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Prediction of Surface Roughness considering Cutting Parameters and Humidity Condition in End Milling of Polyamide Materials
To know the impact of processing parameters of PA6G under different humidity conditions is important as it is vulnerable to humidity up to 7 %. This study investigated the effect of cutting parameters to surface roughness quality in wet and dry conditions. Artificial Neural Network (ANN) modeling is...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2018
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6046151/ https://www.ncbi.nlm.nih.gov/pubmed/30050565 http://dx.doi.org/10.1155/2018/5850432 |
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author | Bozdemir, Mustafa |
author_facet | Bozdemir, Mustafa |
author_sort | Bozdemir, Mustafa |
collection | PubMed |
description | To know the impact of processing parameters of PA6G under different humidity conditions is important as it is vulnerable to humidity up to 7 %. This study investigated the effect of cutting parameters to surface roughness quality in wet and dry conditions. Artificial Neural Network (ANN) modeling is also developed with the obtained results from the experiments. Humidity condition, tool type, cutting speed, cutting rate, and depth of cutting parameters were used as input and average surface roughness value were used as output of the ANN model. Testing results showed that ANN can be used for prediction of average surface roughness. |
format | Online Article Text |
id | pubmed-6046151 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-60461512018-07-26 Prediction of Surface Roughness considering Cutting Parameters and Humidity Condition in End Milling of Polyamide Materials Bozdemir, Mustafa Comput Intell Neurosci Research Article To know the impact of processing parameters of PA6G under different humidity conditions is important as it is vulnerable to humidity up to 7 %. This study investigated the effect of cutting parameters to surface roughness quality in wet and dry conditions. Artificial Neural Network (ANN) modeling is also developed with the obtained results from the experiments. Humidity condition, tool type, cutting speed, cutting rate, and depth of cutting parameters were used as input and average surface roughness value were used as output of the ANN model. Testing results showed that ANN can be used for prediction of average surface roughness. Hindawi 2018-06-28 /pmc/articles/PMC6046151/ /pubmed/30050565 http://dx.doi.org/10.1155/2018/5850432 Text en Copyright © 2018 Mustafa Bozdemir. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Bozdemir, Mustafa Prediction of Surface Roughness considering Cutting Parameters and Humidity Condition in End Milling of Polyamide Materials |
title | Prediction of Surface Roughness considering Cutting Parameters and Humidity Condition in End Milling of Polyamide Materials |
title_full | Prediction of Surface Roughness considering Cutting Parameters and Humidity Condition in End Milling of Polyamide Materials |
title_fullStr | Prediction of Surface Roughness considering Cutting Parameters and Humidity Condition in End Milling of Polyamide Materials |
title_full_unstemmed | Prediction of Surface Roughness considering Cutting Parameters and Humidity Condition in End Milling of Polyamide Materials |
title_short | Prediction of Surface Roughness considering Cutting Parameters and Humidity Condition in End Milling of Polyamide Materials |
title_sort | prediction of surface roughness considering cutting parameters and humidity condition in end milling of polyamide materials |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6046151/ https://www.ncbi.nlm.nih.gov/pubmed/30050565 http://dx.doi.org/10.1155/2018/5850432 |
work_keys_str_mv | AT bozdemirmustafa predictionofsurfaceroughnessconsideringcuttingparametersandhumidityconditioninendmillingofpolyamidematerials |