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.
more »
« less
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.
more »
« less
- Award ID(s):
- 1849085
- NSF-PAR ID:
- 10488818
- Publisher / Repository:
- arXiv
- Date Published:
- Journal Name:
- arXivorg
- ISSN:
- 2331-8422
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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
-
more » « less
The complex solidification cycles experienced by multi-principal element alloys (MPEAs) during laser-based additive manufacturing (LBAM) often lead to structural defects that affect the build quality. The underlying thermal processes and phase transformations are a function of the process parameters employed. With a moving Gaussian heat source to mimic LBAM and leveraging material thermodynamics guidelines from CALculation of PHAse Diagrams (CALPHAD), we estimate the temperature-dependent thermal properties, phase fractions, and melt pool geometry using an experimentally validated computational fluid dynamics model. The results substantiate that the peak temperatures are inversely correlated to the scan speeds, and the melt pool dimensions can assist in the predictive selection of process parameters such as hatch distance and layer thickness. A relatively low cooling rate recorded during the process is ascribed to the preheating of the substrate to ensure printability of the alloy.
-
more » « less
The potential defects during the additive manufacturing (AM) process greatly deteriorate the mechanical properties of the fabricated structures and, as a result, increase the risks of part fatigue failure and even disasters. As laser additive manufacturing is such a complex process, many different physical phenomena such as electromagnetic radiation, optical and acoustic emission, and plasma generation will occur. Unlike vision and acoustic methods, the spectroscopy based smart optical monitoring system (SOMS) provides atomic level information revealing mechanical and chemical condition of the product. By monitoring plasma, multiple information such as line intensity, standard deviation, plasma temperature, or electron density, and by using different signal processing algorithms such as vector machine training or wavelet transforming, AM defects have been detected and classified. Utilizing two fiber optic components, a bifurcated fiber and a split fiber, the experimental results were performed to improve SOMS signal-to-noise ratio. Defects, including subsurface pores and sudden changes of process parameters including shielding gas shut-off and foreign substance, were identified by the spectroscopy based SOMS. For chemical composition characterization, a degree of dilution in terms of chemical element variation is identified by a spectral peak intensity ratio through the SOMS. It turned out that the information on the Cr/Fe ratio of deposit at a certain layer is vital to design the mechanical property in the IN625 deposition on the mild steel case. The SOMS has also demonstrated that the chemistry ratio can be determined from the calibration curve method based on the known alloy samples and that the ratio of the maximum intensities of multiple species provides more information about the quality of the alloy.
-
more » « less
Thermal conductivity (TC) is greatly influenced by the working temperature, microstructures, thermal processing (heat treatment) history and the composition of alloys. Due to computational costs and lengthy experimental procedures, obtaining the thermal conductivity for novel alloys, particularly parts made with additive manufacturing, is difficult and it is almost impossible to optimize the compositional space for an absolute targeted value of thermal conductivity. To address these difficulties, a machine learning method is explored to predict the TC of additive manufactured alloys. To accomplish this, an extensive thermal conductivity dataset for additively manufactured alloys was generated for several AM alloy families (nickel, copper, iron, cobalt-based) over various temperatures (300–1273 K). This unique dataset was used in training and validating machine learning models. Among the five different regression machine learning models trained with the dataset, extreme gradient boosting performs the best as compared with other models with an R2 score of 0.99. Furthermore, the accuracy of this model was tested using Inconel 718 and GRCop-42 fabricated with laser powder bed fusion-based additive manufacture, which have never been observed by the extreme gradient boosting model, and a good match between the experimental results and machine learning prediction was observed. The average mean error in predicting the thermal conductivity of Inconel 718 and GRCop-42 at different temperatures was 3.9% and 2.08%, respectively. This paper demonstrates that the thermal conductivity of novel AM alloys could be predicted quickly based on the dataset and the ML model.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- The DOI is not currently available.
