scholarly journals Mid-Layer Visualization in Convolutional Neural Network for Microstructural Images of Cast Irons

2021 ◽  
Vol 59 (6) ◽  
pp. 430-438
Author(s):  
Hyun-Ji Lee ◽  
In-Kyu Hwang ◽  
Sang-Jun Jeong ◽  
In-Sung Cho ◽  
Hee-Soo Kim
Keyword(s):  
Neural Network ◽  
Cast Iron ◽  
Convolutional Neural Network ◽  
Morphological Characteristics ◽  
Training Data ◽  
Spheroidal Graphite ◽  
Cast Irons ◽  
Network Layers ◽  
Graphite Cast Iron ◽  
Fully Connected

We attempted to classify the microstructural images of spheroidal graphite cast iron and grey cast iron using a convolutional neural network (CNN) model. The CNN comprised four combinations of convolution and pooling layers followed by two fully-connected layers. Numerous microscopic images of each cast iron were prepared to train and verify the CNN model. After training the network, the accuracy of the model was validated using an additional set of microstructural images which were not included in the training data. The CNN model exhibited an accuracy of approximately 98% for classification of the cast irons. Typically, CNN does not provide bases for image classification to human users. We tried to visualize the images between the network layers, to find out how the CNN identified the microstructures of the cast irons. The microstructural images shrank as they passed the convolutional and pooling layers. During the processes, it seems that the CNN detected morphological characteristics including the edges and contrast of the graphite phases. The mid-layer images still retained their characteristic microstructural features, although the image sizes were shrunk. The final images just before connecting the fully-connected layers seemed to have minimalized the information about the microstructural features to classify the two kinds of cast irons. Matrix phases such as ferrite and pearlite did not show prominent effects on the classification accuracy.

Semisolid Forging of Cast Iron Using Rapid Resistance Heating

2007 ◽  
Vol 561-565 ◽  
pp. 925-928 ◽  
Author(s):  
Seijiro Maki ◽  
Kazuhito Suzuki ◽  
Kenichiro Mori
Keyword(s):  
Cast Iron ◽  
Control Function ◽  
Electric Energy ◽  
Spheroidal Graphite ◽  
Pig Iron ◽  
Resistance Heating ◽  
Cast Irons ◽  
Ac Power ◽  
Spheroidal Graphite Cast Iron ◽  
Graphite Cast Iron

Feasibility of semisolid forging of cast iron using rapid resistance heating was experimentally investigated. Gray pig iron FC250 and spheroidal graphite cast iron FCD600, whose carbon equivalents are both 4.3% in mass, were used for the experiments. Since these cast irons have a narrow semisolid temperature range, an AC power supply with an input electric energy control function was used. In this study, the resistance heating characteristics of the cast irons were firstly examined, and then their semisolid forging experiments were conducted. In the forging experiments, the conditions of the forgings such as microstructures and hardness properties were examined, and the feasibility of the semisolid forging of cast iron using resistance heating was discussed. As a result, it was found that the method presented here is highly feasible.

Experimental Investigation of Twist Fatigue Characteristics on Piston Rings

2020 ◽  
Vol 12 (2) ◽  
Author(s):  
E. Pavithra ◽  
Mahesh Dhakal ◽  
Prithvi Hada ◽  
N. Yuvaraj ◽  
K. Sridhar
Keyword(s):  
Cast Iron ◽  
Internal Combustion Engines ◽  
Piston Ring ◽  
Spheroidal Graphite ◽  
Piston Rings ◽  
Cast Irons ◽  
Spheroidal Graphite Cast Iron ◽  
Graphite Cast Iron ◽  
Grey Cast ◽  
Fatigue Characteristics

Piston ring is one of the most important parts of the internal combustion engines. This paper investigates the mechanical and twist fatigue characteristics on different piston ring materials. The piston ring materials were chosen in this study such as grey cast irons (3740 and 6140), malleable cast iron (3929), spheroidal graphite cast iron (6139) and martensitic carbidic grey cast iron (6454). Twist fatigue test was conducted on different materials of piston rings in order to identify the suitable piston ring for the effective operation. Geometrical features and the mechanical properties were also assessed in different materials for the effectiveness of piston rings.

Suppression of Elevated Temperature Brittleness in Spheroidal Graphite Cast Iron by Increasing Phosphorus Content

2010 ◽  
Vol 457 ◽  
pp. 428-432
Author(s):  
Yoshitaka Iwabuchi ◽  
Isao Kobayashi
Keyword(s):  
Cast Iron ◽  
Elevated Temperature ◽  
Phosphorus Content ◽  
Marked Decrease ◽  
Spheroidal Graphite ◽  
Cast Irons ◽  
Spheroidal Graphite Cast Iron ◽  
Graphite Cast Iron ◽  
Low Phosphorus ◽  
Fracture Appearance

Elevated temperature brittleness (ETB) of spheroidal graphite (s-g) cast iron has been referred to as reduced ductility within an elevated temperature range and has been related to grain boundary brittleness. The phenomenon of ETB has not been yet clearly understood. In this study, the factor affecting on ETB was studied in terms of strain rate and chemical composition. A study was carried out on the influence of phosphorus on ETB by using laboratory-made heats containing different phosphorus contents. ETB indicated the marked decrease in ductility at around 673K. S-g cast iron containing low phosphorus content manifested ETB at temperatures between 650K and 700K. There was a consistent correlation between the fractional increases in intergranular fracture appearance and the decrease in elongation. The increase of phosphorus suppressed ETB and s-g cast irons containing phosphorus exceeding 0.030 % were found to be immune to ETB. It was found that ETB could be suppressed by reducing the ratio of magnesium and phosphorus to less than 1.5.

Operational Degradation of Spheroidal Graphite Cast Iron EN GJS SiMo

2017 ◽  
Vol 270 ◽  
pp. 27-33
Author(s):  
Břetislav Skrbek
Keyword(s):  
Cast Iron ◽  
High Temperature ◽  
Critical Temperature ◽  
Spheroidal Graphite ◽  
Temperature Structure ◽  
Cast Irons ◽  
Alloyed Cast ◽  
Spheroidal Graphite Cast Iron ◽  
Graphite Cast Iron ◽  
Operational Degradation

The specifics of low-alloyed cast irons after EN 16124 standard of type GJS SiMo for high temperature applications of exhaust tracts of internal combustion piston engines. Boundary exposition temperature. Structure failure of cast iron by temperature overloading. Metalography, SEM, XRD of overexposed exhaust pipelines. Failure reason hypothesis of useful properties by exceeding of critical temperature.

Abstract 268: A Convolutional Neural Network for Real-Time Discrimination of Shock-Indicated Rhythms During CPR

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_2) ◽  
Author(s):  
Tetsuo Hatanaka ◽  
Hiroshi Kaneko ◽  
Aki Nagase ◽  
Seishiro Marukawa
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sensitivity And Specificity ◽  
Binary Classification ◽  
Total Duration ◽  
Training Data ◽  
Validation Data ◽  
Data Set ◽  
The Neural Network ◽  
Fully Connected

Introduction: An interruption of chest compressions during CPR adversely affects patient outcome. Currently, however, periodical interruptions are unavoidable to assess the ECG rhythms and to give shocks for defibrillation if indicated. Evidence suggests a 5-second interruption immediately before shocks may translate into ~15% reduction of the chance of survival. The objective of this study was to build, train and validate a convolutional neural network (artificial intelligence) for detecting shock-indicated rhythms out of ECG signals corrupted with chest compression artifacts during CPR. Methods: Our convolutional neural network consisted of 7 convolutional layers, 3 pooling layers and 3 fully-connected layers for binary classification (shock-indicated vs non-shock-indicated). The input data set was a spectrogram consisting of 56 frequency-bins by 80 time-segments transformed from a 12.16-seconds ECG signal. From AEDs used for 236 patients with out-of-hospital cardiac arrest, 1,223 annotated ECG strips were extracted. Ventricular fibrillation and wide-QRS ventricular tachycardia with HR>180 beats/min were annotated as shock-indicated, and the others as non-shock-indicated. The total length of the strips was 8:49:57 (hr:min:sec) and 8:02:07 respectively for shock-indicated and non-shock-indicated rhythms. Those strips were converted into 465,102 spectrograms allowing partial overlaps and were fed into the neural network for training. The validation data set was obtained from a separate group of 225 patients, from which annotated ECG strips (total duration of 62:11:28) were extracted, yielding 43,800 spectrograms. Results: After the training, both the sensitivity and specificity of detecting shock-indicated rhythms over the training data set were 99.7% - 100% (varying with training instances). The sensitivity and specificity over the validation data set were 99.3% - 99.7% and 99.3% - 99.5%, respectively. Conclusions: The convolutional neural network has accurately and continuously evaluated the ECG rhythms during CPR, potentially obviating the need for rhythm checks for defibrillation during CPR.

KLASIFIKASI CITRA GENUS PANTHERA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

2019 ◽  
Vol 24 (3) ◽  
pp. 220-228
Author(s):  
Gusti Alfahmi Anwar ◽  
Desti Riminarsih
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Input Layer ◽  
Fully Connected

Panthera merupakan genus dari keluarga kucing yang memiliki empat spesies popular yaitu, harimau, jaguar, macan tutul, singa. Singa memiliki warna keemasan dan tidak memilki motif, harimau memiliki motif loreng dengan garis-garis panjang, jaguar memiliki tubuh yang lebih besar dari pada macan tutul serta memiliki motif tutul yang lebih lebar, sedangkan macan tutul memiliki tubuh yang sedikit lebih ramping dari pada jaguar dan memiliki tutul yang tidak terlalu lebar. Pada penelitian ini dilakukan klasifikasi genus panther yaitu harimau, jaguar, macan tutul, dan singa menggunakan metode Convolutional Neural Network. Model Convolutional Neural Network yang digunakan memiliki 1 input layer, 5 convolution layer, dan 2 fully connected layer. Dataset yang digunakan berupa citra harimau, jaguar, macan tutul, dan singa. Data training terdiri dari 3840 citra, data validasi sebanyak 960 citra, dan data testing sebanyak 800 citra. Hasil akurasi dari pelatihan model untuk training yaitu 92,31% dan validasi yaitu 81,88%, pengujian model menggunakan dataset testing mendapatan hasil 68%. Hasil akurasi prediksi didapatkan dari nilai F1-Score pada pengujian didapatkan sebesar 78% untuk harimau, 70% untuk jaguar, 37% untuk macan tutul, 74% untuk singa. Macan tutul mendapatkan akurasi terendah dibandingkan 3 hewan lainnya tetapi lebih baik dibandingkan hasil penelitian sebelumnya.

Color Etching for Characterization the Grain Orientation in Spheroidal Graphite Cast Iron

2007 ◽  
Vol 537-538 ◽  
pp. 389-396 ◽  
Author(s):  
Ibolya Kardos ◽  
Zoltán Gácsi ◽  
Péter János Szabó
Keyword(s):  
Cast Iron ◽  
Crystallographic Orientation ◽  
Grain Orientation ◽  
Test Material ◽  
Spheroidal Graphite ◽  
Metallic Materials ◽  
Additional Information ◽  
Spheroidal Graphite Cast Iron ◽  
Graphite Cast Iron ◽  
Back Scattering

Color etching is a widely used technique for visualizing different phases in metallic materials. Its advantage to the traditional etching techniques is that it gives additional information within one phase, namely, the color shade of a given phase can change in a certain range. This paper demonstrates that, due to the physics of the color etching, the shade of a phase also depends on the crystallographic orientation of the investigated grain. As a test material, spheroidal graphite cast iron was used, and individual grain orientation was identified by automated electron back scattering diffraction (EBSD). Results showed that there is a strong correlation between grain orientation and the shades obtained by color etching.

scholarly journals Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 119
Author(s):  
Tao Wang ◽  
Changhua Lu ◽  
Yining Sun ◽  
Mei Yang ◽  
Chun Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Ecg Signals ◽  
Rr Interval ◽  
Frequency Components ◽  
Fully Connected

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

scholarly journals Cashmere and wool identification based on convolutional neural network

2021 ◽  
Vol 16 ◽  
pp. 155892502110050
Author(s):  
Junli Luo ◽  
Kai Lu ◽  
Yueqi Zhong ◽  
Boping Zhang ◽  
Huizhu Lv
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Morphological Characteristics ◽  
Identification Accuracy ◽  
Test Set ◽  
Image Preprocessing ◽  
Softmax Regression ◽  
Wool Fibers ◽  
Chinese White ◽  
Multi Classification

Wool fiber and cashmere fiber are similar in physical and morphological characteristics. Thus, the identification of these two fibers has always been a challenging proposition. This study identifies five kinds of cashmere and wool fibers using a convolutional neural network model. To this end, image preprocessing was first performed. Then, following the VGGNet model, a convolutional neural network with 13 weight layers was established. A dataset with 50,000 fiber images was prepared for training and testing this newly established model. In the classification layer of the model, softmax regression was used to calculate the probability value of the input fiber image for each category, and the category with the highest probability value was selected as the prediction category of the fiber. In this experiment, the total identification accuracy of samples in the test set is close to 93%. Among these five fibers, Mongolian brown cashmere has the highest identification accuracy, reaching 99.7%. The identification accuracy of Chinese white cashmere is the lowest at 86.4%. Experimental results show that our model is an effective approach to the identification of multi-classification fiber.

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