Python inspired artificial neural networks modeling in drilling of glass-hemp-flax fiber composites
| Autor: | Anirudh Selvam, Karthick Subramanian, Achyuth Ramachandran, Sam Vimal Singh |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Materials science
torque 02 engineering and technology Flax fiber 0202 electrical engineering electronic engineering information engineering Process engineering computer.programming_language Artificial neural network business.industry Mechanical Engineering Mechanics of engineering. Applied mechanics Drilling 020206 networking & telecommunications TA349-359 Python (programming language) Engineering (General). Civil engineering (General) 021001 nanoscience & nanotechnology drilling of hybrid fiber composites python Mechanics of Materials thrust force TA1-2040 0210 nano-technology business computer artificial neural network |
| Zdroj: | FME Transactions, Vol 49, Iss 2, Pp 422-429 (2021) |
| ISSN: | 2406-128X 1451-2092 |
| DOI: | 10.5937/fme2102422s |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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