Milling of Graphene Reinforced Ti6Al4V Nanocomposites: An Artificial Intelligence Based Industry 4.0 Approach
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Industrial Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
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Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada
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Author to whom correspondence should be addressed.
Materials 2020, 13(24), 5707; https://doi.org/10.3390/ma13245707
Received: 12 November 2020
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Revised: 6 December 2020
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Accepted: 9 December 2020
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Published: 14 December 2020
(This article belongs to the Special Issue Additive and Subtractive Manufacturing of Advanced Materials: Applications, Future Trends and Perspectives of Industry 4.0)
The studies about the effect of the graphene reinforcement ratio and machining parameters to improve the machining performance of Ti6Al4V alloy are still rare and incomplete to meet the Industry 4.0 manufacturing criteria. In this study, a hybrid adaptive neuro-fuzzy inference system (ANFIS) with a multi-objective particle swarm optimization method is developed to obtain the optimal combination of milling parameters and reinforcement ratio that lead to minimize the feed force, depth force, and surface roughness. For achieving this, Ti6Al4V matrix nanocomposites reinforced with 0 wt.%, 0.6 wt.%, and 1.2 wt.% graphene nanoplatelets (GNPs) are produced. Afterward, a full factorial approach was used to design experiments to investigate the effect of cutting speed, feed rate, and graphene nanoplatelets ratio on machining behaviour. After that, artificial intelligence based on ANFIS is used to develop prediction models as the fitness function of the multi-objective particle swarm optimization method. The experimental results showed that the developed models can obtain an accurate estimation of depth force, feed force, and surface roughness with a mean absolute percentage error of 3.87%, 8.56%, and 2.21%, respectively, as compared with experimentally measured outputs. In addition, the developed artificial intelligence models showed 361.24%, 35.05%, and 276.47% less errors for depth force, feed force, and surface roughness, respectively, as compared with the traditional mathematical models. The multi-objective optimization results from the new approach indicated that a cutting speed of 62 m/min, feed rate of 139 mm/min, and GNPs reinforcement ratio of 1.145 wt.% lead to the improved machining characteristics of GNPs reinforced Ti6Al4V matrix nanocomposites. Henceforth, the hybrid method as a novel artificial intelligent method can be used for optimizing the machining processes with complex relationships between the output responses.
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Keywords:
Ti6Al4V matrix nanocomposite; graphene nanoplatelets; multi-objective particle swarm optimization; artificial intelligence; industry 4.0
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MDPI and ACS Style
M. Nasr, M.; Anwar, S.; M. Al-Samhan, A.; Ghaleb, M.; Dabwan, A. Milling of Graphene Reinforced Ti6Al4V Nanocomposites: An Artificial Intelligence Based Industry 4.0 Approach. Materials 2020, 13, 5707. https://doi.org/10.3390/ma13245707
AMA Style
M. Nasr M, Anwar S, M. Al-Samhan A, Ghaleb M, Dabwan A. Milling of Graphene Reinforced Ti6Al4V Nanocomposites: An Artificial Intelligence Based Industry 4.0 Approach. Materials. 2020; 13(24):5707. https://doi.org/10.3390/ma13245707
Chicago/Turabian StyleM. Nasr, Mustafa, Saqib Anwar, Ali M. Al-Samhan, Mageed Ghaleb, and Abdulmajeed Dabwan. 2020. "Milling of Graphene Reinforced Ti6Al4V Nanocomposites: An Artificial Intelligence Based Industry 4.0 Approach" Materials 13, no. 24: 5707. https://doi.org/10.3390/ma13245707
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