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• W4377014035 abstract "Abstract The laser selective melting (SLM) process is commonly used in additive manufacturing (AM) to produce integrated parts from metallic powder. However, this process can result in porosity in the fabricated components due to gas bubbles or lack of fusion. In a recent study, four different methods (response surface methodology (RSM), artificial neural network (ANN), fuzzy logic (FL), and adaptive-network-based fuzzy inference system (ANFIS)) were employed to predict the correlation between process parameters and porosity levels in AlSi10Mg alloy produced by the SLM process. The ANFIS method was used in this study, which utilizes fuzzy rules and artificial neural networks to predict the impact of process parameters, such as island size, hatch space, scan speed, and laser power on porosity levels. The results showed that the laser power and scan speed had a significant effect on the volume of porosity, while the influence of island size and hatch space was slighter in the SLM process of AlSi10Mg alloy. Furthermore, the ANFIS model demonstrated an excellent fitting parameter with an R 2 value of more than 0.99 for the total data and an RMSE of about 0.67 for the output. This result indicates that the ANFIS method is the best model among all the methods tested in accurately predicting the process target. The success of the ANFIS method in this study suggests that it is a robust and reliable technique for predicting porosity levels in the SLM process." @default.
• W4377014035 created "2023-05-19" @default.
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• W4377014035 date "2023-05-18" @default.
• W4377014035 modified "2023-10-01" @default.
• W4377014035 title "Investigation of process parameters effects on porosity in SLM process of AlSi10Mg alloys" @default.
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• W4377014035 doi "https://doi.org/10.21203/rs.3.rs-2921045/v1" @default.
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