The Influence of Annealing Temperature and Time on the Formation of δ-Phase in Additively-Manufactured Inconel 625

Abstract

The lack of an engaging pedagogy and the highly competitive atmosphere in introductory science courses tend to discourage students from pursuing science, technology, engineering, and mathematics (STEM) majors. Once in a STEM field, academic and social integration has been long thought to be important for students’ persistence. Yet, it is rarely investigated. In particular, the relative impact of in-class and out-of-class interactions remains an open issue. Here, we demonstrate that, surprisingly, for students whose grades fall in the “middle of the pack,” the out-of-class network is the most significant predictor of persistence. To do so, we use logistic regression combined with Akaike’s information criterion to assess in- and out-of-class networks, grades, and other factors. For students with grades at the very top (and bottom), final grade, unsurprisingly, is the best predictor of persistence—these students are likely already committed (or simply restricted from continuing) so they persist (or drop out). For intermediate grades, though, only out-of-class closeness—a measure of one’s immersion in the network—helps predict persistence. This does not negate the need for in-class ties. However, it suggests that, in this cohort, only students that get past the convenient in-class interactions and start forming strong bonds outside of class are or become committed to their studies. Since many students are lost through attrition, our results suggest practical routes for increasing students’ persistence in STEM majors.

Predicting Precipitation Kinetics During the Annealing of Additive Manufactured Inconel 625 Components

“…10 and 11, for the Nb-rich and Nb-depleted regions respectively. The model predictions are in reasonable agreement with Stoudt et al [21], however over-predicts kinetics at high temperatures.…”

“…10. The δ precipitates are predicted to be stable at higher temperatures than Stoudt et al [21] observed experimentally. Lindwall et al [22] have also modelled precipitation kinetics during the heat treatment of AM Inconel 625, and have captured this behaviour.…”

“…The normalised continuity equation is given by the following: (19) The normalised growth rate,ṙ(r, t ) is then given by the following: (20) where r c (t ) ≡ R c /R k and refers to the first moment of the function. The normalised nucleation rate is given by the following: (21) with the following normalisation parameters as follow:…”

Simulation of TTT Curves for Additively Manufactured Inconel 625

“…For example, recent experimental work at the National Institute of Standards and Technology (NIST), USA, focused on L-PBF produced IN625 and the effect of different post-build heat treatments on the microstructure evolution. [1][2][3][4][5] The extreme processing conditions during AM using L-PBF, with cooling rates as high as 10 6 K/s, are known to result in large residual stresses in the as-built parts; thus, AM system providers recommend stress-relieving treatments for IN625 before the components are removed from the build plate. [8] The high solidification rates followed by several heating and cooling cycles during the L-PBF process result in a very fine but highly segregated as-built microstructure [1][2][3] which is in contrast to the coarser solidification microstructure seen in conventionally produced material.…”

“…[19] The approaches use materials input data calculated using CALPHAD (CALculation of Phase Diagrams) databases, which make it possible to account for the multicomponent thermodynamic and kinetic aspects of the microstructure evolution. The [3,5] The curves represent the time-temperature combinations that result in a volume fraction of % 1 pct d-phase in AM IN625 (red solid line) compared to wrought IN625 (black dashed line), from Ref. [9] (Color figure online).…”