Prediction of GMA Welding Characteristic Parameter by Artificial Neural Network System

Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.

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Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts

Mathematics ◽

10.3390/math9060626 ◽

2021 ◽

Vol 9 (6) ◽

pp. 626

Author(s):

Svajone Bekesiene ◽

Rasa Smaliukiene ◽

Ramute Vaicaitiene

Keyword(s):

Neural Network ◽

Transfer Functions ◽

Back Propagation ◽

Armed Forces ◽

Activation Function ◽

Optimal Number ◽

Ann Model ◽

Back Propagation Algorithm ◽

Classification Rate ◽

Artificial Neural

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.

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Prediction of Bead Geometry with Changing Welding Speed Using Artificial Neural Network

Materials ◽

10.3390/ma14061494 ◽

2021 ◽

Vol 14 (6) ◽

pp. 1494

Author(s):

Ran Li ◽

Manshu Dong ◽

Hongming Gao

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Welding Speed ◽

Gas Metal Arc Welding ◽

Welding Process ◽

Training Dataset ◽

Welding Parameters ◽

Bead Geometry ◽

Bead Width ◽

Artificial Neural

Bead size and shape are important considerations for industry design and quality detection. It is hard to deduce an appropriate mathematical model for predicting the bead geometry in a continually changing welding process due to the complex interrelationship between different welding parameters and the actual bead. In this paper, an artificial neural network model for predicting the bead geometry with changing welding speed was developed. The experiment was performed by a welding robot in gas metal arc welding process. The welding speed was stochastically changed during the welding process. By transient response tests, it was indicated that the changing welding speed had a spatial influence on bead geometry, which ranged from 10 mm backward to 22 mm forward with certain welding parameters. For this study, the input parameters of model were the spatial welding speed sequence, and the output parameters were bead width and reinforcement. The bead geometry was recognized by polynomial fitting of the profile coordinates, as measured by a structured laser light sensor. The results showed that the model with the structure of 33-6-2 had achieved high accuracy in both the training dataset and test dataset, which were 99% and 96%, respectively.

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A New Model for Predicting Rate of Penetration Using an Artificial Neural Network

Sensors ◽

10.3390/s20072058 ◽

2020 ◽

Vol 20 (7) ◽

pp. 2058 ◽

Cited By ~ 2

Author(s):

Salaheldin Elkatatny ◽

Ahmed Al-AbdulJabbar ◽

Khaled Abdelgawad

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mutual Effect ◽

Percentage Error ◽

Drilling Process ◽

Ann Model ◽

Data Set ◽

Individual Parameter ◽

Rate Of Penetration ◽

Artificial Neural

The drilling rate of penetration (ROP) is defined as the speed of drilling through rock under the bit. ROP is affected by different interconnected factors, which makes it very difficult to infer the mutual effect of each individual parameter. A robust ROP is required to understand the complexity of the drilling process. Therefore, an artificial neural network (ANN) is used to predict ROP and capture the effect of the changes in the drilling parameters. Field data (4525 points) from three vertical onshore wells drilled in the same formation using the same conventional bottom hole assembly were used to train, test, and validate the ANN model. Data from Well A (1528 points) were utilized to train and test the model with a 70/30 data ratio. Data from Well B and Well C were used to test the model. An empirical equation was derived based on the weights and biases of the optimized ANN model and compared with four ROP models using the data set of Well C. The developed ANN model accurately predicted the ROP with a correlation coefficient (R) of 0.94 and an average absolute percentage error (AAPE) of 8.6%. The developed ANN model outperformed four existing models with the lowest AAPE and highest R value.

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Dynamic Modeling of Streptomyces hygroscopicus Fermentation Broth Microfiltration by Artificial Neural Networks

Periodica Polytechnica Chemical Engineering ◽

10.3311/ppch.13866 ◽

2019 ◽

Author(s):

Aleksandar Jokić ◽

Nevenka Nikolić ◽

Nataša Lukić ◽

Jovana Grahovac ◽

Jelena Dodić ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Dynamic Modeling ◽

Fermentation Broth ◽

Optimal Number ◽

Streptomyces Hygroscopicus ◽

Transmembrane Pressure ◽

Ann Model ◽

Permeate Flux ◽

Artificial Neural

Artificial neural networks (ANNs) have been used to dynamically model cross-flow microfiltration of Streptomyces hygroscopicus fermentation broths. The aim is to predict permeate flux as a function of temperature, feed flow, transmembrane pressure and processing time. Dynamic modeling of microfiltration performance of complex systems (such as broths) is very important for design of new processes and better understanding of the present. The results of ANN model analysis suggest that the coefficients of the determination have high values. The application of the Bayesian regularization gave better results to the performance of the neural network compared to the Levenberg-Marquet algorithm. The optimal number of neurons in the hidden layer is eight. Analysis of the absolute relative error showed excellent permeate flux estimates for 100 % of the data points, with an error less than 5 % for the data obtained during microfiltration in the presence of a turbulence promoter. Whilst in the case of microfiltration without turbulence promoter 90 % of predictions have an error less than 10 %. The results of applying the concept of neural networks in the dynamic modeling of microfiltration of Streptomyces hygroscopicus fermentative broths with and without a turbulence promoter clearly show the validity of proposed method for simulation and prediction of microfiltration experimental results.

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A Comparative Study on Improved Arrhenius-Type and Artificial Neural Network Models to Predict High-Temperature Flow Behaviors in 20MnNiMo Alloy

The Scientific World JOURNAL ◽

10.1155/2014/108492 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 5

Author(s):

Guo-zheng Quan ◽

Chun-tang Yu ◽

Ying-ying Liu ◽

Yu-feng Xia

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

High Temperature ◽

Constitutive Model ◽

Relative Error ◽

Type Model ◽

Ann Model ◽

Data Set ◽

Average Absolute Relative Error ◽

Artificial Neural

The stress-strain data of 20MnNiMo alloy were collected from a series of hot compressions on Gleeble-1500 thermal-mechanical simulator in the temperature range of 1173∼1473 K and strain rate range of 0.01∼10 s−1. Based on the experimental data, the improved Arrhenius-type constitutive model and the artificial neural network (ANN) model were established to predict the high temperature flow stress of as-cast 20MnNiMo alloy. The accuracy and reliability of the improved Arrhenius-type model and the trained ANN model were further evaluated in terms of the correlation coefficient (R), the average absolute relative error (AARE), and the relative error (η). For the former,Rand AARE were found to be 0.9954 and 5.26%, respectively, while, for the latter, 0.9997 and 1.02%, respectively. The relative errors (η) of the improved Arrhenius-type model and the ANN model were, respectively, in the range of −39.99%∼35.05% and −3.77%∼16.74%. As for the former, only 16.3% of the test data set possessesη-values within±1%, while, as for the latter, more than 79% possesses. The results indicate that the ANN model presents a higher predictable ability than the improved Arrhenius-type constitutive model.

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Dengue Outbreak Prediction Based on Artificial Neural Networking Model Using Climatic Parameters

10.21203/rs.3.rs-86587/v1 ◽

2020 ◽

Author(s):

Biplab Ghosh ◽

Monika Soni

Keyword(s):

Dengue Fever ◽

Meteorological Parameters ◽

Ann Model ◽

Data Set ◽

Long Run ◽

Modelling Techniques ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

And Control ◽

The Relationship

Abstract Background: Dengue fever is a vector-borne tropical disease radically amplified by 30 times in occurrence between 1960 and 2010. The upsurge is considered to be because of urbanization, population growth and climate change. Therefore, Meteorological parameters (temperature, precipitation and relative humidity) have impact on the occurrence and outbreaks of dengue fever. There are not many studies that enumerate the relationship between the dengue cases in a particular locality and the meteorological parameters. This study explores the relationship between the dengue cases and the meteorological parameters. In prevalent localities, it is essential to alleviate the outbreaks using modelling techniques for better disease control.Methods: An artificial neural network (ANN) model was developed for predicting the number of dengue cases by knowing the meteorological parameters. The model was trained with 7 years of dengue fever data of Kamrup and Lakhimpur district of Assam, India. The practicality of the model was corroborated using independent data set with satisfactory outcomes. Findings: It was apparent from the sensitivity analysis that precipitation is more sensitive to the number of dengue cases than other meteorological parameters. Conclusion: This model would assist dengue fever alleviation and control in the long run.

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Modeling Baseline Energy Using Artificial Neural Network – A Small Dataset Approach

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v12.i2.pp662-669 ◽

2018 ◽

Vol 12 (2) ◽

pp. 662

Author(s):

Wan Nazirah Wan Md Adnan ◽

Nofri Yenita Dahlan ◽

Ismail Musirin

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Cross Validation ◽

Model Development ◽

Energy Model ◽

Ann Model ◽

Data Set ◽

Validation Technique ◽

Artificial Neural ◽

Small Dataset

In this work, baseline energy model development using Artificial Neural Network (ANN) with resampling techniques; Cross Validation (CV) and Bootstrap (BS) are presented. Resampling techniques are used to examine the ability of the ANN model to deal with a small dataset. Working days, class days and Cooling Degree Days (CDD) are used as ANN input meanwhile the ANN output is monthly electricity consumption. The coefficient of correlation (R) is used as performance function to evaluate the model accuracy. For this analysis, R is calculated for the entire data set (R_all) and separately for training set (R_train), validation set (R_valid) dan testing set (R_test). The closer R to 1, the higher similarities between targeted and predicted output. The total of two different models with several number of neurons are developed and compared. It can be concluded that all models are capable to train the network. Artificial Neural Network with Bootstrap Cross Validation technique (ANN-BSCV) outperforms Artificial Neural Network with Cross Validation technique (ANN-CV). The 3-6-1 ANN-BSCV, with R_train = 0.95668, R_valid = 0.97553, R_test = 0.85726 and R_all = 0.94079 is selected as the baseline energy model to predict energy consumption for Option C IPMVP.

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Artificial neural networks predictive models. A case study: carbon and bromine concentrations prediction based on chlorination time

Global NEST Journal ◽

10.30955/gnj.000813 ◽

2013 ◽

Vol 14 (1) ◽

pp. 10-17

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Potable Water ◽

Back Propagation ◽

Ann Model ◽

Data Set ◽

Test Set ◽

Feed Forward Back Propagation ◽

Artificial Neural ◽

Comparison Of The Results

Artificial neural networks (ANNs) are being used increasingly to predict water variables. This study offers an alternative approach to quantify the relationship between time of chlorination in potable water (due to convectional treatment procedure) and chlorination by-products concentration (expressed as carbon and bromine) with an ANN model, i.e., capturing non-linear relationships among the water quality variables. Thus, carbon and bromine concentrations in potable water (the second chosen due to the toxicity of brominated trihalomethanes, THMs) were predicted using artificial neural networks (ANNs) based mainly on multi-layer perceptrons (MLPs) architecture. The chlorination (detention) time as much as 58 hours in Athens distributed network, comprised the input variables to the ANNs models. Moreover, to develop an ANN model for estimating carbon and bromine, the available data set was partitioned into training, validation and test set. In order to reach an optimum amount of hidden layers or nodes, different architectures were tested. The quality of the ANN simulations was evaluated in terms of the error in the validation sample set for the proper interpretation of the results. The calculated sum-squared errors for training, validation and test set were 0.056, 0.039 and 0.060 respectively for the best model selected. Comparison of the results showed that a two-layer feed-forward back propagation ANN model could be used as an acceptable model for predicting carbon and bromine contained in potable water THMs.

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Artificial Neural Network Model for Managing and Forecasting Water Reservoir Discharge (Hemren Reservoir as A Case Study)

Diyala Journal of Engineering Sciences ◽

10.24237/djes.2014.07409 ◽

2014 ◽

Vol 7 (4) ◽

pp. 132-143

Author(s):

ABBAS M. ABD ◽

SAAD SH. SAMMEN

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Correlation Coefficient ◽

Mean Squared Error ◽

Water Reservoir ◽

Prediction Method ◽

Training Data ◽

Ann Model ◽

Data Set ◽

Artificial Neural

The prediction of different hydrological phenomenon (or system) plays an increasing role in the management of water resources. As engineers; it is required to predict the component of natural reservoirs’ inflow for numerous purposes. Resulting prediction techniques vary with the potential purpose, characteristics, and documented data. The best prediction method is of interest of experts to overcome the uncertainty, because the most hydrological parameters are subjected to the uncertainty. Artificial Neural Network (ANN) approach has adopted in this paper to predict Hemren reservoir inflow. Available data including monthly discharge supplied from DerbendiKhan reservoir and rain fall intensity falling on the intermediate catchment area between Hemren-DerbendiKhan dams were used.A Back Propagation (LMBP) algorithm (Levenberg-Marquardt) has been utilized to construct the ANN models. For the developed ANN model, different networks with different numbers of neurons and layers were evaluated. A total of 24 years of historical data for interval from 1980 to 2004 were used to train and test the networks. The optimum ANN network with 3 inputs, 40 neurons in both two hidden layers and one output was selected. Mean Squared Error (MSE) and the Correlation Coefficient (CC) were employed to evaluate the accuracy of the proposed model. The network was trained and converged at MSE = 0.027 by using training data subjected to early stopping approach. The network could forecast the testing data set with the accuracy of MSE = 0.031. Training and testing process showed the correlation coefficient of 0.97 and 0.77 respectively and this is refer to a high precision of that prediction technique.

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Validation of evaporation-determined model of arc-cathode coupling in the peak current phase in pulsed GMA welding

Journal of Physics D Applied Physics ◽

10.1088/1361-6463/ac3daa ◽

2021 ◽

Author(s):

Marek Sebastian Simon ◽

Oleg Mokrov ◽

Rahul Sharma ◽

Uwe Reisgen ◽

Guokai Zhang ◽

...

Keyword(s):

Surface Temperature ◽

Weld Pool ◽

Cathode Surface ◽

Gma Welding ◽

Gas Metal Arc Welding ◽

Welding Process ◽

Qualitative Reasoning ◽

Current Phase ◽

Temperature Plasma ◽

Peak Current

Abstract A first experimental validation of the EDACC (evaporation-determined arc-cathode coupling) model is performend by comparing the experimental and simulated current in the peak current phase of a pulsed GMAW (gas metal arc welding) process. For this, the EDACC model was extended to limit the cathode surface temperature to a realistic value of <2400K. The information on the plasma for the EDACC model was gathered from literature and extrapolated and extended according to qualitative reasoning. The information on the cathode surface of the EDACC model was derived from a steady-state simulation of the weld pool, using an averaging approach over time for the energy and current. The weld pool surface temperature was compared to pyrometric measurements, that were performed for this work, and the agreement was found to be fair. The observed agreement between the modelled and experimentally determined current was within 10%. As strong assumptions were made for the comparison, the validation cannot be considered as final, but the assumptions are thoroughly analyzed and discussed. However the critical link between surface temperature, plasma temperature and total current transmitted could be reconstructed.

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