article · IEEE Access
Aluminium alloys are difficult to weld using conventional fusion techniques, making friction stir welding an effective alternative for producing high-quality joints. The mechanical properties of these joints depend strongly on the operational parameters selected during the process. To address this, a hybrid computational method was created to predict the ultimate tensile strength of friction stir welded AA6061 aluminium alloy joints. The system integrates the Henry Gas Solubility Optimisation algorithm into a Random Vector Functional Link network, using rotational speed, welding speed, tilt angle, and tool pin profile as inputs. Validation showed strong agreement between experimental measurements and the model predictions, demonstrating that the approach can reliably identify appropriate operational settings to achieve optimal joint strength.
Aluminium alloys are vital across manufacturing, but their low weldability under standard processes can compromise joint integrity. Determining optimal friction stir welding parameters typically demands extensive physical testing. Applying hybrid machine learning models reduces trial and error, enabling engineers to predict structural strength accurately and identify ideal machine settings with greater speed and consistency.
This predictive model could benefit manufacturing and fabrication operations working with AA6061 aluminium joints by speeding up parameter optimisation and reducing material waste during setup. The work represents an applied research stage, having demonstrated success against experimental laboratory data, but it would require integration into commercial welding control software or quality assurance workflows before industrial adoption.
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Aluminum alloys have low weldability by conventional fusion welding processes. Friction stir welding (FSW) is a promising alternative to traditional fusion welding techniques for producing high quality aluminum joints. The quality of the welded joints is highly dependent on the process parameters used during welding. In this research, a new approach was developed to predict the process parameters and mechanical properties of AA6061-T6 aluminium alloy joints in terms of ultimate tensile strength (UTS). A new hybrid artificial neural network (ANN) approach has been proposed in which Henry Gas Solubility Optimization (HGSO) algorithm has been incorporated to improve the performance of Random Vector Functional Link (RVFL) network. The HGSO-RVFL model was constructed with four parameters; rotational speed, welding speed, tilt angle, and pin profile. The validity of the model was tested, and it was demonstrated that the HGSO-RVFL model is a powerful technique for predicting the UTS of friction stir welded (FSWD) joints. In addition, the effects of process parameters on UTS of welded joints were discussed, where a significant agreement was observed between experimental results and predicted results which indicates the high performance of the model developed to predict the appropriate welding parameters that achieve optimal UTS.
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DOI: 10.1109/access.2020.2990137
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