Evaluation of microstructure and prediction of hardness of Al–Cu based composites by using artificial neural network and linear regression through machine learning technique-Reference-Cited by-同舟云学术

Evaluation of microstructure and prediction of hardness of Al–Cu based composites by using artificial neural network and linear regression through machine learning technique

Published:2024-07-12 Issue: Volume: Page:
ISSN:2520-8160
Container-title:Multiscale and Multidisciplinary Modeling, Experiments and Design
language:en
Short-container-title:Multiscale and Multidiscip. Model. Exp. and Des.

Author:

Bharath L.,Kumaraswamy J.,Manjunath T. V.,Kulkarni Suneel Kumar N.

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s41939-024-00525-0.pdf

Reference24 articles.

1. Babalola P, Bolu C, Inegbenebor A (2015) Artificial neural network prediction of aluminium metal matrix composite with silicon carbide particles developed using stir casting method. Int J Mech Mechatr Eng IJMME IJENS 15:151–158

2. Bharath L, Sreenivasa Reddy M, Girisha HN, Balakumar G (2020a) Formation and evaluation of tensile strength and hardness properties of Al2024 alloy reinforced with boron carbide particulates. IJRAR 7:494–501

3. Bharath L, Sreenivasa Reddy M, Girisha HN, Balakumar G (2020b) Influence of boron carbide, heat treatment on ductility and yield strength of Al2024 alloy. Int J Sci Eng Res 11:13–17

4. Bharath L, Sreenivasa Reddy M, Girisha HN, Balakumar G (2021a) Optimization of ductility and yield strength on Al2024/B4C composite material using Taguchi technique. IOP Conf Ser Mater Sci Eng 1055:012117. https://doi.org/10.1088/1757-899x/1055/1/012117

5. Bharath L, Sreenivasa Reddy M, Girisha HN, Balakumar G (2021b) Influence of process parameters on tensile strength and hardness of AW2024/B4C composite using Taguchi’s technique. Mater Today Proc 47:2345–2350