Artificial neural network approach for mechanical properties prediction of as-cast A380 aluminum alloy

Li, Qiao; Wang, Xingchen; Sun, Jiacheng; Chang, Zhiyu; Deng, Qingchen; Sun, Qicai; Liu, Zehua; Yuan, Lingyang; Wang, Jiacheng; Wu, Yaxu; Liu, Baoliang; Peng, Liming

doi:10.1016/j.mtcomm.2022.103301

Cited by 16 publications

(8 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently artificial neural network (ANN) has been utilized as an effective tool to predict an objective variable from multiple factors influencing the variable. Predictions of mechanical properties using ANN models have been reported in metals and alloys, [6][7][8][9][10][11][12][13] ceramics, 14 and metal-ceramic composites. [15][16][17] In these studies, mechanical properties as objective variables were regression-predicted from features such as material compositions, processing parameters, and so forth.…”

Section: Introductionmentioning

confidence: 99%

Fracture toughness evaluation of silicon nitride from microstructures via convolutional neural network

et al. 2022

View full text Add to dashboard Cite

The fracture toughness of silicon nitride (Si3N4) ceramics was evaluated directly from their microstructures via deep learning using convolutional neural network models. Totally 156 data sets containing microstructural images and relative densities were prepared from 45 types of Si3N4 samples as input feature quantities (IFQs) and were correlated to the fracture toughness as an objective variable. The data sets were divided into two groups. One was used for training, resulting in the creation of regression models for two kinds of IFQs: the microstructures only and a combination of the microstructures and the relative densities. The other group was used for testing the validity of the created models. As a result, the determination coefficient was approximately 0.8 even when using only the microstructures as the IFQs and was further improved when adding the relative densities. It was revealed that the fracture toughness of Si3N4 ceramics was well evaluated from their microstructures.

show abstract

Section: Introductionmentioning

confidence: 99%

Fracture toughness evaluation of silicon nitride from microstructures via convolutional neural network

et al. 2022

View full text Add to dashboard Cite

show abstract

“…As the most popular machine learning approach, an artificial neural network (ANN) can be constructed by combining several processing nodes (i.e., neurons or nodes) in some interconnected successive neuronic layers 47 . The multi-layer perceptron (MLP) is a well-established ANN type that often includes two feedforward neuronic layers, namely hidden and output 48 . Since the number of output nodes equals the number of dependent variables, it is always known 49 .…”

Section: Wavelet Transform-artificial Neural Networkmentioning

confidence: 99%

Wavelet-artificial neural network to predict the acetone sensing by indium oxide/iron oxide nanocomposites

Iranmanesh

Pourahmad

Shabestani

et al. 2023

Sci Rep

View full text Add to dashboard Cite

This study applies a hybridized wavelet transform-artificial neural network (WT-ANN) model to simulate the acetone detecting ability of the Indium oxide/Iron oxide (In2O3/Fe2O3) nanocomposite sensors. The WT-ANN has been constructed to extract the sensor resistance ratio (SRR) in the air with respect to the acetone from the nanocomposite chemistry, operating temperature, and acetone concentration. The performed sensitivity analyses demonstrate that a single hidden layer WT-ANN with nine nodes is the highest accurate model for automating the acetone-detecting ability of the In2O3/Fe2O3 sensors. Furthermore, the genetic algorithm has fine-tuned the shape-related parameters of the B-spline wavelet transfer function. This model accurately predicts the SRR of the 119 nanocomposite sensors with a mean absolute error of 0.7, absolute average relative deviation of 10.12%, root mean squared error of 1.14, and correlation coefficient of 0.95813. The In2O3-based nanocomposite with a 15 mol percent of Fe2O3 is the best sensor for detecting acetone at wide temperatures and concentration ranges. This type of reliable estimator is a step toward fully automating the gas-detecting ability of In2O3/Fe2O3 nanocomposite sensors.

show abstract

“…So far, predictions of mechanical properties using neural network models have been reported in metals and alloys, [10][11][12][13][14][15][16][17] ceramics, 18 and metal-ceramic composites. [19][20][21] The authors also successfully predicted fracture toughness directly from microstructure images of Si 3 N 4 ceramics via convolutional neural network (CNN) models.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence‐based determination of fracture toughness and bending strength of silicon nitride ceramics

et al. 2023

View full text Add to dashboard Cite

Two mechanical properties, fracture toughness (K IC ) and bending strength (σ), of silicon nitride (Si 3 N 4 ) ceramics were determined from their microstructural images via convolutional neural network (CNN) models. The Si 3 N 4 samples used for database were fabricated using various kinds of sintering additives under different process conditions. In total, 330 data sets were prepared and used for building the CNN models for artificial intelligence-bassed determination of the two mechanical properties and testing the determination accuracy of the trained models. The determination coefficients (R 2 ), which were used as accuracy indices, were approximately 0.85 for K IC and 0.92 for σ. Although both the R 2 values were relatively high, the lower value for K IC suggests that it is influenced more by what is little obtained from the microstructural information, such as grain-boundary characteristics. Furthermore, gradient-weighted class activation mapping, which can visualize which parts of the image the CNN models focus on, showed that the trained models determined the two mechanical properties based on correct recognition of the microstructural difference among the images.

show abstract

Artificial neural network approach for mechanical properties prediction of as-cast A380 aluminum alloy

Cited by 16 publications

References 42 publications

Fracture toughness evaluation of silicon nitride from microstructures via convolutional neural network

Fracture toughness evaluation of silicon nitride from microstructures via convolutional neural network

Wavelet-artificial neural network to predict the acetone sensing by indium oxide/iron oxide nanocomposites

Artificial Intelligence‐based determination of fracture toughness and bending strength of silicon nitride ceramics

Contact Info

Product

Resources

About