Determination of Acceptance Criteria for Aircraft Wet-Molding Composite Materials’ Void Content

Currently, in the realm of wet vacuum bagging (WVB) molding composites for general aviation aircraft, there remains a deficiency of systematic methods and specification guidelines for establishing void content (VC) acceptance standards. This study employs an improved acid digestion method to test the VC of RX4E aircraft composite structures. It was discovered that the average VC exceeds 3.0%, which is significantly higher than the traditional empirical standard of 1% or 2%. Through repetitive testing, process traceability verification, and comparison with literature data, it has been confirmed that the aforementioned characteristics are inherent properties of the WVB technique. The acceptance criteria of 1% or 2% are no longer applicable. To address this critical issue, this study has developed a reliable system for determining the acceptance standard for VC. By integrating statistical process control with probability statistics theory, the system enables reliable inferences about overall characteristics based on limited sample data through a four-step closed-loop process: data statistical analysis, distribution characteristic modeling, population inspection, and basis value calculation. The 90th percentile value is calculated as 5.26% at a 95% confidence level, which is adopted as the quality acceptance criterion for void content in composite structures.

Authors

Institutions

Publication Details

Journal
Journal of Aircraft
Published
2026-01-19
DOI
https://doi.org/10.2514/1.c038686
Primary Topic
Epoxy Resin Curing Processes
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Determination of Acceptance Criteria for Aircraft Wet-Molding Composite Materials’ Void Content

Yiting Wang, Shaowei Lu, Shuo Zhang, Shuang Zheng et al.
Journal of Aircraft
Epoxy Resin Curing Processes
article

Determination of Acceptance Criteria for Aircraft Wet-Molding Composite Materials’ Void Content

Yiting Wang, Shaowei Lu, Shuo Zhang, Shuang Zheng, Fengtian Yang
article en

Abstract

Currently, in the realm of wet vacuum bagging (WVB) molding composites for general aviation aircraft, there remains a deficiency of systematic methods and specification guidelines for establishing void content (VC) acceptance standards. This study employs an improved acid digestion method to test the VC of RX4E aircraft composite structures. It was discovered that the average VC exceeds 3.0%, which is significantly higher than the traditional empirical standard of 1% or 2%. Through repetitive testing, process traceability verification, and comparison with literature data, it has been confirmed that the aforementioned characteristics are inherent properties of the WVB technique. The acceptance criteria of 1% or 2% are no longer applicable. To address this critical issue, this study has developed a reliable system for determining the acceptance standard for VC. By integrating statistical process control with probability statistics theory, the system enables reliable inferences about overall characteristics based on limited sample data through a four-step closed-loop process: data statistical analysis, distribution characteristic modeling, population inspection, and basis value calculation. The 90th percentile value is calculated as 5.26% at a 95% confidence level, which is adopted as the quality acceptance criterion for void content in composite structures.

Journal of Aircraft
Shenyang Aerospace University (CN)
Openalex Percentile: Top 66%
Epoxy Resin Curing Processes
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.