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Evaluation of ANN and ANFIS modeling ability in the prediction of AISI 1050 steel machining performance
In the development of an accurate modeling technique for the design of an efficient machining process, manufacturers must be able to identify the most suitable technique capable of producing a fast and accurate performance. This study evaluates the performance of the Artificial Neural Network (ANN)...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7856477/ https://www.ncbi.nlm.nih.gov/pubmed/33553780 http://dx.doi.org/10.1016/j.heliyon.2021.e06136 |
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author | Sada, S.O. Ikpeseni, S.C. |
author_facet | Sada, S.O. Ikpeseni, S.C. |
author_sort | Sada, S.O. |
collection | PubMed |
description | In the development of an accurate modeling technique for the design of an efficient machining process, manufacturers must be able to identify the most suitable technique capable of producing a fast and accurate performance. This study evaluates the performance of the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models in predicting the machining responses (metal removal rate and tool wear) in an AIS steel turning operation. With data generated from carefully designed machining experimentation, the adequacies of the ANN and ANFIS techniques in modeling and predicting the responses were carefully analyzed and compared. Both techniques displayed excellent abilities in predicting the responses of the machining process. However, a comparison of both techniques indicates that ANN is relatively superior to the ANFIS techniques, considering the accuracy of its results in terms of the prediction errors obtained for the ANN and ANFIS of 6.1% and 11.5% for the MRR and 4.1% and 7.2% for the Tool wear respectively. The coefficient of correlation (R(2)) obtained from the analysis further confirms the preference of the ANN with a maximum value of 92.1% recorded using the ANN compared to that of the ANFIS of 73%. The experiment further reveals that the performance of the ANN technique can yield the most ideal results when the right parameters are employed. |
format | Online Article Text |
id | pubmed-7856477 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-78564772021-02-05 Evaluation of ANN and ANFIS modeling ability in the prediction of AISI 1050 steel machining performance Sada, S.O. Ikpeseni, S.C. Heliyon Research Article In the development of an accurate modeling technique for the design of an efficient machining process, manufacturers must be able to identify the most suitable technique capable of producing a fast and accurate performance. This study evaluates the performance of the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models in predicting the machining responses (metal removal rate and tool wear) in an AIS steel turning operation. With data generated from carefully designed machining experimentation, the adequacies of the ANN and ANFIS techniques in modeling and predicting the responses were carefully analyzed and compared. Both techniques displayed excellent abilities in predicting the responses of the machining process. However, a comparison of both techniques indicates that ANN is relatively superior to the ANFIS techniques, considering the accuracy of its results in terms of the prediction errors obtained for the ANN and ANFIS of 6.1% and 11.5% for the MRR and 4.1% and 7.2% for the Tool wear respectively. The coefficient of correlation (R(2)) obtained from the analysis further confirms the preference of the ANN with a maximum value of 92.1% recorded using the ANN compared to that of the ANFIS of 73%. The experiment further reveals that the performance of the ANN technique can yield the most ideal results when the right parameters are employed. Elsevier 2021-02-01 /pmc/articles/PMC7856477/ /pubmed/33553780 http://dx.doi.org/10.1016/j.heliyon.2021.e06136 Text en © 2021 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Sada, S.O. Ikpeseni, S.C. Evaluation of ANN and ANFIS modeling ability in the prediction of AISI 1050 steel machining performance |
title | Evaluation of ANN and ANFIS modeling ability in the prediction of AISI 1050 steel machining performance |
title_full | Evaluation of ANN and ANFIS modeling ability in the prediction of AISI 1050 steel machining performance |
title_fullStr | Evaluation of ANN and ANFIS modeling ability in the prediction of AISI 1050 steel machining performance |
title_full_unstemmed | Evaluation of ANN and ANFIS modeling ability in the prediction of AISI 1050 steel machining performance |
title_short | Evaluation of ANN and ANFIS modeling ability in the prediction of AISI 1050 steel machining performance |
title_sort | evaluation of ann and anfis modeling ability in the prediction of aisi 1050 steel machining performance |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7856477/ https://www.ncbi.nlm.nih.gov/pubmed/33553780 http://dx.doi.org/10.1016/j.heliyon.2021.e06136 |
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