Abstract
Flow-related defects in friction stir welding are critical for the joints affecting their mechanical properties and functionality. One way to identify them, avoiding long and sometimes expensive destructive and nondestructive testing, is using machine learning tools with monitored physical quantities as input data. In this work, artificial neural network and decision tree models are trained, validated, and tested on a large dataset consisting of forces, torque, and temperature in the stirred zone measured when friction stir welding three aluminum alloys such as 5083-H111, 6082-T6, and 7075-T6. The built models successfully classified welds between sound and defective with accuracies over 95%, proving their usefulness in identifying defects on new datasets. Independently from the models, the temperature in the stirred zone is found to be the most influential parameter for the assessment of friction stir weld quality.
References
1.
Thomas
, W. M.
, Nicholas
, E. D.
, Needham
, J.
, Murch
, M. G.
, Templesmith
, P.
, and Dawes
, C. J.
, 1991
, “Friction Stir Butt Welding
,” International Patent Application No. PCT/GB92/02203 and GB Patent Application No. 9125978.8, Dec. 1991, U.S. Patent No. 5,460,317,Oct. 1995.2.
Kumar Rajak
, D.
, Pagar
, D. D.
, Menezes
, P. L.
, and Eyvazian
, A.
, 2020
, “Friction-Based Welding Processes: Friction Welding and Friction Stir Welding
,” J. Adhes. Sci. Technol.
, 34
(24
), pp. 2613
–2637
. 3.
Mishra
, R. S.
, and Ma
, Z. Y.
, 2005
, “Friction Stir Welding and Processing
,” Mater. Sci. Eng. R: Rep.
, 50
(1–2
), pp. 1
–78
. 4.
Huang
, Y.
, Meng
, X.
, Xie
, Y.
, Wan
, LongLv
, and Feng
, J.
, 2018
, “Friction Stir Welding/Processing of Polymers and Polymer Matrix Composites
,” Compos. Part A: Appl. Sci. Manuf.
, 105
, pp. 235
–257
. 5.
Lambiase
, F.
, Scipioni
, S. I.
, Lee
, C.-J.
, Ko
, D.-C.
, and Liu
, F.
, 2021
, “A State-of-the-Art Review on Advanced Joining Processes for Metal-Composite and Metal-Polymer Hybrid Structures
,” Materials
, 14
(8
), p. 1890
. 6.
Kashaev
, N.
, Ventzke
, V.
, and Çam
, G.
, 2018
, “Prospects of Laser Beam Welding and Friction Stir Welding Processes for Aluminum Airframe Structural Applications
,” J. Manuf. Process.
, 36
, pp. 571
–600
. 7.
Radisavljevic
, I.
, Zivkovic
, A.
, Radovic
, N.
, and Grabulov
, V.
, 2013
, “Influence of FSW Parameters on Formation Quality and Mechanical Properties of Al 2024-T351 Butt Welded Joints
,” Trans. Nonferrous Metals Soc. China (English Edition)
, 23
(12
), pp. 3525
–3539
. 8.
Hartl
, R.
, Hansjakob
, J.
, and Zaeh
, M. F.
, 2020
, “Improving the Surface Quality of Friction Stir Welds Using Reinforcement Learning and Bayesian Optimization
,” Int. J. Adv. Manuf. Technol.
, 110
(11–12
), pp. 3145
–3167
. 9.
Zeng
, X. H.
, Xue
, P.
, Wang
, D.
, Ni
, D. R.
, Xiao
, B. L.
, Wang
, K. S.
, and Ma
, Z. Y.
, 2018
, “Material Flow and Void Defect Formation in Friction Stir Welding of Aluminium Alloys
,” Sci. Technol. Welding Joining
, 23
(8
), pp. 677
–686
. 10.
Beaudet
, J.
, Rückert
, G.
, and Cortial
, F.
, 2020
, “Fatigue Behavior of FSW High-Yield Strength Steel Welds for Shipbuilding Application
,” Welding World
, 72
(4
), pp. 407
–420
. 11.
Ambrosio
, D.
, Wagner
, V.
, Garnier
, C.
, Jacquin
, D.
, Tongne
, A.
, Fazzini
, M.
, Cahuc
, O.
, and Dessein
, G.
, 2020
, “Influence of Welding Parameters on the Microstructure, Thermal Fields and Defect Formation in AA7075-T6 Friction Stir Welds
,” Welding World
, 64
(5
), pp. 773
–784
. 12.
Wang
, Z. L.
, Zhang
, Z.
, Xue
, P.
, Ni
, D. R.
, Ma
, Z. Y.
, Hao
, Y. F.
, Zhao
, Y. H.
, and Wang
, G. Q.
, 2022
, “Defect Formation, Microstructure Evolution, and Mechanical Properties of Bobbin Tool Friction–Stir Welded 2219-T8 Alloy
,” Mater. Sci. Eng. A.
, 832
, p. 142414
. 13.
Zettler
, R.
, Vugrin
, T.
, and Schmücker
, M.
, 2010
, 9—Effects and Defects of Friction Stir Welds
, Woodhead Publishing
, Sawston, UK
, pp. 245
–276
.14.
Khan
, N. Z.
, Siddiquee
, A. N.
, Khan
, Z. A.
, and Shihab
, S. K.
, 2015
, “Investigations on Tunneling and Kissing Bond Defects in FSW Joints for Dissimilar Aluminum Alloys
,” J. Alloys. Compd.
, 648
, pp. 360
–367
. 15.
Kim
, Y. G.
, Fujii
, H.
, Tsumura
, T.
, Komazaki
, T.
, and Nakata
, K.
, 2006
, “Three Defect Types in Friction Stir Welding of Aluminum Die Casting Alloy
,” Mater. Sci. Eng. A.
, 415
(1–2
), pp. 250
–254
. 16.
Ambrosio
, D.
, Wagner
, V.
, Dessein
, G.
, Paris
, J. Y.
, Jlaiel
, K.
, and Cahuc
, O.
, 2021
, “Plastic Behavior-Dependent Weldability of Heat-Treatable Aluminum Alloys in Friction Stir Welding
,” Int. J. Adv. Manuf. Technol.
, 117
, pp. 635
–652
. 17.
Sudhagar
, S.
, Sakthivel
, M.
, and Ganeshkumar
, P.
, 2019
, “Monitoring of Friction Stir Welding Based on Vision System Coupled With Machine Learning Algorithm
,” Meas.: J. Int. Meas. Confed.
, 144
, pp. 135
–143
. 18.
Rosado
, L. S.
, Santos
, T. G.
, Piedade
, M.
, Ramos
, P. M.
, and Vilaça
, P.
, 2010
, “Advanced Technique for Non-destructive Testing of Friction Stir Welding of Metals
,” Measurement
, 43
(8
), pp. 1021
–1030
. 19.
Ambrosio
, D.
, Tongne
, A.
, Wagner
, V.
, Dessein
, G.
, and Cahuc
, O.
, 2023
, “Towards Material Flow Prediction in Friction Stir Welding Accounting for Mechanisms Governing Chip Formation in Orthogonal Cutting
,” J. Manuf. Proces.
, 85
, pp. 450
–465
. 20.
Guan
, W.
, Li
, D.
, Cui
, L.
, Wang
, D.
, Wu
, S.
, Kang
, S.
, Wang
, J.
, Mao
, L.
, and Zheng
, X.
, 2021
, “Detection of Tunnel Defects in Friction Stir Welded Aluminum Alloy Joints Based on the In-Situ Force Signal
,” J. Manuf. Proces.
, 71
, pp. 1
–11
. 21.
Franke
, D.
, Rudraraju
, S.
, Zinn
, M.
, and Pfefferkorn
, F. E.
, 2020
, “Understanding Process Force Transients With Application Towards Defect Detection During Friction Stir Welding of Aluminum Alloys
,” J. Manuf. Proces.
, 54
, pp. 251
–261
. 22.
Das
, B.
, Pal
, S.
, and Bag
, S.
, 2017
, “Torque Based Defect Detection and Weld Quality Modelling in Friction Stir Welding Process
,” J. Manuf. Proces.
, 27
, pp. 8
–17
. 23.
Das
, B.
, Pal
, S.
, and Swarup
, B.
, 2014
, “Monitoring of Friction Stir Welding Process Through Signals Acquired During the Welding
,” All India Manufacturing Technology Design and Research (AIMTDR)
, Guwahati, India
, Dec. 12–14
, pp. 1
–7
.24.
Balachandar
, K.
, and Jegadeeshwaran
, R.
, 2021
, “Friction Stir Welding Tool Condition Monitoring Using Vibration Signals and Random Forest Algorithm—A Machine Learning Approach
,” Mater. Today: Proc.
, 46
, pp. 1174
–1180
. 25.
Ambrosio
, D.
, Dessein
, G.
, Wagner
, V.
, Yahiaoui
, M.
, Paris
, J. Y.
, Fazzini
, M.
, and Cahuc
, O.
, 2022
, “On the Potential Applications of Acoustic Emission in Friction Stir Welding
,” J. Manuf. Process.
, 75
, pp. 461
–475
. 26.
Ambrosio
, D.
, Aldanondo
, E.
, Wagner
, V.
, Dessein
, G.
, Garnier
, C.
, Vivas
, J.
, and Cahuc
, O.
, 2022
, “A Semi-empirical Model for Peak Temperature Estimation in Friction Stir Welding of Aluminium Alloys
,” Sci. Technol. Welding Joining
, 27
(7
), pp. 1
–10
. 27.
Huggett
, D. J.
, Dewan
, M. W.
, Wahab
, M. A.
, Okeil
, A.
, and Liao
, T. W.
, 2017
, “Phased Array Ultrasonic Testing for Post-Weld and OnLine Detection of Friction Stir Welding Defects
,” Res. Nondestructive Eval.
, 28
(4
), pp. 187
–210
. 28.
Doude
, H.
, Schneider
, J.
, Patton
, B.
, Stafford
, S.
, Waters
, T.
, and Varner
, C.
, 2015
, “Optimizing Weld Quality of a Friction Stir Welded Aluminum Alloy
,” J. Mater. Process. Technol.
, 222
, pp. 188
–196
. 29.
Chen
, C.
, Kovacevic
, R.
, and Jandgric
, D.
, 2003
, “Wavelet Transform Analysis of Acoustic Emission in Monitoring Friction Stir Welding of 6061 Aluminum
,” Int. J. Mach. Tools. Manuf.
, 43
(13
), pp. 1383
–1390
. 30.
Eren
, B.
, Guvenc
, M. A.
, and Mistikoglu
, S.
, 2021
, “Artificial Intelligence Applications for Friction Stir Welding: A Review
,” Metals Mater. Int.
, 27
(2
), pp. 193
–219
. 31.
Shanavas
, S.
, and Dhas
, J. E. R.
, 2018
, “Quality Prediction of Friction Stir Weld Joints on Aa 5052 H32 Aluminium Alloy Using Fuzzy Logic Technique
,” Mater. Today: Proc.
, 5
(5
), pp. 12124
–12132
. 32.
Huggett
, D. J.
, Liao
, T. W.
, Wahab
, M. A.
, and Okeil
, A.
, 2018
, “Prediction of Friction Stir Weld Quality Without and With Signal Features
,” Int. J. Adv. Manuf. Technol.
, 95
(5–8
), pp. 1989
–2003
. 33.
Du
, Y.
, Mukherjee
, T.
, and DebRoy
, T.
, 2019
, “Conditions for Void Formation in Friction Stir Welding From Machine Learning
,” npj Comput. Mater.
, 5
(1
), pp. 1
–8
. 34.
Hartl
, R.
, Bachmann
, A.
, Habedank
, J. B.
, Semm
, T.
, and Zaeh
, M. F.
, 2021
, “Process Monitoring in Friction Stir Welding Using Convolutional Neural Networks
,” Metals
, 11
(4
), p. 535
. 35.
Nadeau
, F.
, Thériault
, B.
, and Gagné
, M. O.
, 2020
, “Machine Learning Models Applied to Friction Stir Welding Defect Index Using Multiple Joint Configurations and Alloys
,” Proc. Inst. Mech. Eng. L P I Mech Eng L-J. Mat
, 234
(5
), pp. 752
–765
. 36.
Verma
, S.
, Gupta
, M.
, and Misra
, J. P.
, 2018
, “Performance Evaluation of Friction Stir Welding Using Machine Learning Approaches
,” MethodsX
, 5
, pp. 1048
–1058
. 37.
Rajiv
, M. W. M.
, and Mishra
, S.
, 2007
, “Friction Stir Welding and Processing
,” Mater. Sci. Eng.: R: Rep.
, 50
(1–2
), pp. 1
–78
.38.
Kumar
, K.
, and Kailas
, S. V.
, 2008
, “The Role of Friction Stir Welding Tool on Material Flow and Weld Formation
,” Mater. Sci. Eng. A.
, 485
(1–2
), pp. 367
–374
. 39.
Cao
, X.
, and Jahazi
, M.
, 2009
, “Effect of Welding Speed on the Quality of Friction Stir Welded Butt Joints of a Magnesium Alloy
,” Mater. Des.
, 30
(6
), pp. 2033
–2042
. 40.
Rajakumar
, S.
, Muralidharan
, C.
, and Balasubramanian
, V.
, 2011
, “Influence of Friction Stir Welding Process and Tool Parameters on Strength Properties of AA7075-T6 Aluminium Alloy Joints
,” Mater. Des.
, 32
(2
), pp. 535
–549
. 41.
Lombard
, H.
, Hattingh
, D.
, Steuwer
, A.
, and James
, M.
, 2008
, “Optimising FSW Process Parameters to Minimise Defects and Maximise Fatigue Life in 5083-h321 Aluminium Alloy
,” Eng. Fract. Mech.
, 75
(3
), pp. 341
–354
. 42.
Imam
, M.
, Biswas
, K.
, and Racherla
, V.
, 2013
, “On Use of Weld Zone Temperatures for Online Monitoring of Weld Quality in Friction Stir Welding of Naturally Aged Aluminium Alloys
,” Mater. Des.
, 52
, pp. 730
–739
. 43.
Salari
, E.
, Jahazi
, M.
, Khodabandeh
, A.
, and Ghasemi-Nanesa
, H.
, 2014
, “Influence of Tool Geometry and Rotational Speed on Mechanical Properties and Defect Formation in Friction Stir Lap Welded 5456 Aluminum Alloy Sheets
,” Mater. Des.
, 58
, pp. 381
–389
. 44.
Upadhyay
, P.
, and Reynolds
, A.
, 2012
, “Effects of Forge Axis Force and Backing Plate Thermal Diffusivity on FSW of Aa6056
,” Mater. Sci. Eng. A.
, 558
, pp. 394
–402
. 45.
Mendes
, N.
, Neto
, P.
, Loureiro
, A.
, and Moreira
, A. P.
, 2016
, “Machines and Control Systems for Friction Stir Welding: A Review
,” Mater. Des.
, 90
, pp. 256
–265
. 46.
Gajowniczek
, K.
, Liang
, Y.
, Friedman
, T.
, and Zabkowski
, T.
, 2020
, “Semantic and Generalized Entropy Loss Functions for Semi-supervised Deep Learning
,” Entropy
, 22
(3
), p. 334
. 47.
Jenq
, J. J. F.
, and Li
, W.
, 1998
, “Feedforward Backpropagation Artificial Neural Networks on Reconfigurable Meshes
,” Future Generation Comput. Syst.
, 14
(5–6
), pp. 313
–319
. 48.
Apicella
, A.
, Donnarumma
, F.
, Isgrò
, F.
, and Prevete
, R.
, 2021
, “A Survey on Modern Trainable Activation Functions
,” Neural Netw.
, 138
, pp. 14
–32
. 49.
Ding
, B.
, Qian
, H.
, and Zhou
, J.
, 2018
, “Activation Functions and Their Characteristics in Deep Neural Networks
,” 2018 Chinese Control And Decision Conference (CCDC)
, Shenyang, China
, June 9–11
, pp. 1836
–1841
.50.
Liao
, S.
, Jiang
, X.
, and Ge
, Z.
, 2022
, “Weakly Supervised Multilayer Perceptron for Industrial Fault Classification With Inaccurate and Incomplete Labels
,” Trans. Autom. Sci. Eng.
, 19
(2
), pp. 1192
–1201
. 51.
Vijayalakshmi
, K.
, Vijayakumar
, K.
, and Nandhakumar
, K.
, 2022
, “Prediction of Virtual Energy Storage Capacity of the Air-Conditioner Using a Stochastic Gradient Descent Based Artificial Neural Network
,” Electric Power Syst. Res.
, 208
, p. 107879
. 52.
Smith
, S. L.
, and Le
, Q. V.
, 2018
, “A Bayesian Perspective on Generalization and Stochastic Gradient Descent
,” 6th International Conference on Learning Representations, ICLR 2018
, Vancouver Convention Center, Vancouver, BC, Canada
, Apr. 30–May 3
, pp. 1
–13
.53.
Moreira
, M.
, and Fiesler
, E.
, 1995
, “Neural Networks with Adaptive Learning Rate and Momentum Terms
,” Technique Report 95-04, IDIAP Annual Reports, pp. 1
–29
.54.
Olden
, J. D.
, Joy
, M. K.
, and Death
, R. G.
, 2004
, “An Accurate Comparison of Methods for Quantifying Variable Importance in Artificial Neural Networks Using Simulated Data
,” Ecol. Modell.
, 178
(3–4
), pp. 389
–397
. 55.
Arbegast
, W. J.
, and Hartley
, P. J.
, 1998
, “Friction Stir Weld Technology Development at Lockheed Martin Michoud Space System—An Overview
,” 5th International Conference on Trends in Welding Research
, Pine Mountain, GA
, June 1–5
.56.
Roy
, G. G.
, Nandan
, R.
, and DebRoy
, T.
, 2006
, “Dimensionless Correlation to Estimate Peak Temperature During Friction Stir Welding
,” Sci. Technol. Welding Join.
, 11
(5
), pp. 606
–608
. 57.
Ambrosio
, D.
, Wagner
, V.
, Dessein
, G.
, Tongne
, A.
, Fazzini
, M.
, Garnier
, C.
, and Cahuc
, O.
, 2022
, “Power-Based Model for Temperature Prediction in FSW
,” J. Phys.: Conference Ser.
, 2287
(1
), p. 012025
. 58.
Kim
, A.
, Oh
, K.
, Jung
, J. Y.
, and Kim
, B.
, 2018
, “Imbalanced Classification of Manufacturing Quality Conditions Using Cost-Sensitive Decision Tree Ensembles
,” Int. J. Comput. Integr. Manuf.
, 31
(8
), pp. 701
–717
. 59.
Yeh
, D. Y.
, Cheng
, C. H.
, and Hsiao
, S. C.
, 2011
, “Classification Knowledge Discovery in Mold Tooling Test Using Decision Tree Algorithm
,” J. Intell. Manuf.
, 22
(4
), pp. 585
–595
. 60.
Zhao
, Y.
, and Zhang
, Y.
, 2008
, “Comparison of Decision Tree Methods for Finding Active Objects
,” Adv. Space. Res.
, 41
(12
), pp. 1955
–1959
. 61.
Navada
, A.
, Ansari
, A. N.
, Patil
, S.
, and Sonkamble
, B. A.
, 2011
, “Overview of Use of Decision Tree Algorithms in Machine Learning
,” 2011 IEEE Control and System Graduate Research Colloquium
, Shah Alam, Malaysia
, June 27–28
, pp. 37
–42
.62.
Kim
, K.
, 2016
, “A Hybrid Classification Algorithm by Subspace Partitioning Through Semi-supervised Decision Tree
,” Pattern Recogn.
, 60
, pp. 157
–163
. 63.
Primartha
, R.
, and Tama
, B. A.
, 2017
, “Anomaly Detection Using Random Forest: A Performance Revisited
,” 2017 International Conference on Data and Software Engineering (ICoDSE)
, Palembang, Indonesia
, Nov. 1–2
, pp. 1
–6
.64.
Mohamed
, W. N. H. W.
, Salleh
, M. N. M.
, and Omar
, A. H.
, 2012
, “A Comparative Study of Reduced Error Pruning Method in Decision Tree Algorithms
,” 2012 IEEE International Conference on Control System, Computing and Engineering
, Penang, Malaysia
, Nov. 23–25
, pp. 392
–397
.Copyright © 2023 by ASME
You do not currently have access to this content.
