Evidence map›Paper›PMID 33291707›Full record

ArticleFoods (Basel, Switzerland)2020

Designing Mechanical Properties of 3D Printed Cookies through Computer Aided Engineering.

Agnese Piovesan, Valérie Vancauwenberghe, Wondwosen Aregawi, Mulugeta A Delele, Evi Bongaers, Mathijs de Schipper, Kjeld van Bommel, Martijn Noort, Pieter Verboven, Bart Nicolai

Abstract read
In one paragraph

Article in Foods (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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  2. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Agnese PiovesanKU Leuven, Division BIOSYST-MeBioS, University of Leuven, 3000 Leuven, Belgium.ORCID 0000-0002-4962-352X
Valérie VancauwenbergheKU Leuven, Division BIOSYST-MeBioS, University of Leuven, 3000 Leuven, Belgium.
Wondwosen AregawiKU Leuven, Division BIOSYST-MeBioS, University of Leuven, 3000 Leuven, Belgium.
Mulugeta A DeleleKU Leuven, Division BIOSYST-MeBioS, University of Leuven, 3000 Leuven, Belgium.
Evi BongaersBruker microCT, 2550 Kontich, Belgium.
Mathijs de SchipperDepartment Equipment for Additive Manufacturing, TNO, NL-5656 Eindhoven, The Netherlands.
Kjeld van BommelDepartment Equipment for Additive Manufacturing, TNO, NL-5656 Eindhoven, The Netherlands.
Martijn NoortWageningen Food & Biobased Research, Wageningen University and Research, 6708WG Wageningen, The Netherlands.ORCID 0000-0003-3655-4796
Pieter VerbovenKU Leuven, Division BIOSYST-MeBioS, University of Leuven, 3000 Leuven, Belgium.
Bart NicolaiKU Leuven, Division BIOSYST-MeBioS, University of Leuven, 3000 Leuven, Belgium.

Funding

CIBUS-Food SUSFOOD project FP7-ERA-NetFonds Wetenschappelijk Onderzoek 1S44318N
6 · The paper itself

Abstract

Additive manufacturing or 3D printing can be applied in the food sector to create food products with personalized properties such as shape, texture, and composition. In this article, we introduce a computer aided engineering (CAE) methodology to design 3D printed food products with tunable mechanical properties. The focus was on the Young modulus as a proxy of texture. Finite element modelling was used to establish the relationship between the Young modulus of 3D printed cookies with a honeycomb structure and their structure parameters. Wall thickness, cell size, and overall porosity were found to influence the Young modulus of the cookies and were, therefore, identified as tunable design parameters. Next, in experimental tests, it was observed that geometry deformations arose during and after 3D printing, affecting cookie structure and texture. The 3D printed cookie porosity was found to be lower than the designed one, strongly influencing the Young modulus. After identifying the changes in porosity through X-ray micro-computed tomography, a good match was observed between computational and experimental Young's modulus values. These results showed that changes in the geometry have to be quantified and considered to obtain a reliable prediction of the Young modulus of the 3D printed cookies.

Indexed as

3D food printingadditive manufacturingcomputed aided engineeringfinite element modellingstructuretextureX-ray computed tomography

Identifiers

PMID33291707
PMCPMC7761976

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.