scholarly journals Application of Artificial Neural Network to the Prediction of Tensile Properties in High-Strength Low-Carbon Bainitic Steels

Metals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1314
Author(s):  
Sang-In Lee ◽  
Seung-Hyeok Shin ◽  
Byoung-Chul Hwang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Yield Strength ◽  
Tensile Properties ◽  
Uniform Elongation ◽  
High Strength ◽  
Low Carbon ◽  
Ann Model ◽  
Bainitic Steels ◽  
Artificial Neural

An artificial neural network (ANN) model was designed to predict the tensile properties in high-strength, low-carbon bainitic steels with a focus on the fraction of constituents such as PF (polygonal ferrite), AF (acicular ferrite), GB (granular bainite), and BF (bainitic ferrite). The input parameters of the model were the fraction of constituents, while the output parameters of the model were composed of the yield strength, yield-to-tensile ratio, and uniform elongation. The ANN model to predict the tensile properties exhibited a higher accuracy than the multi linear regression (MLR) model. According to the average index of the relative importance for the input parameters, the yield strength, yield-to-tensile ratio, and uniform elongation could be effectively improved by increasing the fraction of AF, bainitic microstructures (AF, GB, and BF), and PF, respectively, in terms of the work hardening and dislocation slip behavior depending on their microstructural characteristics such as grain size and dislocation density. The ANN model is expected to provide a clearer understanding of the complex relationships between constituent fraction and tensile properties in high-strength, low-carbon bainitic steels.

Prediction of Tensile Properties of CNF/Epoxy Composites Using Artificial Neural Network

2014 ◽  
Vol 898 ◽  
pp. 111-114
Author(s):  
Xing Kai Chen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tensile Properties ◽  
Back Propagation ◽  
Epoxy Composites ◽  
Ann Model ◽  
Mechanical Stirring ◽  
Promising Tool ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In present study, artificial neural network (ANN) was used to predict the tensile moduli of carbon nanofibers (CNF)/epoxy composites. The tensile properties of CNF/epoxy composites made by different dispersion technique were measured by tensile test. It was found that the tensile properties are seriously affected by the CNF fraction, ultrasonication time and mechanical stirring time. According to the test results, ANN was trained and used to predict the tensile moduli of CNF/epoxy composites. By compared the predicted values with the experimental data, it was demonstrated that the back propagation ANN model is a promising tool for prediction of properties of composites.

Prediction of the Ultimate Tensile Strength in API X70 Line Pipe Steel Using an Artificial Neural Network Model

2019 ◽  
Vol 297 ◽  
pp. 71-81
Author(s):  
Adel Saoudi ◽  
Djahida Lerari ◽  
Farida Khamouli ◽  
L'Hadi Atoui ◽  
Khaldoun Bachari
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Tensile Strength ◽  
Chemical Composition ◽  
Ultimate Tensile Strength ◽  
Yield Strength ◽  
High Strength ◽  
Ann Model ◽  
Mean Relative Error

An artificial neural network (ANN) model has been developed for the analysis and simulation of the correlation between the chemical composition and mechanical properties of high strength low alloy (HSLA) steel X70. The input parameters of the model consist of the base metal chemical composition (C, Si, Mn, the sum of Cr+Cu+Ni+Mo, the sum of Nb+Ti+V, carbon equivalent CEpcm) and the yield strength (YS). The outputs of the ANN model include the ultimate tensile strength (UTS) of the test material. Scatter plots, correlation coefficient (R) and mean relative error (MRE) were used to assess the performance of the developed neural network. Interestingly, the model output is efficient to calculate the mechanical properties of high strength low alloy steels, especially the ultimate tensile strength as a function of chemical composition and yield strength of the used material. The obtained results are in a good agreement with experimental ones, with high correlation coefficient and low mean relative error. The predictions accuracy of the developed model also conforms to the results of mean paired T-test.

SPARTAN II

Alloy Digest ◽  
2016 ◽  
Vol 65 (1) ◽  
Keyword(s):  
Yield Strength ◽  
Physical Properties ◽  
Tensile Properties ◽  
High Strength ◽  
Low Carbon ◽  
Copper Precipitation ◽  
High Toughness ◽  
The Family ◽  
Alloy Steels ◽  
Minimum Yield

Abstract SPARTAN II (HSLA-100) is one of the family of Spartan high strength (>690 MPa, or >100 ksi, minimum yield strength), high toughness, improved weldability steels, which are alternatives to traditional quenched and tempered alloy steels. The Spartan family of steels are low carbon, copper precipitation hardened steels. Spartan II has improved yield strength compared to Spartan I. This datasheet provides information on composition, physical properties, microstructure, tensile properties. It also includes information on forming and joining. Filing Code: SA-738. Producer or source: ArcelorMittal USA.

Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

2019 ◽  
Vol 12 (3) ◽  
pp. 248-261
Author(s):  
Baomin Wang ◽  
Xiao Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temperature Rise ◽  
High Speed ◽  
Ball Bearing ◽  
Influence Factors ◽  
Ball Bearings ◽  
Ann Model ◽  
Angular Contact Ball Bearing ◽  
Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

scholarly journals Estimation of the Hot Swap Circulation Current of a Multiple Parallel Lithium Battery System with an Artificial Neural Network Model

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1448
Author(s):  
Nam-Gyu Lim ◽  
Jae-Yeol Kim ◽  
Seongjun Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Lithium Battery ◽  
Battery Pack ◽  
Battery Management ◽  
Ann Model ◽  
Battery System ◽  
Load Current ◽  
Circulating Current ◽  
Artificial Neural

Battery applications, such as electric vehicles, electric propulsion ships, and energy storage systems, are developing rapidly, and battery management issues are gaining attention. In this application field, a battery system with a high capacity and high power in which numerous battery cells are connected in series and parallel is used. Therefore, research on a battery management system (BMS) to which various algorithms are applied for efficient use and safe operation of batteries is being conducted. In general, maintenance/replacement of multi-series/multiple parallel battery systems is only possible when there is no load current, or the entire system is shut down. However, if the circulating current generated by the voltage difference between the newly added battery and the existing battery pack is less than the allowable current of the system, the new battery can be connected while the system is running, which is called hot swapping. The circulating current generated during the hot-swap operation is determined by the battery’s state of charge (SOC), the parallel configuration of the battery system, temperature, aging, operating point, and differences in the load current. Therefore, since there is a limit to formulating a circulating current that changes in size according to these various conditions, this paper presents a circulating current estimation method, using an artificial neural network (ANN). The ANN model for estimating the hot-swap circulating current is designed for a 1S4P lithium battery pack system, consisting of one series and four parallel cells. The circulating current of the ANN model proposed in this paper is experimentally verified to be able to estimate the actual value within a 6% error range.

Modeling of lime production process using artificial neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolghasem Daeichian ◽  
Rana Shahramfar ◽  
Elham Heidari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
High Energy ◽  
Ann Model ◽  
Artificial Neural ◽  
Rotary Kilns ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

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