Classification and identification of damage mechanisms in polyethylene self‐reinforced laminates by acoustic emission technique

Abstract

The objective of present study is to classify and identify damage mechanisms in polyethylene(PE) self‐reinforced composites by acoustic emission (AE) technique. Model specimens including LDPE resin, [90°]laminate, single fiber composite, fiber bundle composite, and [±45°] laminates are fabricated to obtain expected damage mechanisms during tensile testing. First, mechanical behaviors and corresponding AE response of model specimens are studied to validate damage mechanisms in UHMWPE/LDPE laminates. Second, relationship among AE descriptors is investigated by hierarchical cluster analysis, and AE signals are classified by k‐means cluster analysis. Correlations between damage mechanisms and AE are established in terms of amplitude, duration, and peak frequency of AE signals. Finally, an artificial neural network is created and trained by various optimal algorithms to identify damage mechanisms. The results reveal that typical damage mechanisms in PE self‐reinforced composite can be classified in terms of the similarity between AE signals and identified by trained artificial neural network. POLYM. COMPOS., 2011. © 2011 Society of Plastics Engineers

Defect controlled transverse compressive strength of polyethylene fiber laminates

“…As a compromise between capturing new acoustic events and minimizing the recording of a previous event's reverberation as a new event, the event duration and lockout time parameters were set to 1200 and 600 ls respectively. Since tensile UHMWPE fiber failure has been shown to be a high amplitude (>80 dB) event compared to other failure mechanisms (matrix deformation, fiber-matrix debonding and delamination) (Wang et al, 2011;Zhuang and Yan, 2006), the event threshold was set at 78 dB to avoid non-fiber failure related events.…”

Localisation and identification of fatigue matrix cracking and delamination in a carbon fibre panel by acoustic emission

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Development of an artificial neural network processing technique for the analysis of damage evolution in pultruded composites with acoustic emission

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