Python inspired artificial neural networks modeling in drilling of glass-hemp-flax fiber composites
As composites are materials whose properties can essentially be customized to suit the necessities of the engineering application on hand, they are being widely used in many applications for radically different purposes. In order to ensure quality in production process of composite products, a solid...
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University of Belgrade - Faculty of Mechanical Engineering, Belgrade
2021-01-01
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doaj-0efe27b0a740491c8c558e866314edf32021-07-20T07:22:55ZengUniversity of Belgrade - Faculty of Mechanical Engineering, BelgradeFME Transactions1451-20922406-128X2021-01-0149242242910.5937/fme2102422S1451-20922102422VPython inspired artificial neural networks modeling in drilling of glass-hemp-flax fiber compositesVimal Sam Singh R.0Ramachandran Achyuth1Selvam Anirudh2Subramanian Karthick3Sri Sivasubramaniya Nadar College of Engineering, Department of Mechanical Engineering, Chennai, IndiaSri Sivasubramaniya Nadar College of Engineering, Department of Mechanical Engineering, Chennai, IndiaSri Sivasubramaniya Nadar College of Engineering, Department of Mechanical Engineering, Chennai, IndiaSri Sivasubramaniya Nadar College of Engineering, Department of Mechanical Engineering, Chennai, IndiaAs composites are materials whose properties can essentially be customized to suit the necessities of the engineering application on hand, they are being widely used in many applications for radically different purposes. In order to ensure quality in production process of composite products, a solid understanding of the process involved during its manufacturing is essential to ensure the product is free from both internal and external defects. To that aim, a study was conducted to model Thrust force and Torque on drilling of Glass-Hemp-Flax reinforced polymer composite by fabricating and maching the composite as per Taguchi's L 27 Orthogonal Array. The process parameters considered for modeling are drill diameter, spindle speed and feed rate. Using the process control parameters as inputs and thrust force and torque to be predicted as outputs, artificial neural networks (ANNs) were created to model the effects of the inputs and their interactions. The predictions obtained from the neural networks were compared with the values obtained from experimentation. Excellent agreement was found between the two sets of values, establishing grounds for more extensive use of neural networks in modelling of machining parameters.https://scindeks-clanci.ceon.rs/data/pdf/1451-2092/2021/1451-20922102422V.pdfdrilling of hybrid fiber compositesthrust forcetorqueartificial neural networkpython |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Vimal Sam Singh R. Ramachandran Achyuth Selvam Anirudh Subramanian Karthick |
spellingShingle |
Vimal Sam Singh R. Ramachandran Achyuth Selvam Anirudh Subramanian Karthick Python inspired artificial neural networks modeling in drilling of glass-hemp-flax fiber composites FME Transactions drilling of hybrid fiber composites thrust force torque artificial neural network python |
author_facet |
Vimal Sam Singh R. Ramachandran Achyuth Selvam Anirudh Subramanian Karthick |
author_sort |
Vimal Sam Singh R. |
title |
Python inspired artificial neural networks modeling in drilling of glass-hemp-flax fiber composites |
title_short |
Python inspired artificial neural networks modeling in drilling of glass-hemp-flax fiber composites |
title_full |
Python inspired artificial neural networks modeling in drilling of glass-hemp-flax fiber composites |
title_fullStr |
Python inspired artificial neural networks modeling in drilling of glass-hemp-flax fiber composites |
title_full_unstemmed |
Python inspired artificial neural networks modeling in drilling of glass-hemp-flax fiber composites |
title_sort |
python inspired artificial neural networks modeling in drilling of glass-hemp-flax fiber composites |
publisher |
University of Belgrade - Faculty of Mechanical Engineering, Belgrade |
series |
FME Transactions |
issn |
1451-2092 2406-128X |
publishDate |
2021-01-01 |
description |
As composites are materials whose properties can essentially be customized to suit the necessities of the engineering application on hand, they are being widely used in many applications for radically different purposes. In order to ensure quality in production process of composite products, a solid understanding of the process involved during its manufacturing is essential to ensure the product is free from both internal and external defects. To that aim, a study was conducted to model Thrust force and Torque on drilling of Glass-Hemp-Flax reinforced polymer composite by fabricating and maching the composite as per Taguchi's L 27 Orthogonal Array. The process parameters considered for modeling are drill diameter, spindle speed and feed rate. Using the process control parameters as inputs and thrust force and torque to be predicted as outputs, artificial neural networks (ANNs) were created to model the effects of the inputs and their interactions. The predictions obtained from the neural networks were compared with the values obtained from experimentation. Excellent agreement was found between the two sets of values, establishing grounds for more extensive use of neural networks in modelling of machining parameters. |
topic |
drilling of hybrid fiber composites thrust force torque artificial neural network python |
url |
https://scindeks-clanci.ceon.rs/data/pdf/1451-2092/2021/1451-20922102422V.pdf |
work_keys_str_mv |
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