Prediction of specific wear rate for LM25/ZrO2 composites using Levenberg–Marquardt backpropagation algorithm
In this study, the wear estimation capability of RSM and artificial neural network (ANN) modelling techniques are examined and compared in this study. Though both RSM and ANN model performed well, ANN-based approach is found to be better in fitting to measure output response in comparison with the R...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-01-01
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| Series: | Journal of Materials Research and Technology |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785419309858 |
| _version_ | 1818509609006006272 |
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| author | Mathi Kannaiyan Govindan karthikeyan Jinu Gowthami Thankachi Raghuvaran |
| author_facet | Mathi Kannaiyan Govindan karthikeyan Jinu Gowthami Thankachi Raghuvaran |
| author_sort | Mathi Kannaiyan |
| collection | DOAJ |
| description | In this study, the wear estimation capability of RSM and artificial neural network (ANN) modelling techniques are examined and compared in this study. Though both RSM and ANN model performed well, ANN-based approach is found to be better in fitting to measure output response in comparison with the RSM model. The comparison of the productive capacity of RSM and LMBP (Levenberg–Marquardt backpropagation) neural network architecture for modelling the output, as well as output, predicted for the wear samples in terms of various statistical parameters such as coefficient of determination (R2), etc., has been done. The coefficient of determination (R2) is higher for which the evaluated value shows that the ANN models have a higher modelling ability than the RSM model. The comparison between the experimental value and predicted value obtained by the ANN and RSM models reveals the coefficient of model determination (R2) for the ANN and RSM model is close to unity. The results obtained from the comparison of specific wear rate values using ANN and RSM were proved to be close to the reading recorded experimentally with a 99% confidence level. Keywords: Artificial neural networks, Coefficient of determination, Levenberg–Marquardt backpropagation, Wear, Specific wear rate, Mean Squared Error |
| first_indexed | 2024-12-10T22:47:46Z |
| format | Article |
| id | doaj.art-35cdbed077e548388e813a1b2c924af9 |
| institution | Directory Open Access Journal |
| issn | 2238-7854 |
| language | English |
| last_indexed | 2024-12-10T22:47:46Z |
| publishDate | 2020-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj.art-35cdbed077e548388e813a1b2c924af92022-12-22T01:30:32ZengElsevierJournal of Materials Research and Technology2238-78542020-01-0191530538Prediction of specific wear rate for LM25/ZrO2 composites using Levenberg–Marquardt backpropagation algorithmMathi Kannaiyan0Govindan karthikeyan1Jinu Gowthami Thankachi Raghuvaran2Department of Mechanical Engineering, University College of Engineering (A Constituent College of Anna University, Chennai), Kancheepuram, Tamil Nadu 631552, IndiaDepartment of Mechanical Engineering, University College of Engineering, Pattukkottai (A Constituent College of Anna University, Chennai), Rajamadam, Pattukkottai, Thanjavur (D.t), Tamilnadu 614701, India; Corresponding author.Department of Mechanical Engineering, University College of Engineering Nagercoil, Konam (A Constituent College of Anna University, Chennai), Nagercoil, Tamilnadu 629004, IndiaIn this study, the wear estimation capability of RSM and artificial neural network (ANN) modelling techniques are examined and compared in this study. Though both RSM and ANN model performed well, ANN-based approach is found to be better in fitting to measure output response in comparison with the RSM model. The comparison of the productive capacity of RSM and LMBP (Levenberg–Marquardt backpropagation) neural network architecture for modelling the output, as well as output, predicted for the wear samples in terms of various statistical parameters such as coefficient of determination (R2), etc., has been done. The coefficient of determination (R2) is higher for which the evaluated value shows that the ANN models have a higher modelling ability than the RSM model. The comparison between the experimental value and predicted value obtained by the ANN and RSM models reveals the coefficient of model determination (R2) for the ANN and RSM model is close to unity. The results obtained from the comparison of specific wear rate values using ANN and RSM were proved to be close to the reading recorded experimentally with a 99% confidence level. Keywords: Artificial neural networks, Coefficient of determination, Levenberg–Marquardt backpropagation, Wear, Specific wear rate, Mean Squared Errorhttp://www.sciencedirect.com/science/article/pii/S2238785419309858 |
| spellingShingle | Mathi Kannaiyan Govindan karthikeyan Jinu Gowthami Thankachi Raghuvaran Prediction of specific wear rate for LM25/ZrO2 composites using Levenberg–Marquardt backpropagation algorithm Journal of Materials Research and Technology |
| title | Prediction of specific wear rate for LM25/ZrO2 composites using Levenberg–Marquardt backpropagation algorithm |
| title_full | Prediction of specific wear rate for LM25/ZrO2 composites using Levenberg–Marquardt backpropagation algorithm |
| title_fullStr | Prediction of specific wear rate for LM25/ZrO2 composites using Levenberg–Marquardt backpropagation algorithm |
| title_full_unstemmed | Prediction of specific wear rate for LM25/ZrO2 composites using Levenberg–Marquardt backpropagation algorithm |
| title_short | Prediction of specific wear rate for LM25/ZrO2 composites using Levenberg–Marquardt backpropagation algorithm |
| title_sort | prediction of specific wear rate for lm25 zro2 composites using levenberg marquardt backpropagation algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2238785419309858 |
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