Wibowo, Eko Ari and Sofyan, Edi and Syahriar, Ary (2021) Optimization Warpage Defects of Pencil Box by Using Finite Element Analysis and Artificial Neural Networks. Masters thesis, Swiss German University.

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Abstract

The use of plastic products is currently increasing rapidly, starting from automotive components, electronics, and office equipment. Injection molding process is a method of making plastic products by injecting the material into the mold. One of the products is a pencil box, but this product has a warpage defect. Defect is indicated by a deflection in the wall, causing misassemble. This study aims to eliminate these defects with parameter optimization. Taguchi method with the L27 (34) orthogonal array was used to make the data input design. Data that has been designed is simulated with the Finite Element Analysis method using MoldFlow to get value of deflection. Results of the experiment were analyzed with Backpropagation Neural Network to determine the pattern of the relationship between process parameters and response, while Genetic Algorithm method was used for parameter optimization. Composition of the recommended parameters, namely: mold temperature 15°C, melt temperature 200°C, packing pressure 120% and injection time 6 seconds. As a result, optimization of deflection reached 44%. Previous maximum deflection of 2.779 mm has decreased to 1.554 mm.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	: Plastic Injection Molding, Warpage Defect, Taguchi Method, Backpropagation Neural Network, Genetic Algorithm
Subjects:	Q Science > QA Mathematics > QA76 Computer software > QA76.87 Neural networks (Computer science) T Technology > TJ Mechanical engineering and machinery T Technology > TJ Mechanical engineering and machinery > TJ163.12 Mechatronics
Divisions:	Faculty of Engineering and Information Technology > Department of Mechatronics Engineering
Depositing User:	Faisal Ifzaldi
Date Deposited:	20 Aug 2021 01:38
Last Modified:	20 Aug 2021 01:38
URI:	http://repository.sgu.ac.id/id/eprint/2176

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