Tailored Time-Temperature Transformation Diagram for IN718 Alloy Obtained via Powder Bed Fusion Additive Manufacturing: Phase Behavior and Precipitation Dynamic.

Experimental and computational approaches were used to study the microstructure of IN718 produced via powder bed fusion additive manufacturing (PBF-AM). The presence, chemical composition, and distribution of stable and metastable phases (γ'', δ, MC, and Laves) were also analyzed. The information obtained from the microstructural study was used to construct a tailored time-temperature transformation (TTT) diagram customized for additive manufacturing of IN718. Experimental techniques, including differential scanning calorimetry (DSC), scanning electron microscopy, energy dispersive X-ray spectroscopy, and electron backscatter diffraction (EBSD), were employed to establish the morphological, chemical, and structural characteristics of the microstructure. The Thermo-Calc software and a Scheil-Gulliver model were used to analyze the presence and behavior of phase transformations during heating and cooling processes under non-thermodynamic equilibrium conditions, typical of AM processes. Unlike conventional TTT diagrams of this alloy, the diagram presented here reveals that the precipitation of γ'' and δ phases occurs at lower temperatures and shorter times in AM-manufactured parts. Significantly, the superposition of γ'' and δ phase curves in the proposed diagram underscores the interdependence between these phases. This TTT diagram is a valuable insight that can help in the development of heat treatment processes and quality control for IN718 produced via PBF-AM.

Determination of TTT Diagrams of Ni-Al Binary Using Neural Networks.

The heat treatment of a metal is a set of heating and cooling cycles that a metal undergoes to change its microstructure and, therefore, its properties. Temperature-time-transformation (TTT) diagrams are an essential tool for interpreting the resulting microstructures after heat treatments. The present work describes a novel proposal to predict TTT diagrams of the γ' phase for the Ni-Al alloy using artificial neural networks (ANNs). The proposed methodology is composed of five stages: (1) database creation, (2) experimental design, (3) ANNs training, (4) ANNs validation, and (5) proposed models analysis. Two approaches were addressed, the first to predict only the nose point of the TTT diagrams and the second to predict the complete curve. Finally, the best models for each approach were merged to compose a more accurate hybrid model. The results show that the multilayer perceptron architecture is the most efficient and accurate compared to the simulated TTT diagrams. The prediction of the nose point and the complete curve showed an accuracy of 98.07% and 86.41%, respectively. The proposed final hybrid model achieves an accuracy of 96.59%.