Artificial intelligence inspired design of non-isothermal aging for γ-γ' two-phase, Ni-Al alloys.

In this paper, a state-of-the-art Artificial Intelligence (AI) technique is used for a precipitation hardening of Ni-based alloy to predict more flexible non-isothermal aging (NIA) and to examine the possible routes for the enhancement in strength that may be practically achieved. Additionally, AI is used to integrate with Materials Integration by Network Technology, which is a computational workflow utilized to model the microstructure evolution and evaluate the 0.2% proof stress for isothermal aging and NIA. As a result, it is possible to find enhanced 0.2% proof stress for NIA for a fixed time of 10 min compared to the isothermal aging benchmark. The entire search space for aging scheduling was ~ 3 billion. Out of 1620 NIA schedules, we succeeded in designing the 110 NIA schedules that outperformed the isothermal aging benchmark. Interestingly, it is found that early-stage high-temperature aging for a shorter time increases the γ' precipitate size up to the critical size and later aging at lower temperature increases the γ' fraction with no anomalous change in γ' size. Therefore, employing this essence from AI, we designed an optimum aging route in which we attained an outperformed 0.2% proof stress to AI-designed NIA routes.

Melt Pool Shape Evaluation by Single-Track Experiments and Finite-Element Thermal Analysis: Balling and Lack of Fusion Criteria for Generating Process Window of Inconel738LC.

Defects occur in laser powder bed fusion (L-PBF) such as the keyholing, lack of fusion, and the balling depending on the laser power (P) and the scan speed (V). The figure shows that the occupied regions of each defect are the process window and are essentially important to fabricate a high-quality part. This paper is a study of process window generation using single-track experiments and finite-element method simulation of thermal conduction for Inconel738LC alloy. A series of single-track experiments were conducted varying the range of P and V and the results were classified into keyholing, lack of fusion, balling, and good track. A series of simulations were conducted and validated by comparison with the experiments. To quantitively identify the balling, the isolines from the contour map generated by the results of simulations and the balling criteria of the ratio of melt pool length and the depth (L/D) of 7.69 were determined considering the past theoretical studies. The lack of fusion criteria: the ratio of the overlap depth in fabrication using multi-scan (Dov) and powder layer thickness (t) of 0.1 was obtained. Using the criteria obtained from the experiments and simulation, the process window was generated.