EDM Parameters Optimization on Tool Wear Rate using Advanced Optimization of Titanium Alloy
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Table 1: Input Parameter Range Parameter Ip (Ampere) V (Volt) Ton (Âľ seconds) t (Duty factor setting)
Level 1 4 40 50 8
Level 2 6 70 100 12
Level 3 8 100 150 16
Table 2: Experimental Layout for the Response Surface Ip 4 4 4 4 4 4 4 4 4 6 6 6 6 6 6 6 6 6 6 6 6 8 8 8 8 8 8 8 8 8
t 8 8 8 8 12 16 16 16 16 8 12 12 12 12 12 12 12 12 12 12 16 8 8 8 8 12 16 16 16 16
V 40 40 100 100 70 40 40 100 100 70 40 70 70 70 70 70 70 70 70 100 70 40 40 100 100 70 40 40 100 100
Ton 50 150 50 150 100 50 150 50 150 100 100 50 100 100 100 100 100 100 150 100 100 50 150 50 150 100 50 150 50 150
TWR mg/min 0.3283 0.1167 0.2917 0.1000 0.0727 0.3567 0.0817 0.2050 0.0767 0.3450 0.4067 0.6833 0.3655 0.3717 0.3883 0.3533 0.3517 0.3626 0.2200 0.2667 0.2417 1.1367 0.5500 0.9000 0.3217 0.6056 0.7367 0.3183 0.2750 0.2567
3.1 Artificial Neural Network Model for Tool Wear Rate A predictable ANN model [9] is developed in this research, based on the design of experiments. Totally, thirty experiments were conducted, which are input to the ANN Model. ANN Model consists of (1) Input Layer (2) Hidden Layersand (3) Output Layer. The Inputs are multiplied by a respective weight and then summation into a hidden layer. After various experimentation, it is found that total of five neurons required with log sigmoid function for hidden layer, and pure linear activation function for the output layer generates better predictable ANN model. ANN model is developed to verify optimum result produced by Jaya Algorithm.
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