Artificial Neural Network Approach to Predict Mechanical Properties of 301 Austenitic Stainless Steel

2010 ◽  
Vol 658 ◽  
pp. 145-148 ◽  
Author(s):  
Zhi Yu Chen ◽  
De Ning Zou ◽  
Jun Hui Yu ◽  
Ying Han
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Rolling Reduction ◽  
Ann Model ◽  
Neural Network Approach ◽  
Artificial Neural

In this study, the effect of original thicknesses of plate, the thicknesses of plate after rolling and rolling reduction on the strength in 301 stainless steel was modeled by means of artificial neural network (ANN). The experimental data were collected to obtain training set and testing set. The normalization method was employed for avoiding over-fitting. The optimal ANN method architecture was determined by according to the trial and error procedure. The results of the ANN model were in good agreement with experimental data. As can be seen from the result, we believe that the neural network model can efficiently predict the relationship between mechanical properties and rolling reduction in 301 austenitic stainless steel.

Prediction of the Flow Stress of a High Alloyed Austenitic Stainless Steel Using Artificial Neural Network

2012 ◽  
Vol 724 ◽  
pp. 351-354 ◽  
Author(s):  
Zhao Hui Zhang ◽  
Dong Na Yan ◽  
Jian Tao Ju ◽  
Ying Han
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stainless Steel ◽  
Flow Stress ◽  
Austenitic Stainless Steel ◽  
Strain Rate ◽  
Flow Behavior ◽  
Constitutive Relationship ◽  
Ann Model ◽  
Artificial Neural

The high temperature flow behavior of as-cast 904L austenitic stainless steel was studied using artificial neural network (ANN). Isothermal compression tests were carried out at the temperature range of 1000°C to 1200°C and strain rate range of 0.01 to 10s1. Based on the experimental flow stress data, an ANN model for the constitutive relationship between flow stress and strain, strain rate and deformation temperature was constructed by back-propagation (BP) method. Three layer structured network with one hidden layer and nine hidden neurons was trained and the normalization method was employed in training process to avoid over fitting. Modeling results show that the developed ANN model exhibits good performance for predicting the flow stresses of the 904L steel. Therefore, it can be used to reflect the hot deformation behavior in a wide working window.

scholarly journals Prediction and Analysis of Tensile Properties of Austenitic Stainless Steel Using Artificial Neural Network

Metals ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 234 ◽  
Author(s):  
Yuxuan Wang ◽  
Xuebang Wu ◽  
Xiangyan Li ◽  
Zhuoming Xie ◽  
Rui Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Tensile Properties ◽  
Prediction Performance ◽  
Good Prediction ◽  
Relative Significance ◽  
Value Analysis ◽  
Artificial Neural

Predicting mechanical properties of metals from big data is of great importance to materials engineering. The present work aims at applying artificial neural network (ANN) models to predict the tensile properties including yield strength (YS) and ultimate tensile strength (UTS) on austenitic stainless steel as a function of chemical composition, heat treatment and test temperature. The developed models have good prediction performance for YS and UTS, with R values over 0.93. The models were also tested to verify the reliability and accuracy in the context of metallurgical principles and other data published in the literature. In addition, the mean impact value analysis was conducted to quantitatively examine the relative significance of each input variable for the improvement of prediction performance. The trained models can be used as a guideline for the preparation and development of new austenitic stainless steels with the required tensile properties.

Prediction of Wear Behavior in Porous Sintered Steels: Artificial Neural Network Approach

2018 ◽  
Vol 18 (2) ◽  
pp. 111-115
Author(s):  
Hassan Abdoos ◽  
Ahmad Tayebi ◽  
Meysam Bayat
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Wear Resistance ◽  
Wear Behavior ◽  
Affecting Factors ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Comparison Of The Results

Abstract Due to the increasing usage of powder metallurgy (PM), there is a demand to evaluate and improve the mechanical properties of PM parts. One of the most important mechanical properties is wear behavior, especially in parts that are in contact with each other. Therefore, the choice of materials and select manufacturing parameters are very important to achieve proper wear behavior. So, prediction of wear resistance is important in PM parts. In this paper, we try to investigate and predict the wear resistance (volume loss) of PM porous steels according to the affecting factors such as: density, force and sliding distance by artificial neural network (ANN). ANN training was done by a multilayer perceptron procedure. The comparison of the results estimated by the ANN with the experimental data shows their proper matching. This issue confirms the efficiency of using method for prediction of wear resistance in PM steel parts.

Effective parameters modeling in compression of an austenitic stainless steel using artificial neural network

2005 ◽  
Vol 34 (4) ◽  
pp. 335-341 ◽  
Author(s):  
A. Bahrami ◽  
S.H. Mousavi Anijdan ◽  
H.R. Madaah Hosseini ◽  
A. Shafyei ◽  
R. Narimani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Effective Parameters ◽  
Artificial Neural

Artificial neural network based modeling of the coupled effect of sulphate and temperature on the strength of cemented paste backfill

2011 ◽  
Vol 38 (1) ◽  
pp. 100-109 ◽  
Author(s):  
Libardo Orejarena ◽  
Mamadou Fall
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Design Parameter ◽  
Curing Temperature ◽  
Ann Model ◽  
Large Variability ◽  
Sulphate Content ◽  
Coupled Effect ◽  
Artificial Neural

Among the different options for mine waste management, cemented paste backfills (CPB) have become important in mining operations around the world due to their environmental and economic benefits. The key design parameter of a CPB structure is its mechanical stability, which is commonly evaluated by the uniaxial compressive strength (UCS) of the CPB material. Experimental studies have shown that the sulphate present within the CPB and the curing temperatures can significantly affect the strength of CPBs. The increasing use of CPBs in underground mine operations as well as the subjection of CPBs to a large variability of thermal (curing temperature) and chemical (sulphate content) loads, make it necessary to model and quantify the coupled effect of sulphate and curing temperature on the strength (key design parameter) of CPBs. Therefore, the main objective of this study is to develop a methodological approach and a mathematical model based on an artificial neural network (ANN) to analyze and predict the effect of different amounts of sulphate on the strength of mature CPBs cured at various temperatures. Based on the experimental results of UCS tests from previous studies on various CPBs, the authors have developed an ANN model by using an ANN methodology implemented through MATLAB™. The developed model is validated with experimental data that is not used for the model development. The validation shows good agreement between the predicted and experimental data. The results from the ANN model of this study show that the coupled effect of curing temperature and sulphate significantly affects the strength of CPBs. This effect can be positive (strength increase) or negative (strength decrease) depending on the initial amount of sulphate content, the curing temperature, and type of binder. Furthermore, this study demonstrates that ANN can be used as a valuable tool to evaluate the coupled influence of sulphate and temperature on the strength of CPBs, i.e., it is a suitable tool for the optimization of a CPB mixture.

Using Artificial Neural Network (ANN) for Manipulating Energy Gain of Nansulate Coating

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Hadi Salehi ◽  
Mosayyeb Amiri ◽  
Morteza Esfandyari
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Energy Gain ◽  
Operating Conditions ◽  
Maximum Relative Error ◽  
Ann Model ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this work, an extensive experimental data of Nansulate coating from NanoTechInc were applied to develop an artificial neural network (ANN) model. The Levenberg–Marquart algorithm has been used in network training to predict and calculate the energy gain and energy saving of Nansulate coating. By comparing the obtained results from ANN model with experimental data, it was observed that there is more qualitative and quantitative agreement between ANN model values and experimental data results. Furthermore, the developed ANN model shows more accurate prediction over a wide range of operating conditions. Also, maximum relative error of 3% was observed by comparison of experimental and ANN simulation results.

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