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Purpose There is recent emphasis on designing new materials and alloys specifically for metal additive manufacturing (AM) processes, in contrast to AM of existing alloys that were developed for other traditional manufacturing methods involving considerably different physics. Process optimization to determine processing recipes for newly developed materials is expensive and time-consuming. The purpose of the current work is to use a systematic printability assessment framework developed by the co-authors to determine windows of processing parameters to print defect-free parts from a binary nickel-niobium alloy (NiNb5) using laser powder bed fusion (LPBF) metal AM. Design/methodology/approach The printability assessment framework integrates analytical thermal modeling, uncertainty quantification and experimental characterization to determine processing windows for NiNb5 in an accelerated fashion. Test coupons and mechanical test samples were fabricated on a ProX 200 commercial LPBF system. A series of density, microstructure and mechanical property characterization was conducted to validate the proposed framework. Findings Near fully-dense parts with more than 99% density were successfully printed using the proposed framework. Furthermore, the mechanical properties of as-printed parts showed low variability, good tensile strength of up to 662 MPa and tensile ductility 51% higher than what has been reported in the literature. Originality/value Although many literature studies investigate process optimization for metal AM, there is a lack of a systematic printability assessment framework to determine manufacturing process parameters for newly designed AM materials in an accelerated fashion. Moreover, the majority of existing process optimization approaches involve either time- and cost-intensive experimental campaigns or require the use of proprietary computational materials codes. Through the use of a readily accessible analytical thermal model coupled with statistical calibration and uncertainty quantification techniques, the proposed framework achieves both efficiency and accessibility to the user. Furthermore, this study demonstrates that following this framework results in printed parts with low degrees of variability in their mechanical properties.
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High-throughput Alloy and Process Design for Metal Additive Manufacturing
Designing alloys for additive manufacturing (AM) presents significant opportunities. Still, the chemical composition and processing conditions required for printability (ie., their suitability for fabrication via AM) are challenging to explore using solely experimental means. In this work, we develop a high-throughput (HTP) computational framework to guide the search for highly printable alloys and appropriate processing parameters. The framework uses material properties from stateof- the-art databases, processing parameters, and simulated melt pool profiles to predict processinduced defects, such as lack-of-fusion, keyholing, and balling. We accelerate the printability assessment using a deep learning surrogate for a thermal model, enabling a 1,000-fold acceleration in assessing the printability of a given alloy at no loss in accuracy when compared with conventional physics-based thermal models. We verify and validate the framework by constructing printability maps for the CoCrFeMnNi Cantor alloy system and comparing our predictions to an exhaustive ’in-house’ database. The framework enables the systematic investigation of the printability of a wide range of alloys in the broader Co-Cr-Fe-Mn-Ni HEA system. We identified the most promising alloys that were suitable for high-temperature applications and had the narrowest solidification ranges, and that was the least susceptible to balling, hot-cracking, and the formation of macroscopic printing defects. A new metric for the global printability of an alloy is constructed and is further used for the ranking of candidate alloys. The proposed framework is expected to be integrated into ICME approaches to accelerate the discovery and optimization of novel high-performance, printable alloys.
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- Award ID(s):
- 1849085
- PAR ID:
- 10488818
- Publisher / Repository:
- arXiv
- Date Published:
- Journal Name:
- arXivorg
- ISSN:
- 2331-8422
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Laser powder bed fusion (L-PBF) additive manufacturing (AM) is an effective method of fabricating nickel–titanium (NiTi) shape memory alloys (SMAs) with complex geometries, unique functional properties, and tailored material compositions. However, with the increase of Ni content in NiTi powder feedstock, the ability to produce high-quality parts is notably reduced due to the emergence of macroscopic defects such as warpage, elevated edge/corner, delamination, and excessive surface roughness. This study explores the printability of a nickel-rich NiTi powder, where printability refers to the ability to fabricate macro-defect-free parts. Specifically, single track experiments were first conducted to select key processing parameter settings for cubic specimen fabrication. Machine learning classification techniques were implemented to predict the printable space. The reliability of the predicted printable space was verified by further cubic specimens fabrication, and the relationship between processing parameters and potential macro-defect modes was investigated. Results indicated that laser power was critical to the printability of high Ni content NiTi powder. In the low laser power setting (P < 100 W), the printable space was relatively wider with delamination as the main macro-defect mode. In the sub-high laser power condition (100 W ≤ P ≤ 200 W), the printable space was narrowed to a low hatch spacing region with macro-defects of warpage, elevated edge/corner, and delamination happened at different scanning speeds and hatch spacing combinations. The rough surface defect emerged when further increasing the laser power (P > 200 W), leading to a further narrowed printable space.more » « less
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