Adesanya, A.O. (2020) PREDICTING EXTRUSION PROCESS PARAMETERS IN NIGERIA CABLE INDUSTRIES USING ARTIFICIAL NEURAL NETWORK. Masters thesis, Covenant University Ota..
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Abstract
Cable manufacturing in a developing country like Nigeria today is faced with different problems during the thermoplastic extrusion processes due to the complex nature of the parameters that are involved in the process. These process parameters which include melt temperature, pressure, and screw speed generally impact the quality of the insulation in electrical cables. The main consequence of the problem is the low and variable output rate from extruder causing cable defects and non-uniform diameter along the cable length. This7 often increases the production time and cost in the extrusion process. Different research has been done to improve extrusion output quality in developed countries. However, there are still some problems in achieving consistent product quality as most of the developing countries still use the trial and error techniques which involves full-size experiments to determine the process parameters and cable insulation thickness in the thermoplastic extrusion process. The main purpose of this research is to determine the realistic extrusion process parameters and cable insulation thickness in the thermoplastic extrusion process in Nigeria cable manufacturing industries with the use of an artificial neural network. The use of an artificial neural network to predict extrusion process parameters before plant execution will make extrusion process operations more efficient. This technique also bridges the gap that exists between theoretical analysis and real manufacturing system. The neural network was developed in a MATLAB environment and was trained with an appropriate learning algorithms. The neural network model developed is capable of predicting manufacturing process parameters and insulation thickness for different thermoplastic materials in the thermoplastic extrusion process.
| Item Type: | Thesis (Masters) |
|---|---|
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science |
| Depositing User: | Mrs Patricia Nwokealisi |
| Date Deposited: | 08 Jun 2021 12:17 |
| Last Modified: | 08 Jun 2021 12:17 |
| URI: | http://eprints.covenantuniversity.edu.ng/id/eprint/14362 |
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