Construction of three-dimensional extrusion limit diagram for magnesium alloy using artificial neural network and its validation

Bai, Shuo; Fang, Gang; Zhou, Jie

doi:10.1016/j.jmatprotec.2019.116361

Cited by 14 publications

(3 citation statements)

References 28 publications

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“…Each processor in the network only deals with the signals it receives and sends to other processors. However, when connected into a large enough network with controlled interaction, these individually simple processors can perform rather complicated tasks [32].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Hardware-Software Processing of High-Frequency Scanning Data

Mukanova,

Atanov,

Jamshidi

2023

Journal of Robotics and Control

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The constant emission of polluting gases is causing an urgent need for timely detection of harmful gas mixtures in the atmosphere. A method and algorithm of the determining spectral composition of gas with a gas analyzer using an artificial neural network (ANN) were suggested in the article. A small closed gas dynamic system was designed and used as an experimental bench for collecting and quantifying gas concentrations for testing the proposed method. This device was based on AS7265x and BMP180 sensors connected in parallel to a 3.3 V compatible Arduino Uno board via QWIIC. Experimental tests were conducted with air from the laboratory room, carbon dioxide (CO2), and a mixture of pure oxygen (O2) with nitrogen (N2) in a 9:1 ratio. Three ANNs with one input, one hidden and one output layer were built. The ANN had 5, 10, and 20 hidden neurons, respectively. The dataset was divided into three parts: 70% for training, 15% for validation, and 15% for testing. The mean square error (MSE) error and regression were analyzed during training. Training, testing, and validation error analysis were performed to find the optimal iteration, and the MSE versus training iteration was plotted. The best indicators of training and construction were shown by the ANN with 5 (five) hidden layers, and 16 iterations are enough to train, test and verify this neural network. To test the obtained neural network, the program code was written in the MATLAB. The proposed scheme of the gas analyzer is operable and has a high accuracy of gas detection with a given error of 3%. The results of the study can be used in the development of an industrial gas analyzer for the detection of harmful gas mixtures.

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Section: Related Workmentioning

confidence: 99%

Intelligent Hardware-Software Processing of High-Frequency Scanning Data

Mukanova,

Atanov,

Jamshidi

2023

Journal of Robotics and Control

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“…Due to the different unit dimensions of forming process parameters such as extrusion temperature, extrusion ratio, the angle of extrusion die and bonding strength, the data needed to be normalized, which could avoid the influence of dimensional changes on BP network model. In this paper, all the input layer and output layer data were concentrated in the [0, 1] interval, the specific transformation formula (2) [23]was as follows :…”

Section: Prediction Based On Bp Neural Network Modelmentioning

confidence: 99%

Accurate prediction of the extrusion forming bonding reliability for heterogeneous welded sheets based on GA-BP neural network

Gao

Huo

et al. 2021

Int J Adv Manuf Technol

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As a widely recognized optimization method, BP neural network can provide scientific guidance for the formulation of reasonable process parameters. However, due to the randomness of its own weights and thresholds, the prediction accuracy remains to be further improved. The forming and manufacturing of heterogeneous welded sheet is a new extrusion connection method. There are many factors affecting the bonding quality, which brings trouble to the evaluation of bonding strength and quality. In this paper, orthogonal experiment, finite element simulation and process experiment were used to design and verify the key process parameters that affected the bonding strength of heterogeneous sheets. BP neural network and genetic algorithm neural network were used to predict the bonding strength. The results showed that the genetic algorithm neural network model has higher reliability, and the prediction accuracy was 99.5 %. Compared with the traditional BP neural network, the prediction accuracy was improved by 5.78 %, and the error was reduced to 0.5 %. It has good generalization ability, and provides a new way for intelligent reliability evaluation of high performance heterogeneous sheets extrusion manufacturing.

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“…Intelligent dynamic systems, such as ANNs, have been under researchers' focus recently [44][45][46][47][48][49][50][51]. ANNs are able to identify the relationship among data by analyzing them and to then exploit this relationship in further analyses [52].…”

Section: Predicting Earned Value Using Artificial Neural Networkmentioning

confidence: 99%

Improving the Results of the Earned Value Management Technique Using Artificial Neural Networks in Construction Projects

et al. 2020

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The cost, time and scope of a construction project are key parameters for its success. Thus, predicting these indices is indispensable. Correct and accurate prediction of cost throughout the progress of a project gives project managers the chance to identify projects that need revision in their schedules in order to result in the maximum benefit. The aim of this study is to minimize the shortcomings of the Earned Value Management (EVM) method using an Artificial Neural Network (ANN) and multiple regression analysis in order to predict project cost indices more precisely. A total of 50 road construction projects in Fars Province, Iran, were selected for analysis in this research. An ANN model was used to predict the projects’ cost performance indices, thereby creating a more accurate symmetry between the predicted and actual cost by considering factors that influence project success. The input data of the ANN model were analysed in MATLAB software. A multiple regression model was also used as another analytical tool to validate the outcome of the ANN. The results showed that the ANN model resulted in a lower Mean Squared Error (MSE) and a greater correlation coefficient than both the traditional EVM model and the multiple regression model.

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Construction of three-dimensional extrusion limit diagram for magnesium alloy using artificial neural network and its validation

Cited by 14 publications

References 28 publications

Intelligent Hardware-Software Processing of High-Frequency Scanning Data

Intelligent Hardware-Software Processing of High-Frequency Scanning Data

Accurate prediction of the extrusion forming bonding reliability for heterogeneous welded sheets based on GA-BP neural network

Improving the Results of the Earned Value Management Technique Using Artificial Neural Networks in Construction Projects

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