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Materials 2016, 9(11), 915; doi:10.3390/ma9110915

Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network

Department of Mechanics Mathematics and Management (DMMM), Polytechnic of Bari, Bari 70126, Italy
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Academic Editor: Daolun Chen
Received: 26 September 2016 / Revised: 27 October 2016 / Accepted: 3 November 2016 / Published: 10 November 2016
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Abstract

A simulation model was developed for the monitoring, controlling and optimization of the Friction Stir Welding (FSW) process. This approach, using the FSW technique, allows identifying the correlation between the process parameters (input variable) and the mechanical properties (output responses) of the welded AA5754 H111 aluminum plates. The optimization of technological parameters is a basic requirement for increasing the seam quality, since it promotes a stable and defect-free process. Both the tool rotation and the travel speed, the position of the samples extracted from the weld bead and the thermal data, detected with thermographic techniques for on-line control of the joints, were varied to build the experimental plans. The quality of joints was evaluated through destructive and non-destructive tests (visual tests, macro graphic analysis, tensile tests, indentation Vickers hardness tests and t thermographic controls). The simulation model was based on the adoption of the Artificial Neural Networks (ANNs) characterized by back-propagation learning algorithm with different types of architecture, which were able to predict with good reliability the FSW process parameters for the welding of the AA5754 H111 aluminum plates in Butt-Joint configuration. View Full-Text
Keywords: Artificial Neural Network (ANN); modeling; simulation; Friction Stir Welding (FSW); mechanical properties; Aluminum Alloy (AA); Ultimate Tensile Strength (UTS); hardness; Heat Effected Zone (HAZ) Artificial Neural Network (ANN); modeling; simulation; Friction Stir Welding (FSW); mechanical properties; Aluminum Alloy (AA); Ultimate Tensile Strength (UTS); hardness; Heat Effected Zone (HAZ)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

De Filippis, L.A.C.; Serio, L.M.; Facchini, F.; Mummolo, G.; Ludovico, A.D. Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network. Materials 2016, 9, 915.

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