Prediction of the vertical force during FSW of AZ31 magnesium alloy sheets using an artificial neural network-based model

Prediction of the vertical force during FSW of AZ31 magnesium alloy sheets using an artificial... A multivariable empirical model based on an artificial neural network (ANN) was developed in order to predict the vertical force occurring during friction stir welding (FSW) of sheets in AZ31 magnesium alloy. To this purpose, FSW experiments were performed at different values of rotational and welding speeds, and the vertical force versus time curve was recorded during the different stages of the process by means of a dedicated sandwich dynamometer. Such results were used in the training stage of the artificial neural network-based model developed to predict vertical force versus time curves. A multi-layer feed forward ANN, using the back-propagation algorithm, consisting of the input layer with four input parameters (rotational speed, welding speed, rotational speed to welding speed ratio and processing time), two hidden layers with four neurons each, and the output layer with the vertical force as output, was built and trained. The generalization capability of the ANN was tested using a two-step procedure: in the former, the leave-one-out cross-validation method was used whilst, in the latter, curves not included in the training dataset were taken into account. The low values of the relative error and average absolute relative error, and the high correlation coefficients between predicted and experimental results have proven the excellent capability of the artificial neural network in modeling complex shape of the curve and in capturing the effect of the process parameters on the vertical force without a priori knowledge of the complex microstructural and mechanical mechanisms taking place during friction stir welding. Finally, the relationship between vertical force and processing time, at different welding and rotational speeds, was also predicted using the support vector machine algorithm and the results were compared with those given by the ANN-based model. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Computing and Applications Springer Journals

Prediction of the vertical force during FSW of AZ31 magnesium alloy sheets using an artificial neural network-based model

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Publisher
Springer Journals
Copyright
Copyright © 2018 by The Natural Computing Applications Forum
Subject
Computer Science; Artificial Intelligence; Data Mining and Knowledge Discovery; Probability and Statistics in Computer Science; Computational Science and Engineering; Image Processing and Computer Vision; Computational Biology/Bioinformatics
ISSN
0941-0643
eISSN
1433-3058
DOI
10.1007/s00521-018-3562-6
Publisher site
See Article on Publisher Site

Abstract

A multivariable empirical model based on an artificial neural network (ANN) was developed in order to predict the vertical force occurring during friction stir welding (FSW) of sheets in AZ31 magnesium alloy. To this purpose, FSW experiments were performed at different values of rotational and welding speeds, and the vertical force versus time curve was recorded during the different stages of the process by means of a dedicated sandwich dynamometer. Such results were used in the training stage of the artificial neural network-based model developed to predict vertical force versus time curves. A multi-layer feed forward ANN, using the back-propagation algorithm, consisting of the input layer with four input parameters (rotational speed, welding speed, rotational speed to welding speed ratio and processing time), two hidden layers with four neurons each, and the output layer with the vertical force as output, was built and trained. The generalization capability of the ANN was tested using a two-step procedure: in the former, the leave-one-out cross-validation method was used whilst, in the latter, curves not included in the training dataset were taken into account. The low values of the relative error and average absolute relative error, and the high correlation coefficients between predicted and experimental results have proven the excellent capability of the artificial neural network in modeling complex shape of the curve and in capturing the effect of the process parameters on the vertical force without a priori knowledge of the complex microstructural and mechanical mechanisms taking place during friction stir welding. Finally, the relationship between vertical force and processing time, at different welding and rotational speeds, was also predicted using the support vector machine algorithm and the results were compared with those given by the ANN-based model.

Journal

Neural Computing and Applications – Springer Journals

Published: May 29, 2018

References

  • Friction stir welding and processing
    Mishra, RS; Ma, ZY
  • Recent advances in friction-stir welding—process, weldment structure and properties
    Nandan, R; DebRoy, T; Bhadeshia, HKDH
  • Joining technologies
    Cabibbo, M; Forcellese, A; Simoncini, M
  • Forming of tailor welded aluminum blanks. Aluminum and magnesium for automotive applications
    Stasik, MC; Wagoner, RH
  • On the role of the thermal contact conductance during the friction stir welding of an AA5754-H111 butt joint
    Contuzzi, N; Campanelli, SL; Casalino, G; Ludovico, AD
  • Design of the friction stir welding tool using the continuum based FEM model
    Buffa, G; Hua, J; Shivpuri, R; Fratini, L
  • Effect of tool pin thread forms on friction stir weldability of different aluminum alloys
    Reza-E-Rabby, MD; Reynolds, AP
  • Sustainability analysis of friction stir welding of AA5754 sheets
    Bevilacqua, M; Ciarapica, FE; D’Orazio, A; Forcellese, A; Simoncini, M
  • Similar and dissimilar FSWed joints in lightweight alloys: heating distribution assessment and IR thermography monitoring for on-line quality control
    Forcellese, A; Martarelli, M; Pandarese, G; Simoncini, M
  • Effect of welding processes on tensile properties of AA6061 aluminum alloy joints
    Lakshminarayanan, AK; Balasubramanian, V; Elangovan, K
  • Comparison of energy consumption and environmental impact of friction stir welding and gas metal arc welding for aluminum
    Shrivastava, A; Krones, M; Pfefferkorn, FE
  • Influence of shoulder geometry and coating of the tool on the friction stir welding of aluminium alloy plates
    Casalino, G; Campanelli, S; Mortello, M
  • Effects of forge axis force and backing plate thermal diffusivity on FSW of AA6056
    Upadhyay, P; Reynolds, AP
  • Optimization of different welding processes using statistical and numerical approaches—a reference guide
    Benyounis, KY; Olabi, AG
  • In-process control strategies for friction stir welding of AZ31 sheets with non-uniform thickness
    Buffa, G; Campanella, D; Forcellese, A; Fratini, L; Commare, U; Simoncini, M
  • A review of numerical analysis of friction stir welding
    He, X; Gu, F; Ball, A
  • Effect of process parameters on vertical forces and temperatures developed during friction stir welding of magnesium alloys
    Forcellese, A; Martarelli, M; Simoncini, M
  • Experimental study of the forces acting on the tool in the friction-stir welding of AA 2024 T3 sheets
    Astarita, A; Squillace, A; Carrino, L
  • Force generation during friction stir welding of AA2024-T3
    Trimble, D; Monaghan, J; O’Donnell, GE
  • Friction stir welding of tailored blanks: investigation on process feasibility
    Buffa, G; Fratini, L; Hua, J; Shivpuri, R
  • Testing and modelling of materials response to deformation
    Bariani, PF; Negro, T; Bruschi, S
  • Artificial neural network approach to predict the flow stress in the isothermal compression of as-cast TC21 titanium alloy
    Zhu, Y; Zeng, W; Sun, Y; Feng, F; Zhou, Y
  • Artificial neural network modeling to evaluate and predict the deformation behavior of stainless steel type AISI 304L during hot torsion
    Mandal, S; Sivaprasad, PV; Venugopal, S; Murthy, KPN
  • Modelling of the rheological behaviour of aluminium alloys in multistep hot deformation using the multiple regression analysis and artificial neural network techniques
    Bruni, C; Forcellese, A; Gabrielli, F; Simoncini, M
  • Prediction of flow curves and forming limit curves of Mg alloy thin sheets using ANN-based models
    Forcellese, A; Gabrielli, F; Simoncini, M
  • Design of an optimized procedure to predict opposite performances in porthole die extrusion
    Ambrogio, G; Gagliardi, F
  • Neural network-based expert system for modeling of tube spinning process
    Vundavilli, PR; Phani Kumar, J; Sai Priyatham, Ch; Parappagoudar, MB
  • Finite element and artificial neural network analysis of ECAP
    Esmailzadeh, M; Aghaie-Khafri, M
  • Prediction of damage evolution in forged aluminium metal matrix composites using a neural network approach
    Roberts, SM; Kusiak, J; Liu, YL; Forcellese, A; Withers, PJ
  • Prediction of bead geometry in pulsed GMA welding using back propagation neural network
    Kanti, KM; Rao, PS
  • Prediction of weld bead geometry and penetration in shielded metal-arc welding using artificial neural networks
    Nagesh, DS; Datta, GL
  • Prediction of laser butt joint welding parameters using back propagation and learning vector quantization networks
    Jeng, JY; Mau, TF; Leu, SM
  • Prediction of gas metal arc welding parameters based on artificial neural networks
    Ates, H
  • Prediction of welding-induced angular distortions in thin butt-welded plates using artificial neural networks
    Choobi, MS; Haghpanahi, M; Sedighi, M
  • RBF-NN-based model for prediction of weld bead geometry in Shielded Metal Arc Welding (SMAW)
    Ahmed, AN; Noor, CWM; Allawi, MF; El-Shafie, A
  • Tool life predictions in milling using spindle power with the neural network technique
    Drouillet, C; Karandikar, J; Nath, C; Journeaux, AC; Mansori, M; Kurfess, T
  • Real time prediction of drilling fluid rheological properties using artificial neural Networks visible mathematical model (white box)
    Elkatatny, S; Tariq, Z; Mahmoud, M
  • Application of artificial neural network modeling for machining parameters optimization in drilling operation
    Deepan Bharathi Kannan, T; Kkannan, GR; Kumar, BS; Baskar, N
  • A genetic algorithm-based artificial neural network model for the optimization of machining processes
    Venkatesan, D; Kannan, K; Saravanan, R
  • Mechanical and microstructural properties prediction by artificial neural networks in FSW processes of dual phase titanium alloys
    Buffa, G; Fratini, L; Micari, F
  • Modelling and Pareto optimization of mechanical properties of friction stir welded AA7075/AA5083 butt joints using neural network and particle swarm algorithm
    Shojaeefard, MH; Behnagh, RA; Akbari, M; Givid, MKB; Farhanie, F
  • Artificial neural network application to the friction stir welding of aluminum plates
    Okuyucu, H; Kurt, A; Arcaklioglu, E
  • Neural network models of peak temperature, torque, traverse force, bending stress and maximum shear stress during friction stir welding
    Manvatkar, VD; Arora, A; De, A; DebRoy, T
  • Mechanical and metallurgical properties of dissimilar AA6061-T6 and AA7277-T6 joint made by FSW technique
    Aliha, MRM; Shahheidari, M; Bisadi, M; Akbari, M; Hossain, S
  • Prediction of tensile strength of friction stir weld joints with adaptive neuro-fuzzy inference system (ANFIS) and neural network
    Dewan, MW; Huggett, DJ; Liao, TW; Wahab, MA; Okeil, AM
  • Investigation of friction stir welding tool parameters using FEM and neural network
    Shojaeefard, MH; Khalkhali, A; Akbari, M; Asadi, P
  • The use of neural network and discrete Fourier transform for real-time evaluation of friction stir welding
    Boldsaikhan, E; Corwin, EM; Logar, AM; Arbegast, WJ
  • Effect of the ω/v ratio and sheet thickness on mechanical properties of magnesium alloy FSWed joints
    Bruni, C; Forcellese, A; Gabrielli, F; Simoncini, M
  • Friction stir welding of magnesium alloys under different process parameters
    Bruni, C; Buffa, G; Fratini, L; Simoncini, M
  • Stopping criteria for ensemble of evolutionary artificial neural networks
    Nguyen, MH; Abbass, HA; McKay, RI
  • A leisurely look at the bootstrap, the jackknife, and crossvalidation
    Efron, B; Gong, G
  • Determination of the influence of processing parameters on the mechanical properties of the Ti–6Al–4V alloy using an artificial neural network
    Sun, Y; Zeng, W; Han, Y; Ma, X; Zhao, Y; Guo, P; Wang, G; Dargusch, MS
  • In-process control of rotational speed in friction stir welding of sheet blanks with variable mechanical properties
    Ciccarelli, D; Mehtedi, M; Forcellese, A; Greco, L; Simoncini, M
  • Statistical learning from a regression perspective. Springer series in statistics
    Berk, RA
  • Encyclopedia of machine learning and data mining
    Zhang, X
  • Support vector regression based flow stress prediction in austenitic stainless steel 304
    Desu, RK; Guntuku, SC; Aditya, B; Gupta, AK

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