Comparison of Artificial Neural Network Model and Response Surface Methodology for Springback Prediction
In sheet metal manufacturing, the ability to predict failures, such as springback, wrinkling and thinning, are of high importance. The objective of this study is to compare the response surface methodology (RSM) and the artificial neural network (ANN) model for predicting springback during the deep...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2021-01-01
|
| Series: | MATEC Web of Conferences |
| Online Access: | https://www.matec-conferences.org/articles/matecconf/pdf/2021/12/matecconf_mse21_03012.pdf |
| _version_ | 1818585054696177664 |
|---|---|
| author | El Mrabti Iliass Touache Abdelhamid El Hakimi Abdelhadi Chamat Abderahim |
| author_facet | El Mrabti Iliass Touache Abdelhamid El Hakimi Abdelhadi Chamat Abderahim |
| author_sort | El Mrabti Iliass |
| collection | DOAJ |
| description | In sheet metal manufacturing, the ability to predict failures, such as springback, wrinkling and thinning, are of high importance. The objective of this study is to compare the response surface methodology (RSM) and the artificial neural network (ANN) model for predicting springback during the deep drawing process. In this investigation, friction coefficient, punch speed and blank holder force were considered as input variables. Sample data were planned by the complete factorial design and obtained via numerical simulation. To compare the RSM and ANN models, a goodness of-fit test was performed. The results of the two methods are promising and it is found that the ANN results are more accurate than the RSM results. |
| first_indexed | 2024-12-16T08:30:58Z |
| format | Article |
| id | doaj.art-bfe707399e5848cd9383e03a5e50bfb8 |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-12-16T08:30:58Z |
| publishDate | 2021-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-bfe707399e5848cd9383e03a5e50bfb82022-12-21T22:37:53ZengEDP SciencesMATEC Web of Conferences2261-236X2021-01-013430301210.1051/matecconf/202134303012matecconf_mse21_03012Comparison of Artificial Neural Network Model and Response Surface Methodology for Springback PredictionEl Mrabti Iliass0Touache Abdelhamid1El Hakimi Abdelhadi2Chamat Abderahim3Mechanical Engineering Laboratory, Sidi Mohamed Ben Abdellah universityMechanical Engineering Laboratory, Sidi Mohamed Ben Abdellah universityMechanical Engineering Laboratory, Sidi Mohamed Ben Abdellah universityIndustrial Techniques Laboratory, Sidi Mohamed Ben Abdellah universityIn sheet metal manufacturing, the ability to predict failures, such as springback, wrinkling and thinning, are of high importance. The objective of this study is to compare the response surface methodology (RSM) and the artificial neural network (ANN) model for predicting springback during the deep drawing process. In this investigation, friction coefficient, punch speed and blank holder force were considered as input variables. Sample data were planned by the complete factorial design and obtained via numerical simulation. To compare the RSM and ANN models, a goodness of-fit test was performed. The results of the two methods are promising and it is found that the ANN results are more accurate than the RSM results.https://www.matec-conferences.org/articles/matecconf/pdf/2021/12/matecconf_mse21_03012.pdf |
| spellingShingle | El Mrabti Iliass Touache Abdelhamid El Hakimi Abdelhadi Chamat Abderahim Comparison of Artificial Neural Network Model and Response Surface Methodology for Springback Prediction MATEC Web of Conferences |
| title | Comparison of Artificial Neural Network Model and Response Surface Methodology for Springback Prediction |
| title_full | Comparison of Artificial Neural Network Model and Response Surface Methodology for Springback Prediction |
| title_fullStr | Comparison of Artificial Neural Network Model and Response Surface Methodology for Springback Prediction |
| title_full_unstemmed | Comparison of Artificial Neural Network Model and Response Surface Methodology for Springback Prediction |
| title_short | Comparison of Artificial Neural Network Model and Response Surface Methodology for Springback Prediction |
| title_sort | comparison of artificial neural network model and response surface methodology for springback prediction |
| url | https://www.matec-conferences.org/articles/matecconf/pdf/2021/12/matecconf_mse21_03012.pdf |
| work_keys_str_mv | AT elmrabtiiliass comparisonofartificialneuralnetworkmodelandresponsesurfacemethodologyforspringbackprediction AT touacheabdelhamid comparisonofartificialneuralnetworkmodelandresponsesurfacemethodologyforspringbackprediction AT elhakimiabdelhadi comparisonofartificialneuralnetworkmodelandresponsesurfacemethodologyforspringbackprediction AT chamatabderahim comparisonofartificialneuralnetworkmodelandresponsesurfacemethodologyforspringbackprediction |