1. The effect of electrochemical discharge machining process parameters and predictive modelling on material removal rate of tungsten carbide using artificial neural network.
- Author
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Lusi, Nuraini, Fiveriati, Anggra, Afandi, Akhmad, Yuliandoko, Herman, and Darsin, Mahros
- Subjects
ARTIFICIAL neural networks ,TUNGSTEN carbide ,METAL cutting ,ELECTROCHEMICAL cutting ,CHEMICAL milling ,PREDICTION models ,MACHINING - Abstract
Electrochemical chemical discharge machining (ECDM) is the hybrid machining process as an alternative in machining process of material that require precision dimension and complex structure. The demand for the manufacture of tungsten carbide products is a challenge due to its high strength. Technological innovation in metal cutting processes such as hybrid manufacturing-based technology is independent of the hardness of the workpiece. This study aims to predict the response value and identify the combination of process parameters to achieve the required performance characteristics in the ECDM process of tungsten carbide using the ANN method. The four process parameters selected were voltage, gap width, electrode type, and electrolyte type, with each parameter having two levels and full factorial 2
4 selected as the design of experiment. R-squared is then used to determine how accurate the model is. In comparison to the linear regression model, the Artificial Neural Network model produces more precise and reliable results. Using the desirability function, the input parameters were improved for a higher MRR. The neural network model was used to validate the results for the best solution. The validation serves as an example of the impact artificial intelligence-focused machining techniques have on optimizing progressions. [ABSTRACT FROM AUTHOR]- Published
- 2024
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