scholarly journals Application of an Artificial Neural Network to Develop Fracture Toughness Predictor of Ferritic Steels Based on Tensile Test Results

Metals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1740
Author(s):  
Kenichi Ishihara ◽  
Hayato Kitagawa ◽  
Yoichi Takagishi ◽  
Toshiyuki Meshii
Keyword(s):  
Neural Network ◽  
Fracture Toughness ◽  
Artificial Neural Network ◽  
Structural Integrity ◽  
Steel Structures ◽  
Large Temperature ◽  
Ferritic Steels ◽  
Long Time ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Analyzing the structural integrity of ferritic steel structures subjected to large temperature variations requires the collection of the fracture toughness (KJc) of ferritic steels in the ductile-to-brittle transition region. Consequently, predicting KJc from minimal testing has been of interest for a long time. In this study, a Windows-ready KJc predictor based on tensile properties (specifically, yield stress σYSRT and tensile strength σBRT at room temperature (RT) and σYS at KJc prediction temperature) was developed by applying an artificial neural network (ANN) to 531 KJc data points. If the σYS temperature dependence can be adequately described using the Zerilli–Armstrong σYS master curve (MC), the necessary data for KJc prediction are reduced to σYSRT and σBRT. The developed KJc predictor successfully predicted KJc under arbitrary conditions. Compared with the existing ASTM E1921 KJc MC, the developed KJc predictor was especially effective in cases where σB/σYS of the material was larger than that of RPV steel.

Artificial Neural Network in Operation Management Regarding Communication Issue

2021 ◽  
pp. 2303-2320
Author(s):  
Ayan Chatterjee ◽  
Susmita Sarkar ◽  
Mahendra Rong ◽  
Debmallya Chatterjee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travelling Salesman Problem ◽  
Operation Management ◽  
Routing Problem ◽  
Important Concern ◽  
Management Area ◽  
Long Time ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Communication issue in operation management is important concern in the age of 21st century. In operation, communication can be described based on major three wings- Travelling Salesman Problem (TSP), Vehicle Routing Problem (VRP) and Transportation Problem (TP). Artificial Neural Network (ANN) is an important tool to handle these systems. In this chapter, different ANN based models are discussed in a comprehensive way. This chapter deals with how various approaches of ANN help to design the optimal communication network. This comprehensive study is important to the decision makers for the analytical consideration. Although there is a lot of development in this particular domain from a long time ago; but only the revolutionary contributed models are taken into account. Another motivation of this chapter is understanding the importance of ANN in the operation management area.

Prediction of Resilient Modulus for Hot Mix Asphalt Based on Artificial Neural Network

2011 ◽  
Vol 304 ◽  
pp. 18-23
Author(s):  
Chun Hua Hu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Test Data ◽  
Hot Mix Asphalt ◽  
Resilient Modulus ◽  
Structure Design ◽  
Forecast Precision ◽  
Long Time ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Resilient modulus of material is an important parameter for pavement structure design and analysis. However it is very tedious to get this parameter for hot mixture asphalt in laboratory. Moreover it takes long time to do experiments. In this paper, artificial neural network (ANN) is applied to predict to resilient modulus for hot mixture asphalt. A neural network model is constructed and trained plenty of times with selected test data until precision meets requirement. Then the model is used to predict resilient modulus for hot mix asphalt. Result of contrast prediction with test data shows that forecast precision is high. This provides a new method to predict resilient modulus for hot mixture asphalt.

Modeling and computation by artificial neural network of fracture toughness of low alloy steel to study the effect of alloy composition

2018 ◽  
Vol 09 (06) ◽  
pp. 1850051
Author(s):  
Subir Paul ◽  
Shibasish Bhattacharjee
Keyword(s):  
Neural Network ◽  
Fracture Toughness ◽  
Artificial Neural Network ◽  
Alloy Steel ◽  
Alloy Composition ◽  
Low Alloy Steel ◽  
Ann Model ◽  
Material Chemistry ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The unpredictable structure failures of carbon steel and low alloy steel leading to accidents may be caused by the propagation of a flaw or crack already present in the structure. Fracture toughness which describes the ability of a material containing a crack to resist fracture is one of the most important material properties for design applications of metallic structures. Since this material property is influenced by several parameters, namely material chemistry, heat treatment, morphology of structure, it requires millions of experiments to be conducted to understand and predict it. So, mathematical modeling is one of the solutions to find the effect of these parameters and design future alloys. Stress–intensity factor [Formula: see text] is a quantitative parameter of fracture toughness determining a maximum value of stress which may be applied to a specimen containing a crack (notch) of a certain length. An artificial neural network (ANN) model was developed using over 100 sets of data to study the effect of alloying elements on fracture toughness, [Formula: see text] for the low alloy steel. 20% of data was used for training, 60% to develop predictive model and rest of the 20% for validation. The model can predict the fracture toughness of unknown new data close to 80% accuracy which is good enough for statistical modeling. The details of program code with ANN modeling steps have been explained. Prediction of fracture toughness by the model with variation of alloy composition as well as yield stress gives interesting and important information which may help in designing alloy which will resist crack propagation in a structure and hence enhance the life of structure to fail.

Artificial Neural Network in Operation Management Regarding Communication Issue

2022 ◽  
pp. 490-508
Author(s):  
Ayan Chatterjee ◽  
Susmita Sarkar ◽  
Mahendra Rong ◽  
Debmallya Chatterjee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travelling Salesman Problem ◽  
Operation Management ◽  
Routing Problem ◽  
Important Concern ◽  
Management Area ◽  
Long Time ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Communication issue in operation management is important concern in the age of 21st century. In operation, communication can be described based on major three wings- Travelling Salesman Problem (TSP), Vehicle Routing Problem (VRP) and Transportation Problem (TP). Artificial Neural Network (ANN) is an important tool to handle these systems. In this chapter, different ANN based models are discussed in a comprehensive way. This chapter deals with how various approaches of ANN help to design the optimal communication network. This comprehensive study is important to the decision makers for the analytical consideration. Although there is a lot of development in this particular domain from a long time ago; but only the revolutionary contributed models are taken into account. Another motivation of this chapter is understanding the importance of ANN in the operation management area.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

Sign in / Sign up

Export Citation Format

Share Document