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Mogonye, Jon-Erik

Publications and source records attributed to Mogonye, Jon-Erik.

3 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Combining solution-, precipitation- and load-transfer strengthening in a cast Al-Ce-Mn- Sc -Zr alloy

Here, a cast Al-9Ce-0.75Mn-0.18Sc-0.12Zr (wt%) alloy is designed to combine three strengthening phases: (i) micron-scale Al 11 Ce 3 platelets formed during eutectic solidification, (ii) nano-scale L1 2 -Al 3 (Sc,Zr) precipitates formed during aging, and (iii) Mn in solid solution in the α-Al matrix. Microstructural analyses by SEM, TEM, and atom-probe tomography reveal that Mn remains in solid solution in the as-cast alloy, providing solution strengthening with no influence on the eutectic Al-Al 11 Ce 3 microstructure, which provides precipitation- and load-transfer strengthening. During long-term over-aging at 400 °C, Mn-rich precipitates grow at the Al-Al 11 Ce 3 interface, with no effect on the microhardness. However, after short aging at 350 °C, a high number density of fine L1 2 -Al 3 (Sc,Zr) nanoprecipitates form in the Al matrix (with a coarser size at the Al-Al 11 Ce 3 interface), providing precipitation strengthening. The synergistic combination of the three strengthening mechanisms (solution, precipitation, and load transfer) in our Al-Ce-Mn-Sc-Zr alloy results in higher microhardness after aging at 350 and 400 °C, and higher creep resistance at 300 °C, as compared to alloys with two strengthening mechanisms: an Al-10Ce-0.93Mn control alloy (without precipitation strengthening from Sc and Zr), Al-Ce-Sc-Zr (without solution strengthening from Mn), and Al-Mn-Zr-Er (without load-transfer strengthening from Ce). Furthermore, these dual-strengthened alloys are more creep resistant than alloys with a single strengthening mechanism (Al-Ce, Al-Mn, and Al-Sc-Zr), confirming that the three mechanisms can be combined in pairs or all together.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Data-driven prediction of geometry- and toolpath sequence-dependent intra-layer process conditions variations in laser powder bed fusion

Geometrical features and toolpath sequence are two important factors that cause process condition variations, such as variations in the meltpool temperature or meltpool size, that might lead to undesired material properties in the laser powder bed fusion (LPBF) process. Due to the high dynamics and complex physics of the LPBF process, it is difficult to predict variations in process conditions with simulations alone. Advances in measurement technology and computational technologies open up new possibilities for smart manufacturing. In this paper, a data-driven method to predict intra-layer variations in the processing conditions that source from the toolpath sequence and part geometry is presented. The approach is demonstrated using two-color on-axis pyrometer measurements. Three demonstration cases are presented in which it is demonstrated (1) how the trained predictive model can be used as a filter to ease the interpretation of process variations and discover patterns related to toolpath and part geometry, and (2) how to generate predictions that can be used for feedforward control, i.e., for adjusting laser power or scanning speed along the toolpath using a meltpool temperature prediction model generated based on on-axis measurements. Results show that the developed prediction model is able to meaningfully predict process variations resulted from toolpath sequence and geometry. Predictions are aligned with the results from the related work of others and for the case of 180° laser path turnarounds in our high-speed X-ray imaging experiments. In conclusion, the potential issues related to the current maturity status of the process and measuring equipment that could in practice affect the performance of the proposed solutions are also discussed.

42 ENGINEERING↗