Classification and Parameter Selection for Damage Characterization in CFRP Composite Materials Using Acoustic Emission and Multivariate Statistics.

David Amoateng-Mensah, Richard Dela Amevorku, Pusan Dhar, Tanzila B Minhaj, Mannur J Sundaresan

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Article in Materials (Basel, Switzerland), 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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5 · Who and what money

Authors and funding

5 authors.

David Amoateng-MensahDepartment of Mechanical Engineering, North Carolina A & T State University, 1601 E. Market Street, Greensboro, NC 27411, USA.ORCID 0009-0002-6177-7414

Richard Dela AmevorkuDepartment of Mechanical Engineering, North Carolina A & T State University, 1601 E. Market Street, Greensboro, NC 27411, USA.ORCID 0009-0008-9093-4777

Pusan DharDepartment of Mechanical Engineering, North Carolina A & T State University, 1601 E. Market Street, Greensboro, NC 27411, USA.

Tanzila B MinhajDepartment of Mechanical Engineering, North Carolina A & T State University, 1601 E. Market Street, Greensboro, NC 27411, USA.

Mannur J SundaresanDepartment of Mechanical Engineering, North Carolina A & T State University, 1601 E. Market Street, Greensboro, NC 27411, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate damage characterization in thermoset Carbon Fiber-Reinforced Polymer (CFRP) composites using Acoustic Emission (AE) requires statistically robust and interpretable models. This study employs multinomial logistic regression with forward selection and Type III analysis to identify the minimal set of AE parameters necessary for classifying damage mechanisms (fiber breaks, delamination, matrix cracks) in quasi-isotropic thermoset CFRP laminates under synchronously recorded load conditions. Starting from 18 conventional time- and frequency-domain descriptors, forward selection yielded seven candidate predictors. However, Type III analysis revealed that only four parameters, Load, Initiation Frequency, Amplitude, and Average Frequency, provide unique, statistically significant contributions (

Indexed as

Acoustic Emission (AE)carbon fiber compositesdamage mechanisms in thermoset CFRPdeep learning (DL)machine learning (ML)multivariate logistic regression

Identifiers

PMID42195731

DOI10.3390/ma19102091

PMCPMC13208745

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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.