Integrated Process and Materials Modeling for Development of Additive Manufacturing of Refractory Materials for Critical Applications
Current projections for increasing the efficiency of gas turbine energy generation have identified the need for materials capable of prolonged operating temperatures of 1300°C, and refractory metals are considered potential candidates to achieve this goal. However, the melting temperature of refractory metals is typically higher than 2500 oC (e.g., NB, Mo, W, Ta) so these metals are conventionally fabricated by powder metallurgy (PM) process due to difficulties in traditional manufacturing processes such as casting. While PM remains a valuable manufacturing technique for refractory metals, the development of additive manufacturing (AM) process for refractory metals is emerging due to its unique advantages in terms of short lead time, superior design flexibility, less material waste and resulting cost saving.
Commonwealth Center for Advanced Manufacturing (CCAM) in private industrial partnership with Siemens and two university members within Commonwealth of Virginia and Oak Ridge National Laboratory (ORNL) have collaborated with the aim of utilizing integrated AM simulations to develop and confirm the process relationships for advanced process control for refractory metal of C103. CCAM and ORNL team used their expertise in building parts with SS316L and C103 part using powder direct energy deposition (DED) and simulating multi-physics melt pool and microstructure simulations using high-performance computing (HPC) systems at ORNL and National Renewable Energy Laboratory (NREL). The experimental data sets by CCAM were used to correct physical interactions among the heat source, material, and manufacturing parameters in multi-physics simulation Star-CCM+. Then, the results from both the experiment and simulation were used to establish process maps for defect detection (lack of fusion porosity) and geometric precision (surface roughness).
The project was successfully completed with the goal of advancing the development of the computational fluid dynamics (CFD) melt pool simulation for powder DED with refractory alloy of C103. Notable accomplishments for this project are: 1) Development of high fidelity CFD melt pool simulation for refractory alloy of C103. 2) Obtained thermo-physical material properties of C103 from literature and calculated from thermo-dynamic simulation tool are used to validate the simulation accuracy in terms of a) scan speed, b) laser power, and c) hatch spacing with single layer multi-track builds and multi-layer multi-track builds. The predicted dimensions of the deposits are consistent in trend with the measured values, 3) The results from the experiments and simulations are used to establish process maps for defect detection and geometric precision. The map found the sweet spot among the process parameters. It indicates that larger hatch spacing can reduce production time due to a smaller number of tracks in each size range. Nevertheless, the trade-off involves an increase in surface roughness. Consequently, it is advisable to avoid excessively large hatch spacing, 4) Microstructure simulation was performed using CAFÉ model to link the melt pool simulation to the microstructure characteristics at various process conditions. The prediction shows a consistency in the grain morphology and growth pattern at defect-free condition.
The technical scope of this work aligns with a larger computational modeling framework aimed at enabling comprehensive simulation of powder DED to facilitate virtual process design and part quality certification. Also, the outcomes of simulations and data for C103 can be used to optimize the process parameters and tool path strategies to ensure the defect-free parts. The CFD melt pool simulation can potentially be integrated into an in-situ sensing system to provide valuable real-time predictions regarding melt pool dynamics and deposit quality in the future. The integration would enable closed-loop control to provide guidance to both the DED machine and the machine operator. The technology developed in the project supports the optimization of the powder DED process, reduces design lead time, and minimizes rejected parts.
Figure 1. CCAM and ORNL team developed computational framework aimed at enabling comprehensive simulation of powder DED to facilitate virtual process design and part quality certification. Process maps for defect detection and geometric precision is established based on the high-fidelity powder DED model and experimental data sets.
