English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Loss of geometric accuracy due to shrinkage is a challenge in material extrusion of biological composites using water-based inks, such as the cellulose-chitin biopolymers used here. The shape of 3D printed objects often departs from the intended design geometry due to evaporative loss of water during curing. Moreover, such materials' viscoelastic characteristics result in complex volumetric changes that are difficult to predict and compensate for. We developed a prediction-correction scheme by 3D printing and scanning cylindrical and conic surfaces, computing the geometric deviations between designed and cured artefacts, and training a neural network such that given the machine path for a 3D print, the model can predict shrinkage deformations and apply adjustments on the generating machine paths to proactively compensate it. In this article, we present the shrinkage characteristics of the material used and the results of applying the predictor-correction scheme. The approach substantially improves geometric accuracy, enabling nearly seamless assembly of separately 3D printed parts. Addressing such a fundamental problem of quality control as geometric accuracy may enable the broader adoption of biopolymers and potentially displace the generalised use of synthetic plastics.

Shrinkage prediction and correction in material extrusion of cellulose-chitin biopolymers using neural network regression