Inconel 718 is a nickel-based superalloy and an excellent candidate for the aerospace, oil, and gas industries due to its high strength and corrosion resistance properties. The machining of IN718 is very challenging; therefore, the application of additive manufacturing (AM) technology is an effective approach to overcoming these difficulties and for the fabrication of complex geometries that cannot be manufactured by the traditional techniques. Selective laser melting (SLM), which is a laser powder bed fusion method, can be applied for the fabrication of IN718 samples with high accuracy. However, the process parameters have a high impact on the properties of the manufactured samples. In this study, a prediction model is developed for obtaining the optimal process parameters, including laser power, hatch spacing, and scanning speed, in the SLM process of the IN718 alloy. For this purpose, artificial neural network (ANN) modeling with various algorithms is employed to estimate the process outputs, namely, sample height and surface hardness. The modeling results fit perfectly with the experimental output, and this consequently proves the benefit of ANN modeling for predicting the optimal process parameters.
Inconel 718 (IN718) is a nickel-based superalloy which is widely used in aerospace, oil, and gas industries due to its outstanding mechanical properties at high temperatures, corrosion, fatigue resistance, and excellent weldability. Selective laser melting (SLM), one of the most used powder-bed based methods, is being extensively used to fabricate functional IN718 components with high accuracy. The accuracy and the properties of the SLM fabricated IN718 parts highly depend on the process parameters employed during fabrication. Thus, depending on the desired properties, the process parameters for a given material need to be optimized for improving the overall reliability of the SLM devices. In this study, design of experiment (DOE) was used to evaluate the dimensional accuracy, composition, and hardness corresponding to the interaction between the SLM process parameters such as laser power (P), scan speed (v), and hatch spacing (h). Contour plots were generated by co-relating the determined values for each characteristic and the process parameters to improve the as-built characteristics of the fabricated IN718 parts and reduce the post-processing time. The outcome of this study shows a range of energy density values for the IN718 superalloy needed to attain optimal values for each of the analyzed characteristics. Finally, an optimal processing region for SLM IN718 fabrication was identified which is in accordance with the values for each characteristic mentioned in literature.
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