Evidence map›Paper›PMID 37629971›Full record

ReviewMaterials (Basel, Switzerland)2023

Advances and Challenges in Predictive Modeling for Additive Manufacturing of Dissimilar Metals and Complex Alloys.

Debajyoti Adak, Praveen Sreeramagiri, Somnath Roy, Ganesh Balasubramanian

Abstract readReview
In one paragraph

Review in Materials (Basel, Switzerland), 2023. 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
–field-weighted citation impact
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.

  1. Article
  2. Review
  3. Review
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

4 authors.

Debajyoti AdakDepartment of Mechanical Engineering, Indian Institute of Technology, Kharagpur 721302, India.ORCID 0000-0002-8877-9973
Praveen SreeramagiriDepartment of Mechanical Engineering & Mechanics, Lehigh University, Bethlehem, PA 18015, USA.
Somnath RoyDepartment of Mechanical Engineering, Indian Institute of Technology, Kharagpur 721302, India.
Ganesh BalasubramanianDepartment of Mechanical Engineering & Mechanics, Lehigh University, Bethlehem, PA 18015, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present a scrutiny on the state of the art and applicability of predictive methods for additive manufacturing (AM) of metals, alloys, and compositionally complex metallic materials, to provide insights from the computational models for AM process optimization. Our work emphasizes the importance of manufacturing parameters on the thermal profiles evinced during processing, and the fundamental insights offered by the models used to simulate metal AM mechanisms. We discuss the methods and assumptions necessary for an educated tradeoff between the efficacy and accuracy of the computational approaches that incorporate multi-physics required to mimic the associated fluid flow phenomena as well as the resulting microstructures. Finally, the current challenges in the existing approaches are summarized and future scopes identified.

Indexed as

additive manufacturingmelt poolmicrostructure simulationprocess parameter optimizationthermal transport

Identifiers

PMID37629971
PMCPMC10456614

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.