Evidence map›Paper›PMID 42280583›Full record

ArticlePolymers2026

Research on Bi-Objective Optimization of Injection Molding Process and Mechanical Anisotropy of Glass Fiber-Reinforced Polypropylene Fan Face Shell Based on RSM and NSGA-II.

Ming Yang, Sailong Yan, Jubao Liu, Feng Li, Jianfeng Yao, Yasheng Li

Abstract read
In one paragraph

Article in Polymers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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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.

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

6 authors.

Ming YangMechanical Science and Engineering College, Northeast Petroleum University, Daqing 163318, China.
Sailong YanMechanical Science and Engineering College, Northeast Petroleum University, Daqing 163318, China.
Jubao LiuMechanical Science and Engineering College, Northeast Petroleum University, Daqing 163318, China.
Feng LiHongfeng Intelligent Manufacturing (Shenzhen) Co., Ltd., Shenzhen 518117, China.
Jianfeng YaoHongfeng Intelligent Manufacturing (Shenzhen) Co., Ltd., Shenzhen 518117, China.
Yasheng LiMechanical Science and Engineering College, Northeast Petroleum University, Daqing 163318, China.

Funding

Heilongjiang Provincial Science and Technology Department LH2022A003Heilongjiang Provincial Science and Technology Department LH2022E018National Natural Science Foundation of China 11972114National Natural Science Foundation of China 52374034
6 · The paper itself

Abstract

Large glass fiber-reinforced polypropylene (GF-PP) shells are widely used in HVAC and automotive industries, but their injection molding suffers from severe warpage deformation, residual stress concentration, and inaccurate mechanical performance prediction due to neglected molding history. This study proposes an integrated optimization framework for a 30% GF-PP fan face shell. The optimal two-gate molding configuration was determined via Moldflow simulation. A Central Composite Design (CCD) combined with NSGA-II was used to optimize process parameters for minimizing warpage and residual stress. A Moldflow-Ansys co-simulation process was established to characterize fiber orientation-induced mechanical anisotropy, and full-scale mold trials were conducted for validation. The optimized process reduced maximum warpage by 58.03% (from 5.299 mm to 2.224 mm) and residual stress by 13.67% (from 54.93 MPa to 47.42 MPa). The average tensile modulus along the flow direction was 1.85 times that perpendicular to the flow direction. Mold trial results showed a warpage prediction error of only 7.583%. The proposed framework effectively addresses the critical quality issues in large GF-PP injection molding, providing a systematic engineering solution for molding quality control and accurate performance characterization.

Indexed as

glass fiber-reinforced polypropyleneinjection moldingmechanical anisotropymolding-structure co-simulationNSGA-IIresponse surface methodology

Identifiers

PMID42280583
PMCPMC13259024

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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.