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Australian Journal of Mechanical Engineering Volume 20, 2022 - Issue 4
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Research Article

Application of artificial neural network in predicting the wear rate of copper surface composites produced using friction stir processing

I. Dinaharana IDM-Joint Lab, Department of Mechanical Engineering, Tsinghua University, Beijing, ChinaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0001-9416-4038View further author information
ORCID Icon,
R. Palanivelb Department of Mechanical Engineering, Shaqra University, Riyadh, Saudi ArabiaORCID Iconhttps://orcid.org/0000-0003-0021-2559View further author information
ORCID Icon,
N. Muruganc Department of Robotics and Automation Engineering, PSG College of Technology, Coimbatore, IndiaORCID Iconhttps://orcid.org/0000-0001-7647-0672View further author information
ORCID Icon &
R. F. Laubscherd Department of Mechanical Engineering Science, University of Johannesburg, Auckland Park Kingsway Campus, Johannesburg, South AfricaORCID Iconhttps://orcid.org/0000-0002-0526-3163View further author information
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Pages 1079-1090 | Received 12 Mar 2020, Accepted 07 May 2020, Published online: 31 May 2020

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Prashant K. Ambadekar, Sarita Ambadekar, C. M. Choudhari, Satish A. Patil & S.H. Gawande. (2023) Artificial intelligence and its relevance in mechanical engineering from Industry 4.0 perspective. Australian Journal of Mechanical Engineering 0:0, pages 1-21.
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Articles from other publishers (7)

Uma Maheshwera Reddy Paturi, Suryapavan Cheruku & N. S. Reddy. (2022) The Role of Artificial Neural Networks in Prediction of Mechanical and Tribological Properties of Composites—A Comprehensive Review. Archives of Computational Methods in Engineering 29:5, pages 3109-3149.
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S. Sudhagar & P. M. Gopal. (2021) Investigation on Mechanical and Tribological Characteristics Cu/Si3N4 Surface Composite Developed Through Friction Stir Processing. Silicon 14:8, pages 4207-4216.
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Xinxin Li, Haipeng Wang, Bing Wang & Yingchun Guan. (2022) Machine learning methods for prediction analyses of 4H–SiC microfabrication via femtosecond laser processing. Journal of Materials Research and Technology 18, pages 2152-2165.
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Vahid M Khojastehnezhad, Hamed H Pourasl & Arian Bahrami. (2021) Estimation of mechanical properties of friction stir processed Al 6061/Al 2 O 3 -Tib 2 hybrid metal matrix composite layer via artificial neural network and response surface methodology . Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications 235:12, pages 2720-2736.
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Ahmed B. Khoshaim, Essam B. Moustafa, Omar Talal Bafakeeh & Ammar H. Elsheikh. (2021) An Optimized Multilayer Perceptrons Model Using Grey Wolf Optimizer to Predict Mechanical and Microstructural Properties of Friction Stir Processed Aluminum Alloy Reinforced by Nanoparticles. Coatings 11:12, pages 1476.
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Roman Hartl, Andreas Bachmann, Jan Bernd Habedank, Thomas Semm & Michael F. Zaeh. (2021) Process Monitoring in Friction Stir Welding Using Convolutional Neural Networks. Metals 11:4, pages 535.
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Mohammad Azad Alam, Hamdan H. Ya, Mohammad Azeem, Patthi Bin Hussain, Mohd Sapuan bin Salit, Rehan Khan, Sajjad Arif & Akhter Husain Ansari. (2020) Modelling and optimisation of hardness behaviour of sintered Al/SiC composites using RSM and ANN: a comparative study. Journal of Materials Research and Technology 9:6, pages 14036-14050.
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