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A Review on Application of Acoustic Emission Testing During Additive Manufacturing

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Abstract

Additive manufacturing transforms the industry by integrating innovative and intelligent technology, resulting in less material waste and faster prototyping. However, qualitative ambiguities are a significant barrier to digital fabrication methods to manufacture essential parts that require great precision and accuracy. However, qualitative ambiguities are a substantial barrier to digital fabrication methods to manufacture crucial parts that demand higher precision and accuracy. As a result, process monitoring techniques during production are becoming increasingly important. Acoustic emission testing is a prominent nondestructive testing approach that has demonstrated its capacity to detect and locate minute and internal developing cracks, allowing for real-time damage monitoring. This study briefly discussed different additive manufacturing processes, their influential parameters, and monitoring techniques, with particular emphasis on acoustic emission techniques. This study provides extensive recommendations for process monitoring of fused deposition modeling, powder bed fusion and directed energy deposition methods using acoustic emission testing. The different approaches used for handling the acoustic emission data and the effect of defects on acoustic emission signal parameters are also reviewed in this study.

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Data Availability

Data will be made available on request.

Abbreviations

CFSFDP:

Clustering by fast search and finding of density peaks

CNN:

Convolutional neural network

DBN:

Deep belief network

DLP:

Digital light processing

EEMD:

Ensemble empirical mode decomposition

FFT:

Fast Fourier transform

FDA:

Fisher discriminant analysis

GMM:

Gaussian mixture model

HHT:

Hilbert-Huang transform

HSMM:

Hidden semi-Markova model

LDA:

Linear discriminant analysis

LOM:

Laminated object manufacturing

LR:

Logistic regression

LSTM:

Long short-term memory

MLP:

Multilayer perceptron

PCA:

Principal component analysis

PLA:

Polylactic acid

PP:

Polypropylene

RBF:

Radial basis function

RF:

Random forest

SOM:

Self-organizing maps

SCNN:

Spectral convolutional neural network

SLA:

Stereolithography

SLM:

Selective laser melting

ST-FFT:

Short-time fast Fourier transform

STFT:

Short-time Fourier transform

SVM:

Support vector machine

RL:

Reinforcement learning

ResNet:

Residual networks

t-SNE:

T-Distributed Stochastic Neighbour Embedding

UAM:

Ultrasonic additive manufacturing

VAE:

Variational autoencoders

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Funding

This project is funded by the In-house R&D activities of the Council of Scientific & Industrial Research -Structural Engineering Research Centre, Chennai, India, under MLP-213.

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  1. Advanced Material Laboratory, Council of Scientific & Industrial Research-Structural Engineering Research Centre, Taramani, Chennai, Tamil Nadu, 600 113, India

    Prabhat Ranjan Prem, Ambily Parukutty Sanker, Shilpa Sebastian & Senthil Kumar Kaliyavaradhan

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PRP: conceptualization, methodology, writing, review and editing. PA: review and editing. SS: writing—review and editing, data collection. SKK: review and editing.

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Prem, P.R., Sanker, A.P., Sebastian, S. et al. A Review on Application of Acoustic Emission Testing During Additive Manufacturing. J Nondestruct Eval 42, 96 (2023). https://doi.org/10.1007/s10921-023-01005-0

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