Multistage artificial aging optimization for tensile properties of Duralium using Response Surface Method (RSM)

Puspitasari, Poppy; Tsamroh, Dewi Izzatus; Achyarsyah, Mochamad; Bandanajaya, Beny; Puspitasari, Dewi

doi:10.1051/matecconf/201820400007

Cited by 4 publications

(1 citation statement)

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“…process, evaluate the relevance of aging parameters, and estimate the combined effect of process factors on the aging behaviour of AA6056. Many researchers used RSM as a statistical tool in their work and concluded that it is an effective tool for optimization and prediction [13] to [15]. To manufacture a certain product in today's world, it is critical to have a thorough understanding of the mechanical properties of materials (under working conditions) that will be utilised in the fabrication process, which must be chosen from the lot and should match the requirements criteria such as hardness and strength; if not, they should be treated to get the desired results.…”

Section: Response Surface Methodology (Rsm) and Artificial Neural Net...mentioning

confidence: 99%

Optimization and prediction of the hardness behaviour of LM4 + Si3N4 composites using RSM and ANN: A comparative study

et al. 2022

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In the present work, LM4 + Si3N4 (1, 2, and 3 wt.%) composites were fabricated using the two-stage stir casting method. Precipitation hardening treatment was carried out on the cast composites and hardness results were compared with as-cast specimens. Microstructural analysis was performed using Scanning Electron Microscope (SEM) images to validate the existence and homogenous distribution of reinforcement in the matrix. LM4 + 3 wt.% Si3N4 composite with multistage solution heat treatment (MSHT) and aging at 100°C showed higher hardness viz., 124% improvement when compared to as-cast LM4 due to the uniform distribution of Si3N4 and precipitation of metastable phases during the heat treatment process. The microhardness values of the fabricated composites was investigated using Artificial Neural Network (ANN) and Response Surface Methodology (RSM). Both RSM and ANN models predicted hardness values close to experimental values with minimum error, and the prominence of aging temperature in the improvement of hardness was observed. The data obtained illustrate that the proposed regression model can accurately predict hardness values within the constraints of the factors under consideration. Based on the error values it can be concluded that the ANN model can deliver results with higher accuracy than the RSM model.

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Section: Response Surface Methodology (Rsm) and Artificial Neural Net...mentioning

confidence: 99%