Microstructure and Mechanical Properties of Cold Roll-Bonded Layered AA6061/AA5052/AA6061/AA5052 Aluminum Alloy Sheet

Jo, Sang-Hyeon; Park, Bo-Bae; Lee, Seong-Hee

doi:10.3740/mrsk.2022.32.3.161

Cited by 1 publication

(1 citation statement)

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“…Te rate at which a network learns is another crucial variable that must be considered during deployment. Authors [25] discussed that a low value for this parameter results in slow network convergence, which slows down the time it takes to obtain the desired response. In that case, there is a risk that the training process may become unstable, increasing the amount of inaccuracy in the response from cycle to cycle.…”

Section: Implementation Of the Neural Networkmentioning

confidence: 99%

Study of Microstructure and Wear Resistance of AA5052/B4C Nanocomposites as a Function of Volume Fraction Reinforcement to Particle Size Ratio by ANN

Kumar

Balamurugan²,

Suresh³

et al. 2023

Journal of Chemistry

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The effects of the percentage volume of reinforcement, the ratio of reinforcement, and the matrix size of particles on the wear behavior of AA5052/B4C metal matrix composites (MMCs) examine. This research examines a model function developed from an artificial neural network (ANN). AA5052/B4C composites bent using a powder metallurgy technique to hardness and ball-on-disc wear testing. There are two exemptions such as (1) when the percentage volume of reinforcement is less than 8% and (2) when the ratio of reinforcement particle size (Rs) and matrix particle size (Ms) increases before decreasing. The results show that wear loss decreases with increasing percentage volume of reinforcement and ratio of Rs and Ms. In the second case, wear loss is increased at high levels of percentage volume (14%) since the proportion of reinforcement and matrix size of the particle is close to 1. When the volume percentage of reinforcement is high (14%) and the matrix and reinforcement particle sizes are substantial (120 m), the reinforcement particles become dislodged and break. Because these broken-up particles are easily removed from the surface, the material’s wear resistance is reduced. In this case, raising the volume fraction yields a uniformly higher hardness for all Rs/Ms values; hence, composites with lower reinforcement volume percentages show better wear resistance. Hardness and wear resistance have no relationship with one another.

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Section: Implementation Of the Neural Networkmentioning

confidence: 99%