Nickel-based alloys have good mechanical properties along with excellent corrosion and oxidation resistances. Inconel 718, which is in the nickel-based alloy group, has excellent fatigue strength, corrosion resistance, high temperature resistance, and also features such as high strength, toughness and deformation hardening. The biggest disadvantage of Ni alloys is their poor processability. Especially in the machining method, expensive tools should be used. In addition, high surface roughness values ​​obtained as a result of machining in such materials negatively affect the fatigue resistance. On the other hand, difficult-to-machine materials can be manufactured cheaply and easily by methods known as non-traditional production. The most widely used of these methods is the Electrical Discharge Machining (EDM) due to its precision and machining capabilities. The Inconel 718 material used in this study was etched by EDM method using different process parameters. The effects of finishing process parameters on surface roughness after scouring were determined. In the study, the probable surface roughness value estimation model was created by using the process parameters and the surface roughness values ​​obtained as a result of these parameters using the artificial intelligence methods ANN and GEP. For this study, it was concluded that the ANN method is more suitable than the GEP method.

镍基合金具有良好的机械性能以及出色的耐腐蚀和抗氧化性。镍基合金组中的Inconel 718具有出色的疲劳强度，耐腐蚀性，耐高温性，并且还具有高强度，韧性和变形硬化等特征。镍合金的最大缺点是加工性能差。特别是在加工方法中，应使用昂贵的工具。此外，由于在此类材料中进行机加工而获得的高表面粗糙度值会对耐疲劳性产生负面影响。另一方面，可以通过称为非传统生产的方法廉价且容易地制造难以加工的材料。这些方法中使用最广泛的是放电加工（EDM），因为它具有很高的精度和加工能力。本研究中使用的Inconel 718材料通过采用不同工艺参数的EDM方法进行蚀刻。确定了精加工工艺参数对精练后表面粗糙度的影响。在研究中，通过使用工艺参数创建了可能的表面粗糙度值估计模型，并使用人工智能方法ANN和GEP根据这些参数获得了表面粗糙度值。对于本研究，得出的结论是，人工神经网络方法比GEP方法更合适。确定了精加工工艺参数对精练后表面粗糙度的影响。在研究中，通过使用工艺参数创建了可能的表面粗糙度值估计模型，并使用人工智能方法ANN和GEP根据这些参数获得了表面粗糙度值。对于本研究，得出的结论是，人工神经网络方法比GEP方法更合适。确定了精加工工艺参数对精练后表面粗糙度的影响。在研究中，通过使用工艺参数创建了可能的表面粗糙度值估计模型，并使用人工智能方法ANN和GEP根据这些参数获得了表面粗糙度值。对于本研究，得出的结论是，人工神经网络方法比GEP方法更合适。