Binder Jet 3D Printing of 316L Stainless Steel: Orthogonal Printing and Sintering Process Optimization

Chen, Ling; Chen, Weiping; Fu, Zhaohui; Ding, Guangxin; Chen, Zhiping; Zhu, Daoben

doi:10.1002/adem.202200641

Cited by 5 publications

(3 citation statements)

References 38 publications

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“…Nanolithography is a powerful technique, which enables the production of particles possessing very complex morphologies, with relative ease and high precision. [ 285 , 286 ] Nanolithography is one of the main methods of producing metamaterials, [ 285 , 287 ] to which most of the ML-based studies on chiral materials are devoted. These studies mainly use computer-simulated data, without resorting to the fabrication of real materials, but optimized structures can be easily discovered and subsequently fabricated by nanolithography.…”

Section: Chiral Nanomaterialsmentioning

confidence: 99%

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Kuznetsova,

Coogan,

Botov

et al. 2024

Advanced Materials

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Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating the design of novel materials, and reducing the need for time‐consuming and labour‐intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials and nanostructures is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarised luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, we provide an analysis of machine learning methods used in studying achiral nanomaterials, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. We present an overview of chiral nanomaterials within the framework of synthesis‐structure‐property‐application relationships and provide insights on how to leverage machine learning for the study of these highly complex relationships. We also review and discuss some key recent publications on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.This article is protected by copyright. All rights reserved

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Section: Chiral Nanomaterialsmentioning

confidence: 99%

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Kuznetsova,

Coogan,

Botov

et al. 2024

Advanced Materials

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“…It can be observed that the sintered sample contains two phases, γ-austenite (γ-Fe) and δ-ferrite (δ-Fe) phase, which is consistent with EDS elemental analysis. [29] This is due to the fact that during the sintering process, the austenite promoting elements (usually Ni and C) diffuse within the γ-Fe matrix, and the ferrite promoting elements (usually Cr and Mo) diffuse at grain borders. The peak positions remain basically the same at different sintering temperatures.…”

Section: Chemical Composition Analysismentioning

confidence: 99%

A Low‐Binder‐Content Ink System for 3D Printing High‐Density and Small Feature Size 316L Stainless Steel Parts

Guo,

Qin,

Zhou

et al. 2023

Adv Eng Mater

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Binder ink system is the backbone of binder jet 3D printing (BJ‐3DP) technology. Comparing with metal injection molding (MIM), BJ‐3DP needs much less amount of binder, which means less exhaust gas generation during the debinding process and more environmental‐friendly. More than that, low content of binder is also supposed to benefit to the structure properties of the printed metal parts. In this paper, an ultralow‐binder‐content ethanol‐water‐PVP ink system was demonstrated for the BJ‐3DP 316L stainless steel parts by applying thermal bubble inkjet technology. The PVP binder concentration is as low as 80 mg/ml, which can achieve an ultralow solid content of 0.2 wt% in the printed green part. The two‐component solution with water and ethanol provides not only the rheology property adjustment freedom for the ink jetting and infiltration in the metal powders, but also the two boiling points for the step‐by‐step capillary bridge formation process. By applying the low‐binder‐content ink, the fully densified 316L stainless steel parts after sintering can obtain the relative density up to 99.4%, Vickers hardness up to 185 HV, tensile strength up to 542 MPa, 25% elongation at break, and feature size as small as 200 μm.This article is protected by copyright. All rights reserved.

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“…In recent years, additive manufacturing (AM, also known as 3D-printing) has become a popular technique to manufacture products made of stainless steels due to its high speed and low cost. [3][4][5] AM also offers good properties such as generating less waste and being more flexible in producing complex parts. Among the AM techniques, the laser powder bed fusion (LPBF) is mostly used for processing 316L SS products.…”

Section: Introductionmentioning

confidence: 99%

Cyclic potentiodynamic passivation of 316L stainless steels of different crystallographic orientation produced by laser powder bed fusion: Towards the improvement of corrosion resistance

Trisnanto

Brochu

et al. 2023

Can J Chem Eng

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The influence of cyclic potentiodynamic passivation (CPP) of 316L stainless steels (SS) of different crystallographic orientation produced by laser powder bed fusion (LPBF) on the resulting general and pitting corrosion resistance is discussed. CPP was performed by cyclic voltammetry in aqueous 0.1 M NaNO3. Electrochemical tests including open circuit potential (OCP), electrochemical impedance spectroscopy (EIS), and linear potentiodynamic polarization were employed to evaluate the resulting corrosion properties of the surfaces in aqueous 3.5 wt.% NaCl. It was found that the CPP method enables the formation of a passive oxide surface film which significantly improved the materials' general and pitting corrosion resistance in comparison to the naturally‐formed passive film under the experimental conditions investigated. It was also found that the general corrosion resistance, for both the unmodified (naturally‐passivated) and CPP‐modified LPBF 316L samples, decreased in the order of {111} > {100} > polycrystalline > {110}. Although the CPP‐modified samples showed a significantly lower current in the passive region and higher pitting potentials, in comparison to the unmodified samples, their crystalline structure was found not to have any influence on the corresponding behaviours.

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Binder Jet 3D Printing of 316L Stainless Steel: Orthogonal Printing and Sintering Process Optimization

Cited by 5 publications

References 38 publications

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

A Low‐Binder‐Content Ink System for 3D Printing High‐Density and Small Feature Size 316L Stainless Steel Parts

Cyclic potentiodynamic passivation of 316L stainless steels of different crystallographic orientation produced by laser powder bed fusion: Towards the improvement of corrosion resistance

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