Nugroho, Albertus Aan Dian and Sofyan, Edi and Hendriana, Dena (2021) Application of an Artificial Neural Network Model to Predict Parameter of Friction Stir Spot Welding on Aluminum Sheet. Masters thesis, Swiss German University.

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Abstract

This research was conducted to predict the maximum load in the Friction Stir Spot Welding process using Aluminium Alloy 1050 material. Process parameters there are 4 variations, namely tool pin diameter, tool rotation speed, welding speed which each has 3 levels, and plunge depth which has 2 levels. The experimental design in this research used the Taguchi method with 54 experiments. The results of the training Backpropagation Neural Network have a 4-8-8-1 network architecture that consists of from 4 input layers, 2 hidden layers with 8 neurons, and 1 neuron on output layer. The activation function used is "tansig" and the training function is "trainrp". In addition, regression analysis was also carried out on the 4 parameters of the Friction Stir Spot Welding which are the input variables. From the results of the regression analysis, it is known that the parameters of welding speed (46.68%) and tool diameter (36.85%) have the most influence on the magnitude of maximum load.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Friction Stir Spot Welding, Maximum Load, Regression, Taguchi, Tansig, Trainrp, Backpropagation
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
Depositing User:	Faisal Ifzaldi
Date Deposited:	19 Aug 2021 10:18
Last Modified:	19 Aug 2021 10:18
URI:	http://repository.sgu.ac.id/id/eprint/2148

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