Process Monitoring in Friction Stir Welding Using Convolutional Neural Networks

Roman Hartl,Andreas Bachmann,Michael F Zaeh,Jan Bernd Habedank,Thomas Semm

doi:10.3390/met11040535

Roman Hartl, Andreas Bachmann + Show 3 more

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Journal: Metals	Publication Date: Mar 25, 2021
Citations: 22	License type: CC BY 4.0

Affiliation: Technical University of Munich

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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.

Highlights

Friction stir welding (FSW) is a modern joining process in which a weld is produced through frictional heating and by the mixing of material in the plastic state using a rotating tool
The present paper examines the crucial question of whether convolutional neural networks (CNNs) are superior to the other two network types fully connected neural networks (FCNNs) and recurrent neural networks (RNNs) for predicting internal weld defects such as cavities
This resulted in a total of 2040 ROI that were available for the training, validation, The RNN had one sequence input layer with two neurons for the instantaneous and testing of the artificial neural networks (ANNs)

Summary

Introduction

Friction stir welding (FSW) is a modern joining process in which a weld is produced through frictional heating and by the mixing of material in the plastic state using a rotating tool. Since it is a solid-state process well below melting-temperature, the weldability of aluminum alloys is superior compared to fusion welding technologies. FSW is well suited for a variety of joining tasks, especially in the aerospace industry [1]. A recent trend is the use of FSW in the production of heat exchangers and battery trays for electric vehicles [2].

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