Modeling of shot-peening effects on the surface properties of a (TiB + TiC)/Ti–6Al–4V composite employing artificial neural networks

Maleki, Erfan; Zabihollah, Abolghassem

doi:10.17222/mit.2015.140

Cited by 12 publications

(5 citation statements)

References 29 publications

(48 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their similarities belong to the first classification because no TEC precursors occur due to the absence of earthquakes with the magnitude (M w ) ≥ 5.0 in this time period. The standard deviation (STD) and mean square error (MSE) are used as statistical approaches to evaluate the verified and prediction accuracy of the two CNN models for their reliable application (Maleki and Zabihollah, 2016;Lin et al, 2018). To verify the accuracy and reliability of the first CNN Model, the STD and MSE are 0.012 and 0.014, respectively, for the time period in Fig.…”

Section: Resultsmentioning

confidence: 99%

Detecting Total Electron Content Precursors Before Earthquakes by Examining Total Electron Content Images Based on Butterworth Filter in Convolutional Neural Networks

Lin

Chiou

2020

IEEE Access

View full text Add to dashboard Cite

Taiwan Standard Time [TST]) as training images (inputs) were used to create two convolutional neural network (CNN) models. However, splitting the TEC maps of the three time periods into daily TEC images caused wedge effects. The wedge effects were reduced using a low-pass filter called the Butterworth filter. This resulted in clearer TEC precursors for earthquakes, facilitating the identification of earthquakes of magnitude M w ≥ 5.0 that exhibited associated TEC precursors during three periods, particularly for the Chi-Chi earthquake of September 21, 1999. The results of this study were compared with those of Lin et al. (2001) and Lin (2010) associated with the Chi-Chi earthquake. Simultaneously, two CNN models that were developed were verified to be rational due to the high accuracy of their predictions. These two models were used to verify each other's accuracies and to demonstrate the reliability of the method in this study. Therefore, statistical analysis was not the aim. The final outputs of the two CNN model were defined as similarities. Similarities, which are larger than 0.5, were defined as TEC precursors of earthquakes. TEC precursors described as temporal TEC multi-precursors (TTMPs) by Zoran et al. (2018) were detectable on the 1st, 3rd, and 4th days (that is, on September 17, 18, and 20, 1999, respectively) prior to the Chi-Chi earthquake of September 21, 1999. These results are consistent with those of Liu et al. (2001) and Lin (2010). A TEC precursor on May 13, 2003, (TST) was detectable 2 days prior to the earthquake on May 15, 2003, (TST) with the magnitude (M w) of 5.52. The low standard deviation (STD) and mean square error (MSE) confirm the reliability of both CNN models. Regarding mechanical principles, the TTMPs related to the Chi-Chi earthquake were caused by an electric field (Liu et al., 2001). The cause of the TEC precursor on May 13, 2003, prior to the earthquake on May 15, 2003, was an argument without any corresponding study for comparison.

show abstract

Section: Resultsmentioning

confidence: 99%

Detecting Total Electron Content Precursors Before Earthquakes by Examining Total Electron Content Images Based on Butterworth Filter in Convolutional Neural Networks

Lin

Chiou

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

Section: Validation By Two Back-propagation Neural Network (Bpnn) Modelsmentioning

confidence: 99%

“…(7) The standard deviation (STD) and mean square error (MSE) (Maleki and Zabihollah, 2016) were used as statistical approaches to evaluate the reliable application and predicted accuracy of the first BPNN model. The STD and MSE values of the predictions using the first BPNN model were 0.012 and 0.020 in the inside test, respectively, as shown in Fig.…”

Section: Validation By Two Back-propagation Neural Network (Bpnn) Modelsmentioning

confidence: 99%

Generalized Two-Dimensional Principal Component Analysis and Two Artificial Neural Networks to Detect Traveling Ionospheric Disturbances Using the Tsunami-Atmosphere-Ionosphere Coupling Mechanism

Lin

2021

Preprint

View full text Add to dashboard Cite

A weak tsunami was induced by the 2016 Mw = 7.8 Sumatra earthquake, which occurred at 12:49 on March 2, 2016 (UTC). The epicenter was at 5.060°S, 94.170°E at a depth of 10 km. At 15.02 on March 2 (UTC), the weak tsunami (amplitude: 0.11 m) arrived at the station located at 10.40°S, 105.67°E. The largest first principal eigenvalue derived using the bilateral projection-based two-dimensional principal component analysis (B2DPCA) indicated a spatial traveling ionospheric disturbance (TID), which was caused by internal gravity waves (IGWs), at 13:20 on March 2 (UTC). The largest second principal eigenvalue represented another TID expanding to the southwest. The two largest principal eigenvalues were associated with the TIDs, which were also determined using two back-propagation neural network (BPNN) models and two convolutional neural network (CNN) models, called the BPNN-B2DPCA and CNN-B2DPCA methods, respectively. These two methods yielded the same results as the B2DPCA. Therefore, the robustness and reliability of the B2DPCA were validated.

show abstract

“…These networks are able to adapt to fit any advanced info and have the capability of prediction and optimization [35,36]. The principles of ANN modeling concerning the performance and application of the biological and artificial neurons has been studied in various works [37][38][39]. The main advantage of an ANN over usual numerical analysis procedures, under the provision that the predicted results fall within acceptable tolerances, is that results can be produced so fast, requiring orders of magnitude less computational effort than the common procedures [40].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Parametric analysis of collapse load factor of planar frames

Maleki

Kashyzadeh

2019

SN Appl. Sci.

Self Cite

View full text Add to dashboard Cite

The plastic analysis and determining the collapse load factor (CLF) of frames have received much attention in all kinematical methods applied for designing these frames. CLF in frames depends on many effective parameters such as length of the bay, the height of story, loads, and plastic moments. As the number of these parameters increases, the analysis becomes more complex and time-consuming. In this regard, artificial neural networks (ANNs) have been found many applications in prediction and optimization of ill-defined problems. In the present paper, two planar frames were considered and analyzed as examples for modeling via ANN. In order to develop the ANNs, results of thirty samples were gathered from every two examples. Parameters including the length of the bay, the height of story, loads, and plastic moments were regarded as inputs and the CLF was considered as the output of the networks. After developing the networks, the model function was generated and the effects of the input parameters were investigated on the CLF of frames. The results showed tuning networks are finely and adjusting their parameters would lead to very good and acceptable results for the design of frames.

show abstract

Modeling of shot-peening effects on the surface properties of a (TiB + TiC)/Ti–6Al–4V composite employing artificial neural networks

Cited by 12 publications

References 29 publications

Detecting Total Electron Content Precursors Before Earthquakes by Examining Total Electron Content Images Based on Butterworth Filter in Convolutional Neural Networks

Detecting Total Electron Content Precursors Before Earthquakes by Examining Total Electron Content Images Based on Butterworth Filter in Convolutional Neural Networks

Generalized Two-Dimensional Principal Component Analysis and Two Artificial Neural Networks to Detect Traveling Ionospheric Disturbances Using the Tsunami-Atmosphere-Ionosphere Coupling Mechanism

Parametric analysis of collapse load factor of planar frames

Contact Info

Product

Resources

About