Deep Convolutional Neural Network Algorithm for Prediction of the Mechanical Properties of Friction Stir Welded Copper Joints from its Microstructures
Main Article Content
Abstract
Convolutional Neural Network (CNN) is a special type of Artificial Neural Network which takes input in the form of an image. Like Artificial Neural Network they consist of weights that are estimated during training, neurons (activation functions), and an objective (loss function). CNN is finding various applications in image recognition, semantic segmentation, object detection, and localization. The present work deals with the prediction of the welding efficiency of the Friction Stir Welded joints on the basis of microstructure images by carrying out training on 3000 microstructure images and further testing on 300 microstructure images. The obtained results showed an accuracy of 80 % on the validation dataset.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Creative Commons CC BY 4.0 https://creativecommons.org/licenses/by/4.0/
Welding Technology Review (WTR) articles are published open access under a CC BY licence (Creative Commons Attribution 4.0 International licence). The CC BY licence is the most open licence available and considered the industry 'gold standard' for open access; it is also preferred by many funders. This licence allows readers to copy and redistribute the material in any medium or format, and to alter, transform, or build upon the material, including for commercial use, providing the original author is credited.
References
Petrou, M.; García Sevilla, P. Image Processing: Dealing with Texture; 2006; ISBN 9780470026281.
Aaron, J.; Chew, T.L. A guide to accurate reporting in digital image processing – Can anyone reproduce your quantitative analysis? J. Cell Sci. 2021, doi:10.1242/jcs.254151.
Monga, V.; Li, Y.; Eldar, Y.C. Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing. IEEE Signal Process. Mag. 2021, 38, 18–44, doi:10.1109/MSP.2020.3016905.
Saxe, A.; Nelli, S.; Summerfield, C. If deep learning is the answer, what is the question? Nat. Rev. Neurosci. 2021, doi:10.1038/s41583-020-00395-8.
Janiesch, C.; Zschech, P.; Heinrich, K. Machine learning and deep learning. Electron. Mark. 2021, 31, 685–695, doi:10.1007/s12525-021-00475-2.
Bhalgat, Y.; Zhang, Y.; Lin, J.M.; Porikli, F. Structured convolutions for efficient neural network design. Adv. Neural Inf. Process. Syst. 2020.
Chen, W.; Jiang, M.; Zhang, W.G.; Chen, Z. A novel graph convolutional feature based convolutional neural network for stock trend prediction. Inf. Sci. (Ny). 2021, 556, 67–94, doi:10.1016/j.ins.2020.12.068.
Hartl, R.; Bachmann, A.; Habedank, J.B.; Semm, T.; Zaeh, M.F. Process monitoring in friction stir welding using convolutional neural networks. Metals (Basel). 2021, 11, 535, doi:10.3390/met11040535.
Hartl, R.; Landgraf, J.; Spahl, J.; Bachmann, A.; Zaeh, M.F. Automated visual inspection of friction stir welds: a deep learning approach.; 2019.
Du, Y.; Mukherjee, T.; DebRoy, T. Conditions for void formation in friction stir welding from machine learning. npj Comput Mater 2019, 5, 68, doi:10.1038/s41524-019-0207-y.
Chadha, U.; Selvaraj, S.K.; Gunreddy, N.; Sanjay Babu, S.; Mishra, S.; Padala, D.; Shashank, M.; Mathew, R.M.; Kishore, S.R.; Panigrahi, S.; et al. A Survey of Machine Learning in Friction Stir Welding, including Unresolved Issues and Future Research Directions. Mater. Des. Process. Commun. 2022, doi:10.1155/2022/2568347.
Morawiński, Ł.; Jasiński, C.; Ciemiorek, M.; Chmielewski, T.; Olejnik, L.; Lewandowska, M. Solid-state welding of ultrafine grained copper rods. Arch. Civ. Mech. Eng. 2021, 21, 89, doi:10.1007/s43452-021-00244-0.
Mishra, A.; Pathak, T. Estimation of Grain Size Distribution of Friction Stir Welded Joint by using Machine Learning Approach. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 2020, 10, 99–110, doi:10.14201/adcaij202110199110.
R.S. Mishra; Z.Y. Ma Friction Stir Welding and Processing II Article in Materials Science and Engineering R Reports · September 2005. Mater. Sci. Eng. R 2014.
Rai, R.; De, A.; Bhadeshia, H.K.D.H.; DebRoy, T. Review: Friction stir welding tools. Sci. Technol. Weld. Join. 2011,16, 325–342, doi:10.1179/1362171811Y.0000000023.
Farias, A.; Batalha, G.F.; Prados, E.F.; Magnabosco, R.; Delijaicov, S. Tool wear evaluations in friction stir processing of commercial titanium Ti-6Al-4V. Wear 2013, 302, 1327–1333, doi:10.1016/j.wear.2012.10.025.
Thomas, W.M.; Johnson, K.I.; Wiesner, C.S. Friction stir welding-recent developments in tool and process technologies. Adv. Eng. Mater. 2003, 5, 485–490, doi:10.1002/adem.200300355.
Arun, R.; Kumar, P.; Shimpi, R. Materials Today : Proceedings Friction stir welding parameters and application : A review. Mater. Today Proc. 2019.
Mishra, A. Friction Stir Welding of Dissimilar Metal: A Review. Int. J. Res. Appl. Sci. Eng. Technol. 2018, 6, 1551–1559, doi:10.22214/ijraset.2018.1237.
Mishra, A.; Dixit, D. Friction Stir Welding of Aerospace Alloys. J. Mech. Eng. 2019, 6, 1551–1559, doi:10.3329/jme.v48i1.41093.
Sakthivel, T.; Mukhopadhyay, J. Microstructure and mechanical properties of friction stir welded copper. J. Mater. Sci. 2007, 42, 8126–8129, doi:10.1007/s10853-007-1666-y.
Lee, W.B.; Jung, S.B. The joint properties of copper by friction stir welding. Mater. Lett. 2004, 58, 1041–1046, doi:10.1016/j.matlet.2003.08.014.
Mironov, S.; Inagaki, K.; Sato, Y.S.; Kokawa, H. Microstructural evolution of pure copper during friction-stir welding. Philos. Mag. 2015, 95, 367–381, doi:10.1080/14786435.2015.1006293.
Shah, P.H.; Badheka, V.J. Friction stir welding of aluminium alloys: An overview of experimental findings – process, variables, development and applications. Proc. Inst. Mech. Eng. Part L J. Mater. Des. Appl. 2019.
Sun, Y.F.; Fujii, H. Investigation of the welding parameter dependent microstructure and mechanical properties of friction stir welded pure copper. Mater. Sci. Eng. A 2010, 527, 6879–6886, doi:10.1016/j.msea.2010.07.030.
Shen, J.J.; Liu, H.J.; Cui, F. Effect of welding speed on microstructure and mechanical properties of friction stir welded copper. Mater. Des. 2010, 31, 3937–3942, doi:10.1016/j.matdes.2010.03.027.
Kumar, A.; Raju, L.S. Influence of tool pin profiles on friction stir welding of copper. Mater. Manuf. Process. 2012, 27, 1414–1418, doi:10.1080/10426914.2012.689455.
Xie, G.M.; Ma, Z.Y.; Geng, L. Development of a fine-grained microstructure and the properties of a nugget zone in friction stir welded pure copper. Scr. Mater. 2007, 57, 73–76, doi:10.1016/j.scriptamat.2007.03.048.