Evidence map›Paper›PMID 42332102›Full record

ArticleScientific reports2026

Multi-objective optimization of dimensional accuracy and part weight in injection molding of a 3D curved shin guard plate.

Son Hoang, Vinh Quang Pham, Cong Chi Tran

Abstract read
In one paragraph

Article in Scientific reports, 2026. 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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0cells of the map it votes in
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.

No citing paper in PubMed yet.

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

3 authors.

Son HoangFaculty of Electronics Engineering 1, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam.
Vinh Quang PhamInstitute of Optics, Mechanics and Specialized Materials, Department of Security Industry, Ministry of Public Security, Hanoi, Vietnam.
Cong Chi TranFaculty of Electromechanical and Civil Engineering, Vietnam National University of Forestry, Hanoi, Vietnam. trancongchi_bk@yahoo.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Injection molding of complex three-dimensional curved components is highly sensitive to processing conditions, particularly when dimensional accuracy and packing quality must be balanced simultaneously. This study proposes an integrated optimization and decision-making framework to improve the molding quality of a commercial curved shin guard plate (SGP) by combining Response Surface Methodology (RSM), adaptive non-dominated sorting genetic algorithm II (NSGA-II), and entropy-weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). A Box-Behnken design was employed to investigate the effects of packing pressure (PP), melt temperature (MT), and cooling time (CT) on absolute dimensional deviation (ΔD) and molded part weight (PW). The results showed that the developed quadratic response surface models exhibited good predictive capability, and analysis of variance identified PP as the most influential parameter. The surrogate models were integrated with standard and adaptive NSGA-II algorithms to perform multi-objective optimization and generate well-distributed Pareto-optimal solutions, with the adaptive NSGA-II showing improved Pareto-front exploration capability based on crowding-distance and hypervolume analyses. Entropy-weighted TOPSIS was subsequently applied to identify the most suitable compromise solution. Experimental validation was conducted at the TOPSIS-selected optimum together with two additional Pareto-optimal conditions. The validation results showed satisfactory agreement between predicted and experimental responses, with deviations remaining below 7.41% for ΔD and below 1.70% for PW. These results demonstrate the practical applicability of the proposed optimization framework for complex injection-molded components under industrial manufacturing conditions.

Indexed as

Entropy-weighted TOPSISInjection moldingMulti-objective optimizationNSGA-IIResponse surface methodology

Identifiers

PMID42332102
PMCPMC13483907

What OpenQuestion holds

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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.