Shear Stress Prediction Using FEA-ANN Hybrid Modeling Of Eicher 11.10 Chassis Frame

Patel, Tushar M.; Bhatt, Nilesh M.

doi:10.9790/1684-0922232

Cited by 3 publications

(8 citation statements)

References 5 publications

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“…Target data files include targets (normalized measured shear stress values respectively of input data sets) for training, validation and testing data sets. The work in this paper included a function approximation or prediction problem that required the final error to be reduced to a very small value [7,8]. The training stopped after 1000 epochs because the validation error increased.…”

Section: Fig 4 Abbreviated View Of Lm20tp Model In Matlab Windowmentioning

confidence: 99%

“…1 shows a flowchart for comparison of ANN and MLR model. Both modes are compared on the basis of error [5,7]. …”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…The result here is reasonable, because the test set error and the validation set error have similar characteristics, and it doesn't appear that any significant over fitting has occurred. After initial training of LM20TP model, it was retrained for epochs and performance MSE was obtained 2.53e-13 in training which took about 10 minutes on the test laptop [7,9]. Fig.…”

Section: Fig 4 Abbreviated View Of Lm20tp Model In Matlab Windowmentioning

confidence: 99%

“…Attempts have been made to study the network performance with a different number of hidden neurons. A network is constructed each of them is trained separately, and the best network is selected based on the accuracy of the predictions in the testing phase [7,9]. Fig.…”

Section: Ann Modelmentioning

confidence: 99%

“…4. A network of two transfer function, where the first transfer function is tansig and the second transfer function is purelin, can be trained to approximate any function [7,10].…”

Section: Ann Modelmentioning

confidence: 99%

See 4 more Smart Citations

ANN and MLR Model of Specific Fuel Consumption for Pyrolysis Oil Blended with Diesel used in a Single Cylinder Diesel Engine: A Comparative Study

Patel¹,

Brahmbhatt²

2015

IJCA

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The main objective of the research is to compare the accurateness of Artificial Neural Networks (ANN) and Multiple Linear Regressions (MLR) model for Specific Fuel Consumption for pyrolysis oil blended with diesel used in a single cylinder diesel engine. In this study, parameters i.e. Injection Timing, Injection Pressure, Compression Ratio, and Load are taken. Artificial Neural Networks (ANN) and Multiple Linear Regressions (MLR) models were prepared using the results of Experiments to predict Specific Fuel Consumption for pyrolysis oil blended with diesel used in a single cylinder diesel engine. The results show that ANN prediction is more accurate than MLR prediction.

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Section: Fig 4 Abbreviated View Of Lm20tp Model In Matlab Windowmentioning

confidence: 99%

“…1 shows a flowchart for comparison of ANN and MLR model. Both modes are compared on the basis of error [5,7]. …”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Fig 4 Abbreviated View Of Lm20tp Model In Matlab Windowmentioning

confidence: 99%

Section: Ann Modelmentioning

confidence: 99%

“…4. A network of two transfer function, where the first transfer function is tansig and the second transfer function is purelin, can be trained to approximate any function [7,10].…”

Section: Ann Modelmentioning

confidence: 99%

See 3 more Smart Citations

ANN and MLR Model of Specific Fuel Consumption for Pyrolysis Oil Blended with Diesel used in a Single Cylinder Diesel Engine: A Comparative Study

Patel¹,

Brahmbhatt²

2015

IJCA

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A machine learning approach to predict the stress results of quadratic tetrahedral elements

Mahesh¹,

Marco

Sharma³

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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Industries are always looking for an effective and efficient way to reduce the computation time of simulation because of the huge expenditure involved. From basics of Finite Element Method (FEM), it is known that, linear order finite elements consume less computation time and are less accurate compared to higher order finite elements say quadratic elements. An approach to get the benefit of less computation cost of linear elements and the good accuracy of quadratic elements can be of a good thought. The methodology to get the accurate results of quadratic elements with the advantage of less simulation run time of linear elements is presented here. Machine Learning (ML) algorithms are found to be effective in making predictions based on some known data set. The present paper discusses a methodology to implement ML model to predict the results equivalent to that of quadratic elements based on the solutions obtained from the linear elements. Here, a ML model is developed using python code, the stress results from Finite Element (FE) model of linear tetrahedral elements is given as the input to it to predict the stress results of quadratic tetrahedral elements. Abaqus is used as the FEM tool to develop the FE models. A python script is used to extract the stresses and the corresponding node numbers. The results showed that the developed ML model is successful in prediction of the accurate stress results for the set of test data. The scatter plots showed that the Z-score method was effective in removing the singularities. The proposed methodology is effective to reduce the computation time for simulation.

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Use of the stochastic frontier and the grey relational analysis in robust design of multi-objective problems

Rezgui

Trabelsi

2020

Concurrent Engineering

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A generic procedure for robust design in developing products and processes, which is referred to as RDPP-SF has been proposed. The method uses the stochastic frontier model to encompass both stochastic noise (e.g. manufacturing unit-to-unit variation, and measurement errors) and special-cause variation (e.g. environment, customer use, wearing, and deterioration noises). Even then, the RDPP-SF method has fallen short of tackling robust design of multi-objective problems, and its applicability is restrained to the performance characteristics of magnitude type (i.e., “the larger is the better” or “the smaller is the better”). Aiming at these limitations, the article seeks to address the robust design of the multi-objective problems using the RDPP-SF method. This is performed by reassessing the procedural scheme of the RDPP-SF method and the statistical significance of the hypothesis test (H0: γ = 0 vs H1: γ > 0) at 5% level. Depending on the statistical significance of the test (H0: γ = 0 vs H1: γ > 0), the arrays of the extrinsic and/or the intrinsic noise insensitivity scores are assigned to the grey relational analysis matrix as performance measures. The most robust design solution for the multi-objective problem is then obtained by sorting the overall grey relational grades. The amended RDPP-SF method is finally demonstrated using three industrial multi-objective case studies.

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Shear Stress Prediction Using FEA-ANN Hybrid Modeling Of Eicher 11.10 Chassis Frame

Cited by 3 publications

References 5 publications

ANN and MLR Model of Specific Fuel Consumption for Pyrolysis Oil Blended with Diesel used in a Single Cylinder Diesel Engine: A Comparative Study

ANN and MLR Model of Specific Fuel Consumption for Pyrolysis Oil Blended with Diesel used in a Single Cylinder Diesel Engine: A Comparative Study

A machine learning approach to predict the stress results of quadratic tetrahedral elements

Use of the stochastic frontier and the grey relational analysis in robust design of multi-objective problems

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