Evidence map›Paper›PMID 40333352›Full record

ArticleMaterials (Basel, Switzerland)2025

Optimization of L-PBF Process Parameters for Defect Reduction and Mechanical Strength of Ni-Cr-Mo-Nb Superalloy Using Multi-Objective Methods.

Anton V Agapovichev, Alexander I Khaimovich, Vitaliy G Smelov, Viktoriya V Kokareva, Vyacheslav P Alekseev, Evgeny V Zemlyakov, Anton Y Kovchik

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Anton V AgapovichevEngine Production Technology Department, Samara National Research University, 34 Moskovskoye Shosse, 443086 Samara, Russia.ORCID 0000-0002-0042-7333
Alexander I KhaimovichEngine Production Technology Department, Samara National Research University, 34 Moskovskoye Shosse, 443086 Samara, Russia.
Vitaliy G SmelovEngine Production Technology Department, Samara National Research University, 34 Moskovskoye Shosse, 443086 Samara, Russia.ORCID 0000-0001-9556-6290
Viktoriya V KokarevaEngine Production Technology Department, Samara National Research University, 34 Moskovskoye Shosse, 443086 Samara, Russia.
Vyacheslav P AlekseevEngine Production Technology Department, Samara National Research University, 34 Moskovskoye Shosse, 443086 Samara, Russia.
Evgeny V ZemlyakovWorld-Class Research Center "Advanced Digital Technologies", State Marine Technical University, 190121 Saint Petersburg, Russia.
Anton Y KovchikWorld-Class Research Center "Advanced Digital Technologies", State Marine Technical University, 190121 Saint Petersburg, Russia.

Funding

Ministry of Science and Higher Education of the Russian Federation within the framework of the «World-class Science Center» program: Advanced digital technologies 075-15-2022-312, 20.04.2022
6 · The paper itself

Abstract

In the context of Additive Manufacturing (AM), particularly the Laser Powder Bed Fusion (L-PBF) technique, optimizing process parameters is essential for achieving dense, defect-free materials. This study investigates the optimization of L-PBF process parameters for a Ni-Cr-Mo-Nb-based superalloy using an integrated three-stage methodology. Stage A applies Grey Relational Analysis to identify the most favourable parameter sets. Stage B uses Response Surface Methodology to develop regression models that correlate process parameters with material characteristics, introducing the specific energy of layer fusion as a key factor. Stage C employs the Gradient Ascent Method to determine the global optimum using a desirability function. The proposed approach reduces the number of required experiments while ensuring optimal mechanical properties: yield strength of 774.73 ± 4.94 MPa, tensile strength of 1022.83 ± 5.19 MPa, and elongation at break of 23.1 ± 0.70%, with minimal LoF area (0.003 mm

Indexed as

gradient ascent methodgrey relational analysislaser powder bed fusionmulti-objective optimizationNi-Cr-Mo-Nb-based superalloyprintability windowprocess parametersresponse surface methodology

Identifiers

PMID40333352
PMCPMC12028714

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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.