skip to main content

Title: Quantitative microstructure analysis for solid-state metal additive manufacturing via deep learning
Metal additive manufacturing (AM) provides a platform for microstructure optimization via process control, but establishing a quantitative processing-microstructure linkage necessitates an efficient scheme for microstructure representation and regeneration. Here, we present a deep learning framework to quantitatively analyze the microstructural variations of metals fabricated by AM under different processing conditions. The principal microstructural descriptors are extracted directly from the electron backscatter diffraction patterns, enabling a quantitative measure of the microstructure differences in a reduced representation domain. We also demonstrate the capability of predicting new microstructures within the representation domain using a regeneration neural network, from which we are able to explore the physical insights into the implicitly expressed microstructure descriptors by mapping the regenerated microstructures as a function of principal component values. We validate the effectiveness of the framework using samples fabricated by a solid-state AM technology, additive friction stir deposition, which typically results in equiaxed microstructures.
; ; ;
Award ID(s):
Publication Date:
Journal Name:
Journal of Materials Research
Page Range or eLocation-ID:
1936 to 1948
Sponsoring Org:
National Science Foundation
More Like this
  1. Owing to the reduced defects, low cost, and high efficiency, the additive manufacturing (AM) technique has attracted increasingly attention and has been applied in high-entropy alloys (HEAs) in recent years. It was found that AM-processed HEAs possess an optimized microstructure and improved mechanical properties. However, no report has been proposed to review the application of the AM method in preparing bulk HEAs. Hence, it is necessary to introduce AM-processed HEAs in terms of applications, microstructures, mechanical properties, and challenges to provide readers with fundamental understanding. Specifically, we reviewed (1) the application of AM methods in the fabrication of HEAs andmore »(2) the post-heat treatment effect on the microstructural evolution and mechanical properties. Compared with the casting counterparts, AM-HEAs were found to have a superior yield strength and ductility as a consequence of the fine microstructure formed during the rapid solidification in the fabrication process. The post-treatment, such as high isostatic pressing (HIP), can further enhance their properties by removing the existing fabrication defects and residual stress in the AM-HEAs. Furthermore, the mechanical properties can be tuned by either reducing the pre-heating temperature to hinder the phase partitioning or modifying the composition of the HEA to stabilize the solid-solution phase or ductile intermetallic phase in AM materials. Moreover, the processing parameters, fabrication orientation, and scanning method can be optimized to further improve the mechanical performance of the as-built-HEAs.« less
  2. Production-volume and cost requirements currently limit machine tool manufacturers’ ability to produce application-specific tooling with traditional methods, motivating the development of innovative manufacturing technologies. To this end, we detail a manufacturing framework for the design and production of application-specific cutting tools based on industry standard tungsten carbide-cobalt (WC-Co)-based “carbide” cutting materials using additive manufacturing (AM). Herein, novel diamond-reinforced carbide structures were designed and manufactured via AM and subsequently tested in comparison to current commercial products that are traditionally-processed. The resulting diamond-reinforced composites were free from large scale cracking and maintained microstructures with multiple reinforcing phases. Diamond incorporation had a remarkablemore »effect on the processing, microstructure, and machining performance of the WC-Co based material in comparison to a commercial carbide cutting tool of similar composition as well as the base WC-Co matrix. Detailed microstructure and phase analysis, as well as machining experiments, demonstrate the ability to exploit laser-based directed energy deposition (DED)-based AM to create multifunctional cutting tools that can be designed to meet ever-increasing manufacturing demands across many industries.« less
  3. 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 analyticalmore »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.« less
  4. 316L stainless steel (SS) to Al12Si aluminum alloy structures were processed, tailoring the compositionally graded interface on a SS 316 substrate using a directed energy deposition (DED)-based additive manufacturing (AM) process. Applying such a compositionally graded transition on bimetallic materials, especially joining two dissimilar metals, could avoid the mechanical property mismatch. This study's objective was to understand the processing parameters that influence the properties of AM processed SS 316L to Al12Si bimetallic structures. Two different approaches fabricated these bimetallic structures. The results showed no visible defects on the as-fabricated samples using 4 layers of Al-rich mixed composition as the transitionmore »section. The microstructural characterization showed a unique morphology in each section. Both cooling rate and compositional variations caused microstructural variation. FeAl, Fe2Al5, and FeAl3 intermetallic phases were formed at the compositionally graded transition section. After stress relief heat-treatment of SS 316L/Al12Si bimetallic samples, diffused intermetallic phases were seen at the compositionally graded transition. At the interface, as processed, bimetallic structures had a microhardness value of 834.2 ± 107.1 HV0.1, which is a result of the FeAl3 phase at the compositionally graded transition area. After heat-treatment, the microhardness value reduced to 578.7 ± 154.1 HV0.1 due to more Fe dominated FexAly phase formation. The compression test results showed that the non-HT and HT SS 316L/Al12Si bimetallic structures had a similar maximum compressive strength of 299.4 ± 22.1 MPa and 270.1 ± 27.1 MPa, respectively.« less
  5. Additive friction stir deposition (AFSD) is an emerging solid-state metal additive manufacturing technology renowned for strong interface adhesion and isotropic mechanical properties. This is postulated to result from the material flow phenomena near the interface, but experimental corroboration has remained absent. Here, we seek to understand the interface formed in AFSD via morphological and microstructural investigation, wherein the non-planar interfacial morphology is characterized on the track-scale (centimeter scale) using X-ray computed tomography and the material deformation history is explored by microstructure mapping at the interfacial regions. X-ray computed tomography reveals unique 3D features at the interface with significant macroscopic materialmore »mixing. In the out-of-plane direction, the deposited material inserts below the initial substrate surface in the feed-rod zone, while the substrate surface surges upwards in the tool protrusion-affected zone. Complex 3D structures like fins and serrations form on the advancing side, leading to structural interlocking; on the retreating side, the interface manifests as a smooth sloped surface. Microstructure mapping reveals a uniform thermomechanical history for the deposited material, which develops a homogeneous, almost fully recrystallized microstructure. The substrate surface develops partially recrystallized microstructures that are location-dependent; more intra-granular orientation gradients are found in the regions further away from the centerline of the deposition track. From these observations, we discuss the mechanisms for interfacial material flow and interface morphology formation during AFSD.« less