Modeling of process parameters to predict the efficiency of shallots stem cutting machine using multiple regression and artificial neural network

The artificial neural network and multiple regression models have been developed to predict the evenness of cotton ring yarn with process parameters such as front roller speed, spindle speed, nip gauge, back draft zone time and roving twist. The efficiencies of prediction of the two models have been experimentally verified, and the predicted evennesses of cotton ring yarns from both the models have been compared statistically. An attempt has been made to study the effect of process parameters on yarn evenness. The MSE and mean absolute error of ANN modelare lower than that of multiple regression model. The results show that the performances of prediction of ANN models are more accurate than those of multiple regression models.

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Tri-objective constrained optimization of pulsating DC sourced magnetic abrasive finishing process parameters using artificial neural network and genetic algorithm

Materials and Manufacturing Processes ◽

10.1080/10426914.2020.1866196 ◽

2021 ◽

pp. 1-15

Author(s):

Shadab Ahmad ◽

Ranganath M. Singari ◽

R.S. Mishra

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Constrained Optimization ◽

Process Parameters ◽

Magnetic Abrasive Finishing ◽

Finishing Process ◽

Abrasive Finishing ◽

Artificial Neural

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Artificial neural network for modeling and investigating the effects of forming tool characteristics on the accuracy and formability of thin aluminum alloy blanks when using SPIF

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-021-06712-4 ◽

2021 ◽

Author(s):

Sherwan Mohammed Najm ◽

Imre Paniti

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Process Parameters ◽

Single Point ◽

Tool Material ◽

Deformation Mode ◽

Geometric Accuracy ◽

Forming Process ◽

Tool Shape ◽

Artificial Neural

AbstractIncremental Sheet Forming (ISF) has attracted attention due to its flexibility as far as its forming process and complexity in the deformation mode are concerned. Single Point Incremental Forming (SPIF) is one of the major types of ISF, which also constitutes the simplest type of ISF. If sufficient quality and accuracy without defects are desired, for the production of an ISF component, optimal parameters of the ISF process should be selected. In order to do that, an initial prediction of formability and geometric accuracy helps researchers select proper parameters when forming components using SPIF. In this process, selected parameters are tool materials and shapes. As evidenced by earlier studies, multiple forming tests with different process parameters have been conducted to experimentally explore such parameters when using SPIF. With regard to the range of these parameters, in the scope of this study, the influence of tool material, tool shape, tool-end corner radius, and tool surface roughness (Ra/Rz) were investigated experimentally on SPIF components: the studied factors include the formability and geometric accuracy of formed parts. In order to produce a well-established study, an appropriate modeling tool was needed. To this end, with the help of adopting the data collected from 108 components formed with the help of SPIF, Artificial Neural Network (ANN) was used to explore and determine proper materials and the geometry of forming tools: thus, ANN was applied to predict the formability and geometric accuracy as output. Process parameters were used as input data for the created ANN relying on actual values obtained from experimental components. In addition, an analytical equation was generated for each output based on the extracted weight and bias of the best network prediction. Compared to the experimental approach, analytical equations enable the researcher to estimate parameter values within a relatively short time and in a practicable way. Also, an estimate of Relative Importance (RI) of SPIF parameters (generated with the help of the partitioning weight method) concerning the expected output is also presented in the study. One of the key findings is that tool characteristics play an essential role in all predictions and fundamentally impact the final products.

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The Artificial Neural Network and Regression Analysis Approach to Prediction of the Fiber Diameter of Spunbonding Fabrics

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.33.74 ◽

2010 ◽

Vol 33 ◽

pp. 74-78

Author(s):

B. Zhao

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Regression Model ◽

Network Model ◽

Process Parameters ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Fiber Diameter ◽

Statistical Regression ◽

Artificial Neural

In this work, the artificial neural network model and statistical regression model are established and utilized for predicting the fiber diameter of spunbonding nonwovens from the process parameters. The artificial neural network model has good approximation capability and fast convergence rate, which is used in this research. The results show the artificial neural network model can provide quantitative predictions of fiber diameter and yield more accurate and stable predictions than the statistical regression model, which reveals that the artificial neural network model is based on the inherent principles, and it can yield reasonably good prediction results and provide insight into the relationship between process parameters and fiber diameter.

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Prediction Models of Air Outlet States of Desiccant Wheels Using Multiple Regression and Artificial Neural Network Methods Based on Criterion Numbers

Applied Thermal Engineering ◽

10.1016/j.applthermaleng.2021.117940 ◽

2021 ◽

pp. 117940

Author(s):

Xianzhong Chen ◽

Rang Tu ◽

Ming Li ◽

Xu Yang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Multiple Regression ◽

Prediction Models ◽

Network Methods ◽

Artificial Neural

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Identification Accuracy of Additional Wave Resistance Through a Comparison of Multiple Regression and Artificial Neural Network Methods

Multidisciplinary Aspects of Production Engineering ◽

10.2478/mape-2018-0026 ◽

2018 ◽

Vol 1 (1) ◽

pp. 197-204

Author(s):

Tomasz Cepowski

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Multiple Regression ◽

Nonlinear Models ◽

Linear Equations ◽

Regression Method ◽

Wave Resistance ◽

Identification Accuracy ◽

Linear Regression Method ◽

Artificial Neural

Abstract The article presents the use of multiple regression method to identify added wave resistance. Added wave resistance was expressed in the form of a four-state nominal function of: “thrust”, “zero”, “minor” and “major” resistance values. Three regression models were developed for this purpose: a regression model with linear variables, nonlinear variables and a large number of nonlinear variables. The nonlinear models were developed using the author's algorithm based on heuristic techniques. The three models were compared with a model based on an artificial neural network. This study shows that non-linear equations developed through a multiple linear regression method using the author’s algorithm are relatively accurate, and in some respects, are more effective than artificial neural networks.

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Modelling of Rotary EDM Process Parameters of Inconel 718 Using Artificial Neural Networks

Mechanika ◽

10.5755/j01.mech.26.6.20484 ◽

2020 ◽

Vol 26 (6) ◽

pp. 540-544

Author(s):

Jayaraj JEEVAMALAR ◽

Sundaresan RAMABALAN ◽

Chinnamuthu SENTHILKUMAR

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Process Parameters ◽

Inconel 718 ◽

Machining Process ◽

Artificial Neural ◽

Pulse On Time ◽

Pulse Off Time ◽

Matlab Programming ◽

The Relationship

Modelling is used for correlating the relationship between the input process parameters and the output responses during the machining process. To characterize real-world systems of considerable complexity, an Artificial Neural Network (ANN) model is regularly used to replace the mathematical approximation of the relationship. This paper explains the methodological procedure and the outcome of the ANN modeling process for Electrical Discharge Drilling of Inconel 718 superalloy and hollow tubular copper as tool electrode. The most important process parameters in this work are peak current, pulse on time and pulse off time with machining performances of material removal rate and surface roughness. The experiments were performed by L20 Orthogonal Array. In such conditions, an Artificial Neural Network model is developed using MATLAB programming on the Feed Forward Back Propagation technique was used to predict the responses. The experimental data were separated into three parts to train, test the network and validate the model. The developed model has been confirmed experimentally for training and testing in considering the number of iterations and mean square error convergence criteria. The developed model results are to approximate the responses fairly exactly. The model has the mean correlation coefficient of 0.96558. Results revealed that the proposed model can be used for the prediction of the complex EDM drilling process.

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