scholarly journals Prediction of Cutting Conditions in Turning AZ61 and Parameters Optimization Using Regression Analysis and Artificial Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Nabeel H. Alharthi ◽  
Sedat Bingol ◽  
Adel T. Abbas ◽  
Adham E. Ragab ◽  
Mohamed F. Aly ◽  
...  
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Regression Analysis ◽  
Parameters Optimization ◽  
Cutting Conditions ◽  
Performance Criteria ◽  
Coefficient Of Determination ◽  
Ann Models ◽  
Artificial Neural

All manufacturing engineers are faced with a lot of difficulties and high expenses associated with grinding processes of AZ61. For that reason, manufacturing engineers waste a lot of time and effort trying to reach the required surface roughness values according to the design drawing during the turning process. In this paper, an artificial neural network (ANN) modeling is used to estimate and optimize the surface roughness (Ra) value in cutting conditions of AZ61 magnesium alloy. A number of ANN models were developed and evaluated to obtain the most successful one. In addition to ANN models, traditional regression analysis was also used to build a mathematical model representing the equation required to obtain the surface roughness. Predictions from the model were examined against experimental data and then compared to the ANN model predictions using different performance criteria such as the mean absolute error, mean square error, and coefficient of determination.

scholarly journals Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Nabeel H. Alharthi ◽  
Sedat Bingol ◽  
Adel T. Abbas ◽  
Adham E. Ragab ◽  
Ehab A. El-Danaf ◽  
...  
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Regression Analysis ◽  
Feed Rate ◽  
Experimental Tests ◽  
Surface Topology ◽  
Coefficient Of Determination ◽  
Depth Of Cut ◽  
Artificial Neural

In this paper artificial neural network (ANN) and regression analysis were used for the prediction of surface roughness. Five models of neural network were developed and the model that showed best fit with experimental results was with 6 neurons in the hidden layer. Regression analysis was also used to build a mathematical model representing the surface roughness as a function of the process parameters. The coefficient of determination was found to be 94.93% and 93.63%, for the best neural network model and regression analysis, respectively, from the comparison of the models with thirteen validation experimental tests. Optical microscopy was conducted on two machined surfaces with two different values of feed rates while maintaining the spindle speed and depth of cut at the same values. Examining the surface topology and surface roughness profile for the two surfaces revealed that higher feed rate results in relatively thick roughness markings that are distantly spaced, whereas low values of feed rate result in thin surface roughness markings that are closely spaced giving better surface finish.

An artificial neural network to estimate soil temperature

1997 ◽  
Vol 77 (3) ◽  
pp. 421-429 ◽  
Author(s):  
Chun-Chieh Yang ◽  
Shiv O. Prasher ◽  
Guy R. Mehuys
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Temperature ◽  
Potential Evapotranspiration ◽  
Daily Rainfall ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Soil Temperatures ◽  
Ann Models ◽  
Artificial Neural

This study was undertaken to develop an artificial neural network (ANN) model for transient simulation of soil temperature at different depths in the profile. The capability of ANN models to simulate the variation of temperature in soils was investigated by considering readily available meteorologic parameters. The ANN model was constructed by using five years of meteorologic data, measured at a weather station at the Central Experimental Farm in Ottawa, Ontario, Canada. The model inputs consisted of daily rainfall, potential evapotranspiration, and the day of the year. The model outputs were daily soil temperatures at the depths of 100, 500 and 1500 mm. The estimated values were found to be close to the measured values, as shown by a root-mean-square error ranging from 0.59 to 1.82 °C, a standard deviation of errors from 0.61 to 1.81 °C, and a coefficient of determination from 0.937 to 0.987. Therefore, it is concluded that ANN models can be used to estimate soil temperature by considering routinely measured meteorologic parameters. In addition, the ANN model executes faster than a comparable conceptual simulation model by several orders of magnitude. Key words: Artificial neural networks, soil temperature, precipitation, potential evapotranspiration

scholarly journals Prediction of Mechanical Strength of Fiber Admixed Concrete Using Multiple Regression Analysis and Artificial Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
S. Karthiyaini ◽  
K. Senthamaraikannan ◽  
J. Priyadarshini ◽  
Kamal Gupta ◽  
M. Shanmugasundaram
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Analysis ◽  
Mechanical Strength ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Mean Squared Error ◽  
Fiber Reinforced Concrete ◽  
Coefficient Of Determination ◽  
Artificial Neural

The present study is to compare the multiple regression analysis (MRA) model and artificial neural network (ANN) model designed to predict the mechanical strength of fiber-reinforced concrete on 28 days. The model uses the data from early literatures; the data consist of tensile strength of fiber, percentage of fiber, water/cement ratio, cross-sectional area of test specimen, Young’s modulus of fiber, and mechanical strength of control specimen, and these were used as the input parameters; the respective strength attained was used as the target parameter. The models are created and are used to predict compressive, split tensile, and flexural strength of fiber admixed concrete. These models are evaluated through the statistical test such as coefficient of determination (R2) and root mean squared error (RMSE). The results show that these parameters produce a valid model through both MRA and ANN, and this model gives more precise prediction for the fiber admixed concrete.

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.

Prediction of Surface Roughness in Magneto Rheological Abrasive Flow Finishing Process by Artificial Neural Networks and Regression Analysis

2015 ◽  
Vol 766-767 ◽  
pp. 1076-1084
Author(s):  
S. Kathiresan ◽  
K. Hariharan ◽  
B. Mohan
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Regression Analysis ◽  
Back Propagation ◽  
Hydraulic Pressure ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Magneto Rheological ◽  
Hidden Neurons ◽  
Abrasive Flow Finishing

In this study, to predict the surface roughness of stainless steel-304 in Magneto rheological Abrasive flow finishing (MRAFF) process, an artificial neural network (ANN) and regression models have been developed. In this models, the parameters such as hydraulic pressure, current to the electromagnet and number of cycles were taken as variables of the model.Taguchi’s technique has been used for designing the experiments in order to observe the different values of surface roughness . A neural network with feed forward with the help of back propagation was made up of 27 input neurons, 7 hidden neurons and one output neuron. The 6 sets of experiments were randomly selected from orthogonal array for training and residuals were used to analyze the performance. To check the validity of regression model and to determine the significant parameter affecting the surface roughness, Analysis of variance (ANOVA) andF-test were made. The numerical analysis depict that the current to the electromagnet was an paramount parameter on surface roughness.Key words: MRAFF, ANN, Regression analysis

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