Machine Learning for Acoustic Emission Signatures in Composite Laminates

2017 ◽  
Author(s):  
HONG TAT ◽  
JASON WU ◽  
MATTHEW PIKE ◽  
JOSEPH SCHAEFER ◽  
VICTOR PAUL PAUCA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Composite Laminates

Machine Learning Based Predictive Model for AFP-Based Unidirectional Composite Laminates

2020 ◽  
Vol 16 (4) ◽  
pp. 2315-2324 ◽  
Author(s):  
Chathura Wanigasekara ◽  
Ebrahim Oromiehie ◽  
Akshya Swain ◽  
B. Gangadhara Prusty ◽  
Sing Kiong Nguang
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Composite Laminates ◽  
Unidirectional Composite

Damage Characterization in Glass/Epoxy Composite Laminates under Normal and Oblique Planes of Cyclic Indentation Loading with AE Monitoring

2021 ◽  
Vol 79 (1) ◽  
pp. 61-77
Author(s):  
A Jayababu ◽  
V Arumugam ◽  
B Rajesh ◽  
C Suresh Kumar
Keyword(s):  
Compressive Strength ◽  
Acoustic Emission ◽  
Cyclic Loading ◽  
Composite Laminates ◽  
Epoxy Composite ◽  
Damage Resistance ◽  
Damage Initiation ◽  
Residual Compressive Strength ◽  
Cyclic Indentation ◽  
Indentation Damage

This work focuses on the experimental investigation of indentation damage resistance in different stacking sequences of glass/epoxy composite laminates under cyclic loading on normal (0°) and oblique (20°) planes. The stacking sequence, such as unidirectional [0]12, angle ply [±45]6S, and cross ply [0/90]6S, were subjected to cyclic indentation loading and monitoring by acoustic emission testing (AE). The laminates were loaded at the center using a hemispherical steel indenter with a 12.7 mm diameter. The cyclic indentation loading was performed at displacements from 0.5 to 3 mm with an increment of 0.5 mm in each cycle. Subsequently, the residual compressive strength of the post-indented laminates was estimated by testing them under in-plane loading, once again with AE monitoring. Mechanical responses such as peak load, absorbed energy, stiffness, residual dent, and damage area were used for the quantification of the indentation-induced damage. The normalized AE cumulative counts, AE energy, and Felicity ratio were used for monitoring the damage initiation and propagation. Moreover, the discrete wavelet analysis of acoustic emission signals and fast Fourier transform enabled the calculation of the peak frequency content of each damage mechanism. The results showed that the cross-ply laminates had superior indentation damage resistance over angle ply and unidirectional (UD) laminates under normal and oblique planes of cyclic loading. However, the conclusion from the results was that UD laminates showed a better reduction in residual compressive strength than the other laminate configurations.

Machine Learning Acoustic Emission Based Monitoring of Cold Forging for Smart Manufacturing: A Review

2021 ◽  
Vol 2 (3) ◽  
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Product Quality ◽  
High Speed ◽  
High Rate ◽  
Cold Forging ◽  
Smart Manufacturing ◽  
Forming Process ◽  
Cold Forming ◽  
Effective Manner

Cold forging is a high-speed forming technique used to shape metals at near room temperature. and it allows high-rate production of high strength metal-based products in a consistent and cost-effective manner. However, cold forming processes are characterized by complex material deformation dynamics which makes product quality control difficult to achieve. There is no well defined mathematical model that governs the interactions between a cold forming process, material properties, and final product quality. The goal of this work is to provide a review for the state of research in the field of using acoustic emission (AE) technology in monitoring cold forging process. The integration of AE with machine learning (ML) algorithms to monitor the quality is also reviewed and discussed. It is realized that this promising technology didn’t receive the deserving attention for its implementation in cold forging and that more work is needed.

scholarly journals Damage Analysis and Prediction in Glass Fiber Reinforced Polyester Composite Using Acoustic Emission and Machine Learning

2021 ◽  
Author(s):  
S. Gholizadeh
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Damage Detection ◽  
Glass Fiber ◽  
Fatigue Cracking ◽  
Great Success ◽  
Fiber Reinforced ◽  
Structural Problems ◽  
Glass Fiber Reinforced ◽  
Polyester Composite

One of the most pervasive types of structural problems in aircraft industries is fatigue cracking that can potentially occur without anticipation with catastrophic failures and unexpected downtime. Acoustic emission (AE) is a passive structural health monitoring (SHM) technique, since it offers real time damage detection based on stress waves generated by cracking in the structure. Machine learning techniques have presented great success over the past few years with a large number of applications. This study assesses the progression of damage occurring on glass fiber reinforced polyester composite specimens using two approaches of machine learning, namely, Supervised and Unsupervised learning. A methodology for damage detection and characterization of composite is presented. The result shows that machine learning can predict the failure. All predictive models and their performance as well as AE parameters had a direct relationship with the applied stress values, suggesting that these correlation coefficients are reliable means of predicting fatigue life in a composite material.

Sign in / Sign up

Export Citation Format

Share Document