after post welding heat treatment.

Friction stir welding

2010 ◽  
Author(s):  
Daniela Lohwasser ◽  
Zhan Chen
Keyword(s):  
Friction Stir Welding ◽  
Friction Stir
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