New insights into the origin of fine equiaxed microstructures in additively manufactured Inconel 718

Cazic, I.; Zollinger, Julien; Mathieu, Sandrine; Kandaoui, Maxime El; Plapper, Peter; Appolaire, Benoît

doi:10.1016/j.scriptamat.2021.113740

Cited by 31 publications

(22 citation statements)

References 27 publications

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“…Recently the observation of 3 orientation relationships between chains of equiaxed grains in noninoculated alloys: Al-Zn-Cr [7], Au-Cu-Ir [9], Ni-based alloys [10], has revealed the so far undetected possibility for fcc crystals to co-nucleate from adjacent facets of icosahedral motifs, which crystallographically leads to their above-mentioned mutual twin orientation relationships. Such an observation looked paradoxical, as enhanced diffusion was understood to be the mechanism that could destroy iSRO and allow for the big rearrangement leading to crystalline order.…”

Section: Impact Of Icosahedral Short and Medium Range Order In Alloy ...mentioning

confidence: 99%

Medium Range Ordering in liquid Al-based alloys: towards a machine learning approach of solidification

Jarry

Jakse

2023

IOP Conf. Ser.: Mater. Sci. Eng.

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Ab initio molecular dynamic simulations of liquid Al93Cr7 and Al83Zn10Cr7 alloy have evidenced the presence of an icosahedral short range order (iSRO) which develops into icosahedral medium range order (iMRO) as the melt is undercooled. This atomic arrangement accounts for the presence of Dynamic Heterogeneities characterized by Al fast-dynamics regions and Cr-rich slow-dynamics regions. Characterisation of the medium range order was carried out by a direct connectivity approach. However, given the small size of the simulation (256 atoms), such characterisation remains partial. In order to better describe both iMRO formation and more dilute alloys closer to industrial compositions, a new modelling strategy has been initiated to allow in the long term for large-scale atomic-level simulations. Molecular Dynamics (MD) of million to billion atoms may indeed lead to meaningful results. Exploitation of such large amounts of MD-generated big data can be carried out by means of Machine Learning (ML) tools which provide relevant and powerful analysis methods. An unsupervised ML approach based on topological descriptors using persistent homology concepts is proposed to reveal the structural features of atomic arrangements without a priori knowledge on the studied system. This approach has been applied so far to pure Al melts. Both translational and orientational orderings are thus evidenced together with nucleation pathways, whose revealed features are beyond the hypotheses of the Classical Nucleation Theory.

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Section: Impact Of Icosahedral Short and Medium Range Order In Alloy ...mentioning

confidence: 99%

Medium Range Ordering in liquid Al-based alloys: towards a machine learning approach of solidification

Jarry

Jakse

2023

IOP Conf. Ser.: Mater. Sci. Eng.

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“…In this section, the proposed MPC is tested using the process parameters and material properties of Inconel ® 718 [43] that are given in Table 4. The linearized model discussed in Section 3.1, for these parameter values, is given by the following state-space matrices, A = [−0.2262]; A is the system matrix.…”

Section: Layer Height Controllermentioning

confidence: 99%

Modeling and Control of Layer Height in Laser Wire Additive Manufacturing

et al. 2022

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Laser Wire Additive Manufacturing (LWAM) is a flexible and fast manufacturing method used to produce variants of high metal geometric complexity. In this work, a physics-based model of the bead geometry including process parameters and material properties was developed for the LWAM process of large-scale products. The developed model aimed to include critical process parameters, material properties and thermal history to describe the relationship between the layer height with different process inputs (i.e., the power, the standoff distance, the temperature, the wire-feed rate, and the travel speed). Then, a Model Predictive Controller (MPC) was designed to keep the layer height trajectory constant taking into consideration the constraints faced in the LWAM technology. Experimental validation results were performed to check the accuracy of the proposed model and the results revealed that the developed model matches the experimental data. Finally, the designed MPC controller was able to track a predefined layer height reference signal by controlling the temperature input of the system.

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“…As mentioned before, these parameters are laser power (P), wire feed rate (F), and travel speed (V), as presented in Table 1. The choices for each of these five sets of parameters result from a test campaign for the development of the process with a wire of 1.2 mm diameter in Inconel ® 718 [45]. These five sets of parameters frame the stability domain of the process, i.e., they allow stable deposition with different bead morphologies, which is the parameter of interest for our study.…”

Section: First-order Deposition Parametersmentioning

confidence: 99%

Bead Geometry Prediction in Laser-Wire Additive Manufacturing Process Using Machine Learning: Case of Study

et al. 2021

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In Laser Wire Additive Manufacturing (LWAM), the final geometry is produced using the layer-by-layer deposition (beads principle). To achieve good geometrical accuracy in the final product, proper implementation of the bead geometry is essential. For this reason, the paper focuses on this process and proposes a layer geometry (width and height) prediction model to improve deposition accuracy. More specifically, a machine learning regression algorithm is applied on several experimental data to predict the bead geometry across layers. Furthermore, a neural network-based approach was used to study the influence of different deposition parameters, namely laser power, wire-feed rate and travel speed on bead geometry. To validate the effectiveness of the proposed approach, a test split validation strategy was applied to train and validate the machine learning models. The results show a particular evolutionary trend and confirm that the process parameters have a direct influence on the bead geometry, and so, too, on the final part. Several deposition parameters have been found to obtain an accurate prediction model with low errors and good layer deposition. Finally, this study indicates that the machine learning approach can efficiently be used to predict the bead geometry and could help later in designing a proper controller in the LWAM process.

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New insights into the origin of fine equiaxed microstructures in additively manufactured Inconel 718

Cited by 31 publications

References 27 publications

Medium Range Ordering in liquid Al-based alloys: towards a machine learning approach of solidification

Medium Range Ordering in liquid Al-based alloys: towards a machine learning approach of solidification

Modeling and Control of Layer Height in Laser Wire Additive Manufacturing

Bead Geometry Prediction in Laser-Wire Additive Manufacturing Process Using Machine Learning: Case of Study

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