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Towards the development of a smart fused filament fabrication system using multi-sensor data fusion for in-process monitoring

Towards the development of a smart fused filament fabrication system using multi-sensor data... This paper aims to illustrate the integration of multiple heterogeneous sensors into a fused filament fabrication (FFF) system and the implementation of multi-sensor data fusion technologies to support the development of a “smart” machine capable of monitoring the manufacturing process and part quality as it is being built.Design/methodology/approachStarting from off-the-shelf FFF components, the paper discusses the issues related to how the machine architecture and the FFF process itself must be redesigned to accommodate heterogeneous sensors and how data from such sensors can be integrated. The usefulness of the approach is discussed through illustration of detectable, example defects.FindingsThrough aggregation of heterogeneous in-process data, a smart FFF system developed upon the architectural choices discussed in this work has the potential to recognise a number of process-related issues leading to defective parts.Research limitations/implicationsAlthough the implementation is specific to a type of FFF hardware and type of processed material, the conclusions are of general validity for material extrusion processes of polymers.Practical implicationsEffective in-process sensing enables timely detection of process or part quality issues, thus allowing for early process termination or application of corrective actions, leading to significant savings for high value-added parts.Originality/valueWhile most current literature on FFF process monitoring has focused on monitoring selected process variables, in this work a wider perspective is gained by aggregation of heterogeneous sensors, with particular focus on achieving co-localisation in space and time of the sensor data acquired within the same fabrication process. This allows for the detection of issues that no sensor alone could reliably detect. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Rapid Prototyping Journal Emerald Publishing

Towards the development of a smart fused filament fabrication system using multi-sensor data fusion for in-process monitoring

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1355-2546
DOI
10.1108/rpj-06-2019-0167
Publisher site
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Abstract

This paper aims to illustrate the integration of multiple heterogeneous sensors into a fused filament fabrication (FFF) system and the implementation of multi-sensor data fusion technologies to support the development of a “smart” machine capable of monitoring the manufacturing process and part quality as it is being built.Design/methodology/approachStarting from off-the-shelf FFF components, the paper discusses the issues related to how the machine architecture and the FFF process itself must be redesigned to accommodate heterogeneous sensors and how data from such sensors can be integrated. The usefulness of the approach is discussed through illustration of detectable, example defects.FindingsThrough aggregation of heterogeneous in-process data, a smart FFF system developed upon the architectural choices discussed in this work has the potential to recognise a number of process-related issues leading to defective parts.Research limitations/implicationsAlthough the implementation is specific to a type of FFF hardware and type of processed material, the conclusions are of general validity for material extrusion processes of polymers.Practical implicationsEffective in-process sensing enables timely detection of process or part quality issues, thus allowing for early process termination or application of corrective actions, leading to significant savings for high value-added parts.Originality/valueWhile most current literature on FFF process monitoring has focused on monitoring selected process variables, in this work a wider perspective is gained by aggregation of heterogeneous sensors, with particular focus on achieving co-localisation in space and time of the sensor data acquired within the same fabrication process. This allows for the detection of issues that no sensor alone could reliably detect.

Journal

Rapid Prototyping Journal – Emerald Publishing

Published: Jul 23, 2020

Keywords: Additive manufacturing; Multi-sensor data fusion; Fused filament fabrication; In-process monitoring

References

  • In-situ monitoring of polymer flow temperature and pressure in extrusion based additive manufacturing
    Anderegg, D.A.; Bryant, H.A.; Ruffin, D.C.; Skrip, S.M.; Fallon, J.J.; Gilmer, E.L.; Bortner, M.J.
  • Highly reproducible printable graphite strain gauges for flexible devices
    Bessonov, A.; Kirikova, M.; Haque, S.; Gartseev, I.; Bailey, M.J.A.
  • Aluminum MK8 extruder for CR-10
  • High resolution 3-channel housed encoder module kits with snap-on cover
  • Vision-based online process control in manufacturing applications
    Cheng, Y.; Jafari, M.A.
  • On-line temperature monitoring in selective laser sintering/melting
    Chivel, Y.; Smurov, I.
  • Online quality control of selective laser melting
    Craeghs, T.; Clijsters, S.; Yasa, E.; Kruth, J.P.
  • Detection of process failures in layerwise laser melting with optical process monitoring
    Craeghs, T.; Clijsters, S.; Kruth, J.P.; Bechmann, F.; Ebert, M.C.
  • Embedded sensing: integrating sensors in 3-D printed structures
    Dijkshoorn, A.; Werkman, P.; Welleweerd, M.; Wolterink, G.; Eijking, B.; Delamare, J.; Sanders, R.; Krijnen, G.J.M.
  • Real-time process monitoring and temperature mapping of a 3D polymer printing process
    Dinwiddie, R.B.; Love, L.J.; Rowe, J.C.
  • Electrically conductive nanocomposites for fused deposition modelling
    Dorigato, A.; Moretti, V.; Dul, S.; Unterberger, S.H.; Pegoretti, A.
  • V6 all metal hot end
  • Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing
    Everton, S.K.; Hirsch, M.; Stavroulakis, P.I.; Leach, R.K.; Clare, A.T.
  • Fabbrix materials
  • Low temperature direct 3D printed bioceramics and biocomposites as drug release matrices
    Gbureck, U.; Vorndran, E.; Müller, F.A.; Barralet, J.E.
  • Closed loop control of slippage during filament transport in molten material extrusion
    Greeff, G.P.; Schilling, M.
  • Profile monitoring based quality control method for fused deposition modeling process
    He, K.; Zhang, Q.; Hong, Y.
  • Rapid 3D printing of anatomically accurate and mechanically heterogeneous aortic valve hydrogel scaffolds
    Hockaday, L.A.
  • In situ real time defect detection of 3D printed parts
    Holzmond, O.; Li, X.
  • Fiber bragg grating based investigation of residual strains in ABS parts fabricated by fused deposition modeling process
    Kantaros, A.; Karalekas, D.
  • In-situ monitoring of strain and temperature distributions during fused deposition modeling process
    Kousiatza, C.; Karalekas, D.
  • Thermography for monitoring the selective laser melting process
    Krauss, H.; Eschey, C.; Zaeh, M.F.
  • Image analysis-based closed loop quality control for additive manufacturing with fused filament fabrication
    Liu, C.; Law, A.C.C.; Roberson, D.; Kong, Z.; , J.
  • An improved fault diagnosis approach for FDM process with acoustic emission
    Liu, J.; Hu, Y.; Wu, B.; Wang, Y.
  • Logitech webcam C170
  • Design of an optical system for the in situ process monitoring of selective laser melting (SLM)
    Lott, P.; Schleifenbaum, H.; Meiners, W.; Wissenbach, K.; Hinke, C.; Bültmann, J.
  • Marlin firmware
  • Dynamic measurements using FDM 3D-printed embedded strain sensors
    Maurizi, M.; Slavič, J.; Cianetti, F.; Jerman, M.; Valentinčič, J.; Lebar, A.; Boltežar, M.
  • Megatronics 3.0
  • Filament temperature dynamics in fused deposition modelling and outlook for control
    Pollard, D.; Ward, C.; Herrmann, G.; Etches, J.
  • Online Real-Time quality monitoring in additive manufacturing processes using heterogeneous sensors
    Rao, P.K.; Liu, J.; , P.; Roberson, D.; Kong, Z.; , J.; Williams, C.
  • Software repetier host
  • Wade geared extruder
  • 3Drag 3D printer kit, open store electronics
  • Online monitoring of additive manufacturing processes using ultrasound
    Rieder, H.; Dillhofer, A.; Spies, M.; Bamberg, J.; Hess, T.
  • Adaptation of pharmaceutical excipients to FDM 3D printing for the fabrication of patient-tailored immediate release tablets
    Sadia, M.; Sośnicka, A.; Arafat, B.; Isreb, A.; Ahmed, W.; Kelarakis, A.; Alhnan, M.A.
  • Evaluating the deposition quality of parts produced by an open-source 3D printer
    Santana, L.; Ahrens, C.H.; Da Costa Sabino Netto, A.; Bonin, C.
  • Multi-view online vision detection based on robot fused deposit modeling 3D printing technology
    Shen, H.; Sun, W.; Fu, J.
  • Deep goove ball bearings
  • Nozzle condition monitoring in 3D printing
    Tlegenov, Y.; Hong, G.S.; Lu, W.F.
  • A dynamic model for current-based nozzle condition monitoring in fused deposition modelling
    Tlegenov, Y.; Lu, W.F.; Hong, G.S.
  • Ultimaker 3
  • Ultimaker cura, ultimaker.com
    Ultimaker, B.V.
  • Low-cost closed-loop control of a 3D printer gantry
    Weiss, B.; Storti, D.; Ganter, M.
  • In situ monitoring of FDM machine condition via acoustic emission
    Wu, H.; Wang, Y.; Yu, Z.