Analysis and synthesis of laser forming process using neural networks and neuro-fuzzy inference system

Maji, Kuntal; Pratihar, Dilip Kumar; Nath, Ashish Kumar

doi:10.1007/s00500-012-0949-7

Cited by 29 publications

(5 citation statements)

References 36 publications

(34 reference statements)

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“…Some of the complex engineering problems with the application of ANN were reported by researcher Fetane et al, Farshad et al, Maji et al, and Naqvi et al and had proved that ANN‐predicted solutions are of good accuracy.…”

Section: Introductionmentioning

confidence: 97%

Dynamic wind response of tall buildings using artificial neural network

Nikose

Sonparote

2019

Structural Design Tall Build

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Summary This paper presents an alternative approach for predicting the dynamic wind response of tall buildings using artificial neural network (ANN). The ANN model was developed, trained, and validated based on the data generated in the context of Indian Wind Code (IWC), IS 875 (Part 3):2015. According to the IWC, dynamic wind responses can be calculated for a specific configuration of buildings. The dynamic wind loads and their corresponding responses of structures other than the specified configurations in IWC have to be estimated by wind tunnel tests or computational techniques, which are expensive and time intensive. Alternatively, ANN is an efficient and economical computational analysis tool that can be implemented to estimate the dynamic wind response of a building. In this paper, ANN models were developed to predict base shear and base bending moment of a tall building in along‐ and across‐wind direction by giving the input as the configuration of the building, wind velocity, and terrain category. Multilayer perceptron ANN models with back‐propagation training algorithm was adopted. On comparison of results, it was found that the predicted values obtained from the ANN models and the calculated responses acquired using IWC standards are almost similar. Using the best fit model of ANN, an extensive parametric study was performed to predict the dynamic wind response of tall buildings for the configurations on which IWC is silent. Based on the results obtained from this study, design charts are developed for the prediction of dynamic wind response of tall buildings.

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Section: Introductionmentioning

confidence: 97%

Dynamic wind response of tall buildings using artificial neural network

Nikose

Sonparote

2019

Structural Design Tall Build

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“…However, due to the huge data required for prediction by the GA, it cannot be used by itself, and the GA is usually used with the combination of ANN or neuro-fuzzy systems. Maji et al [14][15][16] compared the effectiveness and performance of the GA with ANN (GA-NN) and an adaptive neuro-fuzzy inference system (GA-ANFIS) in laser bending. A batch of experimental data was used to assess both approaches.…”

Section: Introductionmentioning

confidence: 99%

Developing a Support Vector Regression (SVR) Model for Prediction of Main and Lateral Bending Angles in Laser Tube Bending Process

2023

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The laser tube bending process (LTBP) is a new and powerful manufacturing method for bending tubes more accurately and economically by eliminating the bending die. The irradiated laser beam creates a local plastic deformation area, and the bending of the tube occurs depending on the magnitude of the heat absorbed by the tube and its material characteristics. The main bending angle and lateral bending angle are the output variables of the LTBP. In this study, the output variables are predicted by support vector regression (SVR) modeling, which is an effective methodology in machine learning. The SVR input data is provided by performing 92 experimental tests determined by the design of the experimental techniques. The measurement results are divided into two sub-datasets: 70% for the training dataset, and 30% for the testing dataset. The inputs of the SVR model are process parameters, which can be listed as the laser power, laser beam diameter, scanning speed, irradiation length, irradiation scheme, and the number of irradiations. Two SVR models are developed for the prediction of the output variables separately. The SVR predictor achieved a mean absolute error of 0.021/0.003, a mean absolute percentage error of 1.485/1.849, a root mean square error of 0.039/0.005, and a determination factor of 93.5/90.8% for the main/lateral bending angle. Accordingly, the SVR models prove the possibility of applying SVR to the prediction of the main bending angle and lateral bending angle in LTBP with quite an acceptable accuracy.

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“…Application of ANN to solve some of the complex engineering problems has been reported in the research article of Fetene et al, [ 6 ] Farshad et al, [ 7 ] Maji et al, [ 8,9 ] and Naqvi et al [ 10 ] and had verified that ANN predicted solutions are of excellent precision with minimum error.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural network for predicting dynamic along‐wind response of tall buildings

Nikose

Sonparote

2021

Structural Design Tall Build

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Summary This paper presents the simplified approach based on the artificial neural network (ANN) for predictions of the dynamic along‐wind response in the context of the Indian Wind Standard IS 875 (Part 3): 2015. This standard provides direct guidelines for the specific aspect ratio of buildings (h/b ratio as 3, 5, 10, and 20) for assessing along‐wind loads. Evaluation of dynamic wind load and its responses, for the aspect ratio not specified in the IS 875 (Part 3): 2015, may have to be estimated by the boundary layer wind tunnel experiment or other computational techniques. Both the experimentation and computational methods are expensive in terms of time and the resources required. Alternatively, ANN is an efficient and cost‐effective computational technique that can be employed to estimate the dynamic wind response of a building. In this paper, ANN predicted estimations are presented for calculating dynamic along‐wind base shear and base bending moment of a tall building. The response is generated using the best fit ANN model for the aspect ratio (h/b) ranging in between 3 and 20. Finally, these responses were used to propose a simplified empirical equation as a function of aspect ratio, side ratio, wind velocity, and terrain category. Furthermore, charts were developed for calculating the story shear force and story bending moments for the building heights varying from 100 to 250 m. The proposed new approach is one of the quick evaluators for dynamic along‐wind response of the tall buildings.

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Analysis and synthesis of laser forming process using neural networks and neuro-fuzzy inference system

Cited by 29 publications

References 36 publications

Dynamic wind response of tall buildings using artificial neural network

Dynamic wind response of tall buildings using artificial neural network

Developing a Support Vector Regression (SVR) Model for Prediction of Main and Lateral Bending Angles in Laser Tube Bending Process

Application of artificial neural network for predicting dynamic along‐wind response of tall buildings

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