About: Detecting first layer bond quality during FDM 3D printing using a discrete wavelet energy approach

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About: Detecting first layer bond quality during FDM 3D printing using a discrete wavelet energy approach Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Detecting first layer bond quality during FDM 3D printing using a discrete wavelet energy approach
Creator	Bhavsar, Pavan Madhavan, Vis Moscoso-Kingsley, Wilfredo Sharma, Bhisham
topic	covid:e45eee092fbf705e752d39013f1a40d217cf7a6a#this
source	Elsevier
abstract	Abstract Fused Deposition Modeling (FDM) is a highly versatile additive manufacturing method for 3D printing thermoplastic-based components at small as well as larger production scales. By combining the filament with fibers from other materials including wood, metal, glass, and carbon, the method can easily be adapted to print complex parts using a variety of materials. However, despite its popularity, online print quality and machine monitoring continue to remain a challenge. Here, we present the preliminary results from our efforts on using cheap off-the-shelf sensors in combination with discrete wavelet transform analysis to identify the differences in the vibroacoustic signals measured near the print area during successful and failed first layer filament deposition on the build plate. A failure in creating a strong first layer bond between the extruded filament and the build plate always results in a print failure and is one of most common print issues occurring in FDM printing. By controlling the extruder and build plate temperatures, we control the filament – build plate bond strength while measuring the generated vibroacoustic signals using a PVDF piezo sensor. The measured signals are analyzed using a discrete wavelet transform to partition the signal energy into different energy levels. For the cases studied, we find that the relevant noticeable differences can be observed in specific energy levels during good and bad bond formation. Reconstructing the signal using these energy contributions provides a time domain representation of signal differences under different conditions. The obtained results demonstrate that a cheap and easy to implement method can be developed using PVDF sensors in combination with a wavelet-based signal analysis approach.
has issue date	2020-12-31(xsd:dateTime)
bibo:doi	10.1016/j.promfg.2020.05.104
has license	els-covid
sha1sum (hex)	e45eee092fbf705e752d39013f1a40d217cf7a6a
schema:url	https://doi.org/10.1016/j.promfg.2020.05.104
resource representing a document's title	Detecting first layer bond quality during FDM 3D printing using a discrete wavelet energy approach
schema:publication	Procedia Manufacturing
resource representing a document's body	covid:e45eee092fbf705e752d39013f1a40d217cf7a6a#body_text
is http://vocab.deri.ie/void#inDataset of	https://covidontheweb.inria.fr:4443/about/id/http/ns.inria.fr/covid19/e45eee092fbf705e752d39013f1a40d217cf7a6a
is schema:about of	named entity 'plate' named entity 'controlling' named entity 'formation' named entity 'extruded' named entity 'common' named entity 'signals' named entity 'adapted' named entity 'print' named entity 'ScienceDirect' named entity 'THESE' named entity 'COMPLEX' named entity 'SMALL' named entity 'METAL' named entity 'MEASURING' named entity 'WOOD' named entity 'ONE OF' named entity 'SENSORS' named entity 'MATERIALS' named entity 'observed' named entity 'materials' named entity 'time domain' named entity 'plate' named entity 'energy levels' named entity 'identify' named entity 'signals' named entity 'popularity' named entity 'remain' named entity 'combination' named entity 'FDM' named entity 'scales' named entity 'measured' named entity 'layer' named entity 'energy' named entity 'bond' named entity 'thermoplastic' named entity 'carbon' named entity 'additive manufacturing' named entity 'time domain' named entity 'FDM' named entity 'discrete wavelet transform' named entity '3D printing' named entity 'Fused Deposition Modeling' named entity 'time domain' named entity 'Residual Network' named entity 'DFT' named entity 'DWT' named entity 'FDM' named entity '3D print' named entity 'spectral content' named entity 'spectral content' named entity 'extrusion' named entity 'thermoplastic filament' named entity 'melting point' named entity 'signal noise' named entity '3D printing' named entity 'DWT' named entity 'melting point' named entity 'Fused Deposition Modeling' named entity 'Dirichlet process' named entity 'extrusion' named entity 'additive techniques' named entity 'DWT' named entity 'extruder' named entity 'Discrete Fourier Transform' named entity 'Fused Deposition Modeling' named entity 'FDM' named entity 'extrusion' named entity 'composites' named entity 'SLS' named entity 'electrical noise' named entity 'thermoplastic filament' named entity 'Stereolithography' named entity 'FDM' named entity 'signal noise' named entity 'ceramics' named entity 'temperature sensor' named entity 'wavelet' named entity 'FDM' named entity 'sensor'

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