Daniel, Ezeliora, Chukwuemeka and Chukwuma, Okoye, Peter and Ikenna, U. Mbabuike, (2019) Prediction and Optimization of Production Quantities in Innoson Manufacturing Extraction Plastic Product. Journal of Engineering Research and Reports, 6 (2). pp. 1-11. ISSN 2582-2926
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Abstract
In this research, it focused on the prediction and optimization of the production quantity in Innoson Plastic Manufacturing Company, Nnewi, Anambra State, Nigeria. The research method used is the application of factorial design methods to model, to evaluate the best optimal solutions for the production quantity of extrusion plastic pipes in the aforementioned company. The analysis shows that the parameters used to model the production quantity are significant and the model produced is also significant with its coefficient of determination to be 0.9968 and the adjusted R-Squared is 0.9823. Adequate Precision measures the signal to noise ratio. A ratio greater than 4 is desirable. The ratio of 29.271 indicates an adequate signal. This model can be used to navigate the design space. The Model F-value of 68.99 implies the model is significant. There is only a 1.44% chance that an F-value this large could occur due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are significant. The 3D surface plot shows the effect of the variables in production system. It describes the variations of the input and output parameters in production of plastic extrusion products. The factorial design method applied shows the optimal solution which revealed that the best quantity of the product that is necessary to produce in any given month is 14414.112 units of a 25mm diameter plastic pipes with the optimal desirability of 100%. The tool also shows that the pigment is almost not important in the optimization of the product production quantity due to its insignificant quantity. However, the results further revealed that the industry should be conscious of highly influence input variable during production.
Item Type: | Article |
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Subjects: | OA Digital Library > Engineering |
Depositing User: | Unnamed user with email support@oadigitallib.org |
Date Deposited: | 15 Apr 2023 07:44 |
Last Modified: | 14 Sep 2024 03:50 |
URI: | http://library.thepustakas.com/id/eprint/950 |