Insight on precipitate evolution during additive manufacturing of stainless steels via in-situ heating-cooling experiments in a transmission electron microscope

Slama, Meriem Ben Haj; Yedra, Lluis; Héripré, Eva; Upadhyay, Manas Vijay

doi:10.1016/j.mtla.2022.101368

Cited by 9 publications

(11 citation statements)

References 38 publications

(64 reference statements)

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“…Previous investigations via TEM (Upadhyay et al, 2021;Ben Haj Slama et al, 2022) have revealed the presence of precipitates (oxide, non-oxide and mixed precipitates) at a length scale that is similar to the objects (precipitates or porosities) observed in TXM micrographs, although no distinction can be made between the objects in the TXM micrographs. Furthermore, while no porosities were observed in the TEM investigations (Upadhyay et al, 2021;Ben Haj Slama et al, 2022), it has to be reminded that TEM investigations only probe a very small volume within which porosities may not have been present. In comparison with TEM lamellae, micropillars have a volume that is four orders of magnitude higher.…”

Section: D Neural Network-based Segmentation Modelmentioning

confidence: 86%

“…This sample holder allows SSTC to be performed inside the controlled environment of the Titan3 TEM. Further information about the technique used for applied controlled SSTC is given by Ben Haj Slama et al (2022). The micropillars were characterized via TXM before and after each SSTC performed inside the TEM.…”

Section: Materials and Sample Preparationmentioning

confidence: 99%

“…A singletrack bidirectionally printed three-layer thin-wall of dimen-sions 100 mm Â 0.6 mm Â 0.6 mm (x, y, z) was manufactured using this machine; the process parameters were: laser power = 225 W, powder flow rate = 6.5 g min À1 , deposition speed = 2000 mm min À1 , vertical displacement of focusing head = 0.2 mm after depositing one layer. Additional details on the material, powder characteristics and specifications of the LMD machine have been given by Upadhyay et al (2021) andBen Haj Slama et al (2022).…”

Section: Materials and Sample Preparationmentioning

confidence: 99%

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3D deep convolutional neural network segmentation model for precipitate and porosity identification in synchrotron X-ray tomograms

Gaudez¹,

Slama²,

Kaestner³

et al. 2022

J Synchrotron Rad

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New developments at synchrotron beamlines and the ongoing upgrades of synchrotron facilities allow the possibility to study complex structures with a much better spatial and temporal resolution than ever before. However, the downside is that the data collected are also significantly larger (more than several terabytes) than ever before, and post-processing and analyzing these data is very challenging to perform manually. This issue can be solved by employing automated methods such as machine learning, which show significantly improved performance in data processing and image segmentation than manual methods. In this work, a 3D U-net deep convolutional neural network (DCNN) model with four layers and base-8 characteristic features has been developed to segment precipitates and porosities in synchrotron transmission X-ray micrograms. Transmission X-ray microscopy experiments were conducted on micropillars prepared from additively manufactured 316L steel to evaluate precipitate information. After training the 3D U-net DCNN model, it was used on unseen data and the prediction was compared with manual segmentation. A good agreement was found between both segmentations. An ablation study was performed and revealed that the proposed model showed better statistics than other models with lower numbers of layers and/or characteristic features. The proposed model is able to segment several hundreds of gigabytes of data in a few minutes and could be applied to other materials and tomography techniques. The code and the fitted weights are made available with this paper for any interested researcher to use for their needs (https://github.com/manasvupadhyay/erc-gamma-3D-DCNN).

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Section: D Neural Network-based Segmentation Modelmentioning

confidence: 86%

Section: Materials and Sample Preparationmentioning

confidence: 99%

Section: Materials and Sample Preparationmentioning

confidence: 99%

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3D deep convolutional neural network segmentation model for precipitate and porosity identification in synchrotron X-ray tomograms

Gaudez¹,

Slama²,

Kaestner³

et al. 2022

J Synchrotron Rad

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“…Further straightforward investigation and understanding of solid‐state thermal cycling‐driven evolution of precipitates have been conducted recently, where the precipitation behavior of oxide and nonoxide precipitates in an LDED‐built 316L austenitic steel during cooling/heating cycles (with cooling and heating rates up to 4000 k s −1 ) were in situ observed by using transmission electron microscopy. [ 12 ] However, this in situ precipitate behavior was observed in commercial materials, and there lacks work to customize new materials by fully exploiting the IHT to exempt PHT. To this end, the development of LAM‐customized new materials with in situ precipitation strengthening capability to potentially eliminate PHT by utilizing unique thermal history in LAM deposition provides insights into the booming of new materials for LAM.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Customized Novel Material for Energy‐Efficient 4D Printing

Tan

Yao

et al. 2023

Advanced Science

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Existing commercial powders for laser additive manufacturing (LAM) are designed for traditional manufacturing methods requiring post heat treatments (PHT). LAM's unique cyclic thermal history induces intrinsic heat treatment (IHT) on materials during deposition, which offers an opportunity to develop LAM-customized new materials. This work customized a novel Fe-Ni-Ti-Al maraging steel assisted by machine learning to leverage the IHT effect for in situ forming massive precipitates during LAM without PHT. Fast precipitation kinetics in steel, tailored intermittent deposition strategy, and the IHT effect facilitate the in situ Ni 3 Ti precipitation in the martensitic matrix via heterogeneous nucleation on high-density dislocations. The as-built steel achieves a tensile strength of 1538 MPa and a uniform elongation of 8.1%, which is superior to a wide range of as-LAM-processed high-strength steel. In the current mainstream ex situ 4D printing, the time-dependent evolutions (i.e., property or functionality changes) of a 3D printed structure occur after part formation. This work highlights in situ 4D printing via the synchronous integration of time-dependent precipitation hardening with 3D geometry shaping, which shows high energy efficiency and sustainability. The findings provide insight into developing LAM-customized materials by understanding and utilizing the IHT-materials interaction.

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“…Oxide precipitates formed during the gas atomization process can persist in the liquid phase during AM [3], and new oxides can nucleate and grow during the rapid solidification phase of an AM process as shown via mean field nucleation and growth models [7]. Along with the oxides, non-oxide precipitates can also nucleate and grow during solidification, often in the vicinity of the oxides [4,12].…”

Section: Introductionmentioning

confidence: 99%

A synchrotron transmission X-ray microscopy study on precipitate evolution during solid-state thermal cycling of a stainless steel

Gaudez

Slama

Héripré

et al. 2023

Additive Manufacturing

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Insight on precipitate evolution during additive manufacturing of stainless steels via in-situ heating-cooling experiments in a transmission electron microscope

Cited by 9 publications

References 38 publications

3D deep convolutional neural network segmentation model for precipitate and porosity identification in synchrotron X-ray tomograms

3D deep convolutional neural network segmentation model for precipitate and porosity identification in synchrotron X-ray tomograms

Machine Learning Customized Novel Material for Energy‐Efficient 4D Printing

A synchrotron transmission X-ray microscopy study on precipitate evolution during solid-state thermal cycling of a stainless steel

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