An official website of the United States government
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
Exploration of Support Structure Design for Additive Manufacturing at a Major OEM: a Case Study
Abstract The support structures required in many forms of additive manufacturing are often seen as waste that is tolerated as necessary. In metal additive processes, cost is frequently reduced by minimizing the amount of support structures needed to produce a part so that in turn, material use is decreased. However, there still exists the challenge of generating parts that are not deformed by the stresses created in the process. In this case study, support structures were leveraged to address deformation. A part was printed via direct metal laser melting with supports with a high grouping density in areas of high anticipated deformation in order to stiffen the part to prevent deformation. Then, they were printed again with a low grouping density to allow the part to relax and reduce stress. Combinations of support strategy and leaving supports on during post processing were used to investigate the effects of keeping or removing the supports in post-print operations such as surface treatment. The two optimized support strategies saw a lower deformation than the baseline approach to supports, and the releasing strategy was closest to the reference solid model with a 26% reduction in average deformation. The results suggest that the support structures in additively manufactured parts have a different impact on the part than the original intent of the supports to simply alleviate a process requirement. The support structures should be used to impact the final part geometry.
more »
« less
- Award ID(s):
- 1829008
- PAR ID:
- 10349493
- Date Published:
- Journal Name:
- Design Engineering Technical Conference
- Volume:
- 5
- Page Range / eLocation ID:
- V005T05A002; 9 pages
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Binder jetting is a powder bed additive manufacturing process where an object is created by depositing liquid binder onto the surface of powder, selectively binding particles in each layer. The quality of the as-printed parts is influenced not only by process parameters such as layer thickness, binder saturation, print speed, and drying time but also by the location within the build box. This study highlights the location-dependent nature of green density and dimensional accuracy in the as-printed samples, and the observed trends are thoroughly discussed. A conventional powder spreading using a single roller was compared with a double roller to maximize powder packing and bed uniformity prior to binder jetting process. The significance of these observations lies in their impact on densification behavior, shrinkage, and the final geometry of the printed part.more » « less
-
Wire arc additive manufacturing (WAAM) presents a highly promising alternative to conventional subtractive manufacturing methods to produce metallic components, particularly in the aerospace industry, where there is a demand for 17–4 precipitation-hardened (PH) stainless steel structures. This study focuses on investigating the microstructural characteristics, showing microhardness evaluations, and analyzing the tensile properties of the as-printed parts during the 17–4 PH manufacturing process at different locations and directions. The fabrication is carried out using gas metal wire arc additive manufacturing (GM-WAAM). As a result, it was found that the microstructure of the as-deposited part showed a complex configuration consisting of both finely equiaxed and coarsely formed δ-ferrite phases with vermicular and lathy morphologies. These phases were dispersed inside the martensitic matrix, while a small amount of retained austenite was also present. It was observed that the volume fraction of retained austenite (20–5%) and δ-ferrite phases (15.5–2.5%) decreased gradually from the bottom to the top of the as-deposited wall. This reduction in the fractions of these phases resulted in a progressive increase in both hardness (∼37%) and ultimate tensile strength (UTS) along the building direction. This study successfully fabricates a high-strength and ductile 17–4 PH as-printed part using WAAM. The findings provide evidence supporting the feasibility of employing WAAM for producing defect-free, high-strength components on a large scale while maintaining mechanical properties similar or better than wrought alloy 17–4 PH.more » « less
-
The goal of this work to mitigate flaws in metal parts produced from laser powder bed fusion (LPBF) additive manufacturing (AM) process. As a step towards this goal, the objective of this work is to predict the build quality of a part as it is being printed via deep learning of in-situ layer-wise images obtained from an optical camera instrumented in the LPBF machine. To realize this objective, we designed a set of thin-wall features (fins) from Titanium alloy (Ti-6Al-4V) material with varying length-to-thickness ratio. These thin-wall test parts were printed under three different build orientations and in-situ images of their top surface were acquired during the process. The parts were examined offline using X-ray computed tomography (XCT), and their build quality was quantified in terms of statistical features, such as the thickness and consistency of its edges. Subsequently, a deep learning convolutional neural network (CNN) was trained to predict the XCT-derived statistical quality features using the layer-wise optical images of the thin-wall part as inputs. The statistical correlation between CNN-based predictions and XCT-observed quality measurements exceeds 85%. This work has two outcomes consequential to the sustainability of additive manufacturing: (1) It provides practitioners with a guideline for building thin-wall features with minimal defects, and (2) the high correlation between the offline XCT measurements and in-situ sensor-based quality metrics substantiates the potential for applying deep learning approaches for the real-time prediction of build flaws in LPBF.more » « less
-
Abstract Binder jetting is an additive manufacturing process utilizing a liquid-based binding agent to selectively join the material in a powder bed. It is capable of manufacturing complex-shaped parts from a variety of materials including metals, ceramics, and polymers. This paper provides a comprehensive review on currently available reports on metal binder jetting from both academia and industry. Critical factors and their effects in metal binder jetting are reviewed and divided into two categories, namely material-related factors and process-related parameters. The reported data on density, dimensional and geometric accuracy, and mechanical properties achieved by metal binder jetting are summarized. With parameter optimization and a suitable sintering process, ten materials have been proven to achieve a relative density of higher than 90%. Indepth discussion is provided regarding densification as a function of various attributes of powder packing, printing, and post-processing. A few grades of stainless steel obtained equivalent or superior mechanical properties compared to cold working. Although binder jetting has gained its popularity in the past several years, it has not been sufficiently studied compared with other metal additive manufacturing (AM) processes such as powder bed fusion and directed energy deposition. Some aspects that need further research include the understanding of powder spreading process, binder-powder interaction, and part shrinkage.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/DETC2021-69818
