The mechanical strength of aluminum alloys which are joined with friction stir welding modelling with artificial neural networks

2017 ◽  
Author(s):  
Fikret Sonmez ◽  
Hudayim Basak ◽  
Sehmus Baday
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Friction Stir Welding ◽  
Aluminum Alloys ◽  
Mechanical Strength ◽  
Friction Stir ◽  
Artificial Neural

scholarly journals Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys

Materials ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5227
Author(s):  
David Merayo ◽  
Alvaro Rodríguez-Prieto ◽  
Ana María Camacho
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Aluminum Alloys ◽  
Food Packaging ◽  
Relevant Information ◽  
Plastic Behavior ◽  
Artificial Neural

In metal forming, the plastic behavior of metallic alloys is directly related to their formability, and it has been traditionally characterized by simplified models of the flow curves, especially in the analysis by finite element simulation and analytical methods. Tools based on artificial neural networks have shown high potential for predicting the behavior and properties of industrial components. Aluminum alloys are among the most broadly used materials in challenging industries such as aerospace, automotive, or food packaging. In this study, a computer-aided tool is developed to predict two of the most useful mechanical properties of metallic materials to characterize the plastic behavior, yield strength and ultimate tensile strength. These prognostics are based on the alloy chemical composition, tempers, and Brinell hardness. In this study, a material database is employed to train an artificial neural network that is able to make predictions with a confidence greater than 95%. It is also shown that this methodology achieves a performance similar to that of empirical equations developed expressly for a specific material, but it provides greater generality since it can approximate the properties of any aluminum alloy. The methodology is based on the usage of artificial neural networks supported by a big data collection about the properties of thousands of commercial materials. Thus, the input data go above 2000 entries. When the relevant information has been collected and organized, an artificial neural network is defined, and after the training, the artificial intelligence is able to make predictions about the material properties with an average confidence greater than 95%.

Prediction of Friction Stir Processed AZ31 Magnesium Alloy Micro-Hardness Using Artificial Neural Networks

2014 ◽  
Vol 1043 ◽  
pp. 91-95 ◽  
Author(s):  
Basil M. Darras ◽  
Ibrahim M. Deiab ◽  
Ahmed Naser
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Grain Structure ◽  
Micro Hardness ◽  
Friction Stir ◽  
Fine Grain ◽  
Optimum Parameters ◽  
Modification Technique ◽  
Artificial Neural ◽  
Selection Of

Friction stir processing (FSP) is a microstructural modification technique. In FSP, the material undergoes intense plastic deformation, yielding a dynamically recrystallized fine grain structure. One of the most important issues that need to be tackled in this field is the lack of predictive tools. That enables the selection of the optimum parameters required to achieve the desired modifications on the mechanical properties of the processed materials. In this study, the effects of different FSP parameters (rotational and translational speeds) on the resulting micro-hardness of friction stir processed AZ31 magnesium sheets are examined. Variations of micro-hardness with longitudinal and through-thickness positions are also investigated. Artificial neural networks (ANNs) are used to model and predict the resulting micro-hardness.

Prediction of the surface quality of friction stir welds by the analysis of process data using Artificial Neural Networks

2020 ◽  
Vol 234 (5) ◽  
pp. 732-751 ◽  
Author(s):  
R Hartl ◽  
B Praehofer ◽  
MF Zaeh
Keyword(s):  
Neural Networks ◽  
Friction Stir Welding ◽  
Surface Quality ◽  
Convolutional Neural Networks ◽  
Visual Inspection ◽  
Process Data ◽  
Friction Stir ◽  
Artificial Neural ◽  
Topography Analysis

Friction stir welding is an advanced joining technology that is particularly suitable for aluminum alloys. Various studies have shown that welding quality depends significantly on the welding speed and the rotational speed of the tool. It is frequently possible to detect an unsuitable setting of these parameters by examining the resulting surface defects, such as increased flash formation or surface galling. In this work, Artificial Neural Networks were used to analyze process data in friction stir welding and predict the resulting quality of the weld surface. For this purpose, nine different variables were recorded during friction stir welding of EN AW-6082 T6 sheets: the forces and accelerations in three spatial directions, the spindle torque, and temperatures at the tool shoulder and tool probe. In Case 1, the welds were assigned to the classes good and defective on the basis of a human visual inspection of the weld surface. In Case 2, the welds were categorized into the two classes on the basis of a surface topography analysis. Subsequently, three different major Artificial Neural Network architectures were tested for their ability to predict the surface quality: Feed Forward Fully Connected Neural Networks, Recurrent Neural Networks and Convolutional Neural Networks. The highest classification accuracy was achieved when Convolutional Neural Networks were used. Thus, the evaluation of the force signal transverse to the welding direction yielded the highest accuracy of 99.1% for the prediction of the result of the human visual inspection. The result achieved for the prediction of the topography analysis was an accuracy of 87.4% when the spindle torque was evaluated. Using all nine different process variables to predict the topography analysis, the accuracy could be improved slightly to 88.0%. The sampling rate of the spindle torque was varied between 40 Hz and 9600 Hz and no significant influence was determined. The findings show that Convolutional Neural Networks are well suited for the interpretation of friction stir welding process data and can be used to predict the resulting surface quality. In future work, the results are to be used to develop a parameter optimization method for friction stir welding.

scholarly journals Artificial neural networks for mechanical strength prediction of lightweight mortar

2011 ◽  
Vol 6 (16) ◽  
pp. 3406-3417 ◽  
Author(s):  
S V Razavi ◽  
M Z Jumaat ◽  
H EI Shafie Ahmed ◽  
Mohammadi Pegah
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mechanical Strength ◽  
Strength Prediction ◽  
Artificial Neural ◽  
Lightweight Mortar

Modeling of Friction Stir Welding of AL7075 Using Neural Networks

2012 ◽  
Vol 3 (1) ◽  
pp. 66-79 ◽  
Author(s):  
Sasidhar Muttineni ◽  
Pandu R. Vundavilli
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Neural Networks ◽  
Friction Stir Welding ◽  
Aluminum Alloys ◽  
High Speed ◽  
Welding Process ◽  
High Speed Steel ◽  
Operating Conditions ◽  
Friction Stir

Friction stir welding (FSW) is a solid state welding process, which is used for the welding of aluminum alloys. It is important to note that the mechanical properties of the FSW process depends on various process parameters, such as spindle speed, feed rate and shoulder depth. Two different tool materials, such as High speed steel (HSS) and H13 are considered for the welding of Al 7075. The present paper deals with the modeling of FSW process using neural networks. A three layered feed forward neural network (NN) has been used to model the FSW of aluminum alloys. It is important to note that the connection weights and bias values of the NN are optimized with the help of a binary coded genetic algorithm (GA). The training of the NN with the help of GA is a time consuming process. Hence, offline training has been provided to optimize the connection weights and bias values of the neural network. Once, the training is over, the GA trained neural network will be used for online prediction of the mechanical properties of FSW process at different operating conditions.

Sign in / Sign up

Export Citation Format

Share Document