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Research Papers

Ultrasonic Welding of Magnesium–Titanium Dissimilar Metals: A Study on Influences of Welding Parameters on Mechanical Property by Experimentation and Artificial Neural Network

[+] Author and Article Information
Dewang Zhao

State Key Laboratory of Structural
Analysis for Industrial Equipment,
Department of Engineering Mechanics,
Dalian University of Technology,
2 Ling Gong Road,
Dalian 116024, China
e-mail: dewangzhao@yahoo.com

Kunmin Zhao

School of Automotive Engineering,
Dalian University of Technology,
Dalian 116024, China;
Institute of Industrial and Equipment Technology,
Hefei University of Technology,
Hefei 230601, China
e-mail: kmzhao@dlut.edu.cn

Daxin Ren

Key Laboratory of Liaoning Advanced
Welding and Joining Technology,
School of Automotive Engineering,
Dalian University of Technology,
2 Ling Gong Road,
Dalian 116024, China
e-mail: rendx@dlut.edu.cn

Xinglin Guo

State Key Laboratory of Structural
Analysis for Industrial Equipment,
Department of Engineering Mechanics,
Dalian University of Technology,
2 Ling Gong Road,
Dalian 116024, China
e-mail: xlguo@dlut.edu.cn

1Corresponding author.

Manuscript received June 6, 2016; final manuscript received December 12, 2016; published online January 27, 2017. Assoc. Editor: Wayne Cai.

J. Manuf. Sci. Eng 139(3), 031019 (Jan 27, 2017) (9 pages) Paper No: MANU-16-1321; doi: 10.1115/1.4035539 History: Received June 06, 2016; Revised December 12, 2016

The advancement in the application of light alloys such as magnesium and titanium is closely related to the state of the art of joining them. As a new type of solid-phase welding, ultrasonic spot welding is an effective way to achieve joints of high strength. In this paper, ultrasonic welding was carried out on magnesium–titanium dissimilar alloys to investigate the influences of welding parameters on joint strength. The analysis of variance was adopted to study the weight of each welding parameter and their interactions. The artificial neural network (ANN) was used to predict joint strength. Results show that in ultrasonic welding of magnesium and titanium alloys, clamping force is the most significant factor, followed by vibration time and vibration amplitude; the interactions between vibration time and vibration amplitude, and between vibration amplitude and clamping force also significantly impact the strength. By using the artificial neural network, test data were trained to obtain a high precision network, which was used to predict the variations of joint strength under different parameters. The analytical model predicts that with the increase in vibration time, the increase in optimal joint strength is limited, but the range of welding parameters to obtain a higher joint strength increases significantly; the minimum joint strength increases as well; and the optimal vibration amplitude expands gradually and reaches the maximum when the vibration time is 1000 ms, then shifts toward the low end gradually.

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References

Li, H. , Choi, H. , Ma, C. , Zhao, J. , Jiang, H. , Cai, W. , Abell, J. A. , and Li, X. , 2013, “ Transient Temperature and Heat Flux Measurement in Ultrasonic Joining of Battery Tabs Using Thin-Film Microsensors,” ASME J. Manuf. Sci. Eng., 135(5), p. 051015. [CrossRef]
Ni, Z. , Zhao, H. , Mi, P. , and Ye, F. , 2016, “ Microstructure and Mechanical Performances of Ultrasonic Spot Welded Al/Cu Joints With Al 2219 Alloy Particle Interlayer,” Mater. Des., 92, pp. 779–786.
Patel, V. K. , Bhole, S. D. , Chen, D. L. , Ni, D. R. , Xiao, B. L. , and Ma, Z. Y. , 2015, “ Solid-State Ultrasonic Spot Welding of SiCp/2009Al Composite Sheets,” Mater. Des., 65, pp. 489–495. [CrossRef]
Haddadi, F. , and Abu-Farha, F. , 2016, “ The Effect of Interface Reaction on Vibration Evolution and Performance of Aluminium to Steel High Power Ultrasonic Spot Joints,” Mater. Des., 89, pp. 50–57.
Chen, K. , and Zhang, Y. , 2015, “ Mechanical Analysis of Ultrasonic Welding Considering Knurl Pattern of Sonotrode Tip,” Mater. Des., 87, pp. 393–404.
Xi, L. , Banu, M. , Hu, S. J. , Cai, W. , and Abell, J. , 2016, “ Performance Prediction for Ultrasonically Welded Dissimilar Materials Joints,” ASME J. Manuf. Sci. Eng., 139(1), p. 011008. [CrossRef]
Lee, S. S. , Kim, T. H. , Hu, J. S. , Cai, W. W. , and Abell, J. A. , 2015, “ Analysis of Weld Formation in Multilayer Ultrasonic Metal Welding Using High-Speed Images,” ASME J. Manuf. Sci. Eng., 137(3), p. 031016. [CrossRef]
Lee, D. , Kannatey-Asibu, E. , and Cai, W. , 2013, “ Ultrasonic Welding Simulations for Multiple Layers of Lithium-Ion Battery Tabs,” ASME J. Manuf. Sci. Eng., 135(6), p. 061011. [CrossRef]
Xu, L. , Wang, L. , Chen, Y. C. , Robson, J. D. , and Prangnell, P. B. , 2016, “ Effect of Interfacial Reaction on the Mechanical Performance of Steel to Aluminum Dissimilar Ultrasonic Spot Welds,” Metall. Mater. Trans. A, 47(1), pp. 334–346. [CrossRef]
Patel, V. K. , Bhole, S. D. , and Chen, D. L. , 2014, “ Fatigue Life Estimation of Ultrasonic Spot Welded Mg Alloy Joints,” Mater. Des., 62, pp. 124–132. [CrossRef]
Shakil, M. , Tariq, N. H. , Ahmad, M. , Choudhary, M. A. , Akhter, J. I. , and Babu, S. S. , 2014, “ Effect of Ultrasonic Welding Parameters on Microstructure and Mechanical Properties of Dissimilar Joints,” Mater. Des., 55, pp. 263–273. [CrossRef]
Patel, V. K. , Bhole, S. D. , and Chen, D. L. , 2014, “ Ultrasonic Spot Welding of Aluminum to High-Strength Low-Alloy Steel: Microstructure, Tensile and Fatigue Properties,” Metall. Mater. Trans. A, 45(4), pp. 2055–2066. [CrossRef]
Lee, S. S. , Kim, T. H. , Hu, J. S. , Cai, W. W. , Abell, J. A. , and Li, J. , 2013, “ Characterization of Joint Quality in Ultrasonic Welding of Battery Tabs,” ASME J. Manuf. Sci. Eng., 135(2), p. 021004. [CrossRef]
Santella, M. , Franklin, T. J. , Pan, J. , Pan, T.-Y. , and Brown, E. , 2010, “ Ultrasonic Spot Welding of AZ31B to Galvanized Mild Steel,” SAE Int. J. Mater. Manuf., 3, pp. 652–657.
Kang, B. , Cai, W. , and Tan, C.-A. , 2013, “ Dynamic Response of Battery Tabs Under Ultrasonic Welding,” ASME J. Manuf. Sci. Eng., 135(5), p. 051013. [CrossRef]
Kang, B. , Cai, W. , and Tan, C.-A. , 2014, “ Vibrational Energy Loss Analysis in Battery Tab Ultrasonic Welding,” J. Manuf. Process., 16(2), pp. 218–232. [CrossRef]
Shawn Lee, S. , Shao, C. , Hyung Kim, T. , Jack Hu, S. , Kannatey-Asibu, E. , Cai, W. W. , Patrick Spicer, J. , and Abell, J. A. , 2014, “ Characterization of Ultrasonic Metal Welding by Correlating Online Sensor Signals With Weld Attributes,” ASME J. Manuf. Sci. Eng., 136(5), p. 051019. [CrossRef]
Wang, L. , Wang, Y. , Prangnell, P. , and Robson, J. , 2015, “ Modeling of Intermetallic Compounds Growth Between Dissimilar Metals,” Metall. Mater. Trans. A, 46(9), pp. 4106–4114. [CrossRef]
Shin, H. S. , and De Leon, M. , 2015, “ Parametric Study in Similar Ultrasonic Spot Welding of A5052-H32 Alloy Sheets,” J. Mater. Process. Technol., 224, pp. 222–232. [CrossRef]
Patel, V. K. , Bhole, S. D. , and Chen, D. L. , 2013, “ Ultrasonic Spot Welded AZ31 Magnesium Alloy: Microstructure, Texture, and Lap Shear Strength,” Mater. Sci. Eng. A, 569, pp. 78–85. [CrossRef]
Santella, M. , Brown, E. , Pozuelo, M. , Pan, T.-Y. , and Yang, J.-M. , 2012, “ Details of Mg–Zn Reactions in AZ31 to Galvanised Mild Steel Ultrasonic Spot Welds,” Sci. Technol. Weld. Joining, 17(3), pp. 219–224. [CrossRef]
Wu, X. , Liu, T. , and Cai, W. , 2015, “ Microstructure, Welding Mechanism, and Failure of Al/Cu Ultrasonic Welds,” J. Manuf. Process., 20, pp. 515–524. [CrossRef]
Zhang, C. Q. , Robson, J. D. , Ciuca, O. , and Prangnell, P. B. , 2014, “ Microstructural Characterization and Mechanical Properties of High Power Ultrasonic Spot Welded Aluminum Alloy AA6111-TiAl6V4 Dissimilar Joints,” Mater. Charact., 97, pp. 83–91. [CrossRef]
Lai, W. J. , and Pan, J. , 2014, “ Stress Intensity Factor Solutions for Adhesive-Bonded Lap-Shear Specimens of Magnesium and Steel Sheets With and Without Kinked Cracks for Fatigue Life Estimations,” Eng. Fract. Mech., 131, pp. 454–470. [CrossRef]
Jedrasiak, P. , Shercliff, H. R. , Chen, Y. C. , Wang, L. , Prangnell, P. , and Robson, J. , 2015, “ Modeling of the Thermal Field in Dissimilar Alloy Ultrasonic Welding,” J. Mater. Eng. Perform., 24(2), pp. 799–807. [CrossRef]
Carboni, M. , and Annoni, M. , 2011, “ Ultrasonic Metal Welding of AA 6022–T4 Lap Joints—Part II: Fatigue Behaviour, Failure Analysis and Modelling,” Sci. Technol. Weld. Joining, 16(2), pp. 116–125. [CrossRef]
Gao, M. , Wang, Z. M. , Yan, J. , and Zeng, X. Y. , 2011, “ Dissimilar Ti/Mg Alloy Butt Welding by Fibre Laser With Mg Filler Wire–Preliminary Study,” Sci. Technol. Weld. Joining, 16(6), pp. 488–496. [CrossRef]
Aonuma, M. , and Nakata, K. , 2010, “ Effect of Calcium on Intermetallic Compound Layer at Interface of Calcium Added Magnesium-Aluminum Alloy and Titanium Joint by Friction Stir Welding,” Mater. Sci. Eng. B, 173(1–3), pp. 135–138. [CrossRef]
Aonuma, M. , and Nakata, K. , 2009, “ Effect of Alloying Elements on Interface Microstructure of Mg-Al-Zn Magnesium Alloys and Titanium Joint by Friction Stir Welding,” Mater. Sci. Eng. B, 161(1–3), pp. 46–49. [CrossRef]
Cao, R. , Wang, T. , Wang, C. , Feng, Z. , Lin, Q. , and Chen, J. H. , 2014, “ Cold Metal Transfer Welding–Brazing of Pure Titanium TA2 to Magnesium Alloy AZ31B,” J. Alloys Compd., 605, pp. 12–20. [CrossRef]
Gao, M. , Wang, Z. M. , Li, X. Y. , and Zeng, X. Y. , 2012, “ Laser Keyhole Welding of Dissimilar Ti-6Al-4V Titanium Alloy to AZ31B Magnesium Alloy,” Metall. Mater. Trans. A, 43(1), pp. 163–172. [CrossRef]
Aonuma, M. , and Nakata, K. , 2012, “ Dissimilar Metal Joining of ZK60 Magnesium Alloy and Titanium by Friction Stir Welding,” Mater. Sci. Eng. B, 177(7), pp. 543–548. [CrossRef]
Ren, D. , Zhao, K. , Pan, M. , Chang, Y. , Gang, S. , and Zhao, D. , 2017, “ Ultrasonic Spot Welding of Magnesium Alloy to Titanium Alloy,” Scr. Mater., 126, pp. 58–62. [CrossRef]
Hornik, K. , 1991, “ Approximation Capabilities of Multilayer Feedforward Networks,” Neural Networks, 4(2), pp. 251–257. [CrossRef]
Martín, Ó. , De Tiedra, P. , López, M. , San-Juan, M. , García, C. , Martín, F. , and Blanco, Y. , 2009, “ Quality Prediction of Resistance Spot Welding Joints of 304 Austenitic Stainless Steel,” Mater. Des., 30(1), pp. 68–77. [CrossRef]
Norouzi, A. , Hamedi, M. , and Adineh, V. R. , 2012, “ Strength Modeling and Optimizing Ultrasonic Welded Parts of ABS-PMMA Using Artificial Intelligence Methods,” Int. J. Adv. Manuf. Technol., 61(1–4), pp. 135–147. [CrossRef]
Sadeghi, B. H. M. , 2000, “ BP-Neural Network Predictor Model for Plastic Injection Molding Process,” J. Mater. Process. Technol., 103(3), pp. 411–416. [CrossRef]
Benyelloul, K. , and Aourag, H. , 2013, “ Bulk Modulus Prediction of Austenitic Stainless Steel Using a Hybrid GA-ANN as a Data Mining Tools,” Comput. Mater. Sci., 77, pp. 330–334. [CrossRef]
Huang, M. , Han, W. , Wan, J. , Ma, Y. , and Chen, X. , 2016, “ Multi-Objective Optimisation for Design and Operation of Anaerobic Digestion Using GA-ANN and NSGA-II,” J. Chem. Technol. Biotechnol., 91(1), pp. 226–233. [CrossRef]
Yu, J. B. , Yu, Y. , Wang, L. N. , Yuan, Z. , and Ji, X. , 2014, “ The Knowledge Modeling System of Ready-Mixed Concrete Enterprise and Artificial Intelligence With ANN-GA for Manufacturing Production,” J. Intell. Manuf., 27(4), pp. 905–914. [CrossRef]
Trenn, S. , 2008, “ Multilayer Perceptrons: Approximation Order and Necessary Number of Hidden Units,” IEEE Trans. Neural Networks, 19(5), pp. 836–844. [CrossRef]
Atik, K. , Aktaş, A. , and Deniz, E. , 2010, “ Performance Parameters Estimation of MAC by Using Artificial Neural Network,” Expert Syst. Appl., 37(7), pp. 5436–5442. [CrossRef]
Sahin, I. , and Koyuncu, I. , 2012, “ Design and Implementation of Neural Networks Neurons With Radbas, Logsig, and Tansig Activation Functions on FPGA,” Elektron. Elektrotech., 120(4), pp. 51–54.
Levenberg, K. , and Levenberg, K. , 1944, “ A Method for the Solution of Certain Problems in Least Squares,” Q. Appl. Math., 2(2), pp. 164–168. [CrossRef]

Figures

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Fig. 1

Schematic diagram of ultrasonic welding and specimen dimension

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Fig. 2

Approximate weldment for tensile-shear test

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Fig. 3

The shape of joint fracture

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Fig. 4

Three-dimensional plot of joint strength

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Fig. 5

Main effect plots and interaction plot for tensile strength

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Fig. 6

Flow chart for the GA-ANN algorithm

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Fig. 7

Network topology diagram

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Fig. 8

Analysis of regression between experimental values and predicted values

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Fig. 9

The residual analysis result

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Fig. 10

The distribution of joint strength under different vibration time: (a) change in joint strength under vibration time of 600 ms, (b) change in joint strength under vibration time of 800 ms, (c) change in joint strength under vibration time of 1000 ms, and (d) change in joint strength under vibration time of 1300 ms

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