| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
Shettar, Manjunath
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (6/6 displayed)
- 2024Synergistic Enhancement of the Mechanical Properties of Epoxy-Based Coir Fiber Composites through Alkaline Treatment and Nanoclay Reinforcementcitations
- 2024Assessment of Wear and Surface Roughness Characteristics of Polylactic Acid (PLA)—Graphene 3D-Printed Composites by Box–Behnken Methodcitations
- 2023Prediction of age-hardening behaviour of LM4 and its composites using artificial neural networkscitations
- 2023Experimental Investigation of Mechanical Property and Wear Behaviour of T6 Treated A356 Alloy with Minor Addition of Copper and Zinccitations
- 2022OPTIMIZATION AND PREDICTION OF THE HARDNESS BEHAVIOUR OF LM4 + SI3N4 COMPOSITES USING RSM AND ANN - A COMPARATIVE STUDYcitations
- 2022Water Sorption-Desorption-Resorption Effects on Mechanical Properties of Epoxy-Nanoclay Nanocompositescitations
Places of action
| Organizations | Location | People |
|---|
article
Prediction of age-hardening behaviour of LM4 and its composites using artificial neural networks
Abstract
<jats:title>Abstract</jats:title><jats:p>This research work highlights the prediction of hardness behaviour of age-hardened LM4 and its composites fabricated using a two-stage stir casting method with TiB<jats:sub>2</jats:sub> and Si<jats:sub>3</jats:sub>N<jats:sub>4</jats:sub>. MATLAB - Artificial Neural Networks is used to predict the age-hardening behaviour of LM4 and its composites. Experiments (hardness and tensile tests) are conducted to collect data for training an ANN model as well as to investigate the effect of reinforcements and age-hardening treatment on LM4 and its composites. The results show that with an increment in the reinforcement wt%, there is an enhancement in hardness and ultimate tensile strength (UTS) values within the monolithic composites. As-cast hybrid composites display a 37 to 54% improvement in hardness compared to as-cast LM4. Heat-treated samples, specifically those treated with peak aging with MSHT and 100 °C aging, perform better than as-cast samples and other heat-treated samples in terms of UTS and hardness. Compared to as-cast LM4, MSHT, and 100 °C aged samples display an 85 to 202% increment in VHN. Hybrid composites perform better in terms of hardness, while composites with 3 wt% of TiB<jats:sub>2</jats:sub> (L3TB) perform better in terms of UTS, peak aged (MSHT and 100 °C aging) L3TB display 68% increment in UTS when compared to as-cast LM4. ANN model is developed and trained with five inputs (wt% of TiB<jats:sub>2</jats:sub>, wt% of Si<jats:sub>3</jats:sub>N<jats:sub>4</jats:sub>, type of solutionizing, aging temperature, and aging time) and one output (VHN) using different algorithms and a different number of hidden neurons to predict the age hardening behaviour of composites. Among them, Lavenberg-Marquardt (LM) training algorithm with normalized data and 30 hidden neurons performs well and shows a least average error of 1.588364. The confirmation test confirms that the trained ANN model can predict the output with an average %error of 0.14 using unseen data.</jats:p>