Evidence map›Paper›PMID 38675305›Full record

ArticleMicromachines2024

Microstructure, Tensile Properties, and Fracture Toughness of an In Situ Rolling Hybrid with Wire Arc Additive Manufacturing AerMet100 Steel.

Lei Lei, Linda Ke, Yibo Xiong, Siyu Liu, Lei Du, Mengfan Chen, Meili Xiao, Yanfei Fu, Fei Yao, Fan Yang and 2 more

Open access · goldAbstract read
In one paragraph

Article in Micromachines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.9field-weighted citation impact, top 16% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Lei LeiShanghai Engineering Technology Research Center of Near-Net-Shape Forming for Metallic Materials, Shanghai Spaceflight Precision Machinery Institute, Shanghai 201600, China.
Linda KeShanghai Engineering Technology Research Center of Near-Net-Shape Forming for Metallic Materials, Shanghai Spaceflight Precision Machinery Institute, Shanghai 201600, China.
Yibo XiongShanghai Engineering Technology Research Center of Near-Net-Shape Forming for Metallic Materials, Shanghai Spaceflight Precision Machinery Institute, Shanghai 201600, China.
Siyu LiuShanghai Engineering Technology Research Center of Near-Net-Shape Forming for Metallic Materials, Shanghai Spaceflight Precision Machinery Institute, Shanghai 201600, China.
Lei DuShanghai Engineering Technology Research Center of Near-Net-Shape Forming for Metallic Materials, Shanghai Spaceflight Precision Machinery Institute, Shanghai 201600, China.
Mengfan ChenShanghai Engineering Technology Research Center of Near-Net-Shape Forming for Metallic Materials, Shanghai Spaceflight Precision Machinery Institute, Shanghai 201600, China.
Meili XiaoShanghai Engineering Technology Research Center of Near-Net-Shape Forming for Metallic Materials, Shanghai Spaceflight Precision Machinery Institute, Shanghai 201600, China.
Yanfei FuShanghai Engineering Technology Research Center of Near-Net-Shape Forming for Metallic Materials, Shanghai Spaceflight Precision Machinery Institute, Shanghai 201600, China.
Fei YaoShanghai Engineering Technology Research Center of Near-Net-Shape Forming for Metallic Materials, Shanghai Spaceflight Precision Machinery Institute, Shanghai 201600, China.
Fan YangShanghai Engineering Technology Research Center of Near-Net-Shape Forming for Metallic Materials, Shanghai Spaceflight Precision Machinery Institute, Shanghai 201600, China.
Kun WangShanghai Engineering Technology Research Center of Near-Net-Shape Forming for Metallic Materials, Shanghai Spaceflight Precision Machinery Institute, Shanghai 201600, China.
Baohui LiShanghai Engineering Technology Research Center of Near-Net-Shape Forming for Metallic Materials, Shanghai Spaceflight Precision Machinery Institute, Shanghai 201600, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As a type of ultra-high strength steel, AerMet100 steel is used in the aerospace and military industries. Due to the fact that AerMet100 steel is difficult to machine, people have been exploring the process of additive manufacturing to fabricate AerMet100 steel. In this study, AerMet100 steel was produced using an in situ rolling hybrid with wire arc additive manufacturing. Microstructure, tensile properties, and fracture toughness of as-deposited and heat-treated AerMet100 steel were evaluated in different directions. The results reveal that the manufacturing process leads to grain fragmentation and obvious microstructural refinement of the AerMet100 steel, and weakens the anisotropy of the mechanical properties. After heat treatment, the microstructure of the AerMet100 steel is mainly composed of lath martensite and reversed austenite. Alloy carbides are precipitated within the martensitic matrix, and a high density of dislocations is the primary strengthening mechanism. The existence of film-like austenite among the martensite matrix enhances the toughness of AerMet100 steel, which coordinates stress distribution and restrains crack propagation, resulting in an excellent balance between strength and toughness. The AerMet100 steel with in situ rolling is isotropy and achieves the following values: an average ultimate strength of 1747.7 ± 16.3 MPa, yield strength of 1615 ± 40.6 MPa, elongation of 8.3 ± 0.2% in deposition direction, and corresponding values in the building direction are 1821.3 ± 22.1 MPa, 1624 ± 84.5 MPa, and 7.6 ± 1.7%, and the K

Indexed as

AerMet100 steelfracture toughnessin situ rollingtensile propertywire arc additive manufacturing

Identifiers

PMID38675305
PMCPMC11052116
OpenAlexW4393866264

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.