Using Neural Networks to Predict the Hardness of Aluminum Alloys

Zahran, Bilal

doi:10.48084/etasr.529

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…While a single-layer neural network may still produce approximate predictions, more hidden layers can optimize and tune for accuracy [17]. Many Artificial Intelligence (AI) applications and services are relying on DL to boost automation by executing analytical and physical activities without human interaction [18]. Authors in [1], said that DL is the rediscovery neural networks, which were algorithmically conceived in the '80s.…”

Section: Experimental Workmentioning

confidence: 99%

Deep Learning CNN for the Prediction of Grain Orientations on EBSD Patterns of AA5083 Alloy

Suker

2022

Eng. Technol. Appl. Sci. Res.

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Indexing of Electron Backscatter Diffraction (EBSD) is a well-established method of crystalline material characterization that provides phase and orientation information about the crystals on the material surface. A deep learning Convolutional Neural Network was trained to predict crystal orientation from the EBSD patterns based on the mean disorientation error between the predicted crystal orientation and the ground truth. The CNN is trained using EBSD images for different deformation conditions of AA5083.

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Section: Experimental Workmentioning

confidence: 99%

Deep Learning CNN for the Prediction of Grain Orientations on EBSD Patterns of AA5083 Alloy

Suker

2022

Eng. Technol. Appl. Sci. Res.

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“…Artificial Neural Networks (ANNs) and deep learning have achieved impressive successes in fields such as image recognition, speech recognition, and prediction [1][2][3][4][5][6]. ANNs are computationally expensive because they are composed of a huge number of computational tasks and internal parameters.…”

Section: Introductionmentioning

confidence: 99%

A Low-cost Artificial Neural Network Model for Raspberry Pi

Truong

2020

Eng. Technol. Appl. Sci. Res.

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In this paper, a ternary neural network with complementary binary arrays is proposed for representing the signed synaptic weights. The proposed ternary neural network is deployed on a low-cost Raspberry Pi board embedded system for the application of speech and image recognition. In conventional neural networks, the signed synaptic weights of –1, 0, and 1 are represented by 8-bit integers. To reduce the amount of required memory for signed synaptic weights, the signed values were represented by a complementary binary array. For the binary inputs, the multiplication of two binary numbers is replaced by the bit-wise AND operation to speed up the performance of the neural network. Regarding image recognition, the MINST dataset was used for training and testing of the proposed neural network. The recognition rate was as high as 94%. The proposed ternary neural network was applied to real-time object recognition. The recognition rate for recognizing 10 simple objects captured from the camera was 89%. The proposed ternary neural network with the complementary binary array for representing the signed synaptic weights can reduce the required memory for storing the model’s parameters and internal parameters by 75%. The proposed ternary neural network is 4.2, 2.7, and 2.4 times faster than the conventional ternary neural network for MNIST image recognition, speech commands recognition, and real-time object recognition respectively.

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“…Also, the prediction of corrosion and hardness using percentage dopants, sintering time and temperature as input variables have been reported [26]. The percentage constituent elements of Al composites have been used as inputs to predict different outputs such as hardness [27], wear resistance [28], wear loss and surface roughness [29], and hardness, density and porosity [26]. Some models for predicting different mechanical properties based on the combination of processing parameters and percentage combination has been developed [30].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network in the Modelling of the Effect of Chromium Dopants on the Mechanical Properties of Al-4%cu Alloy

Emagbetere¹,

ARUOTURE²,

ASHIEDU³

2019

JESR

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Artificial Neural Network (ANN) was used to model the effect of Chromium dopants on the mechanical properties duralumin (Al-4 %Cu). The results showed that the hardness, yield strength, and ultimate tensile strength increased, while the energy absorbed and percentage elongation decreased, with increasing %wt of Chromium dopants. Simulation results of ANN show strong agreement with experimental values, having satisfactory R-values of Mean Square Error. ANN can suitably be used to predict the mechanical properties of Al-4%Cu doped with Chromium.

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Using Neural Networks to Predict the Hardness of Aluminum Alloys

Cited by 8 publications

References 6 publications

Deep Learning CNN for the Prediction of Grain Orientations on EBSD Patterns of AA5083 Alloy

Deep Learning CNN for the Prediction of Grain Orientations on EBSD Patterns of AA5083 Alloy

A Low-cost Artificial Neural Network Model for Raspberry Pi

Artificial Neural Network in the Modelling of the Effect of Chromium Dopants on the Mechanical Properties of Al-4%cu Alloy

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