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

Defects such as warping, which are introduced during additive manufacturing, severely compromise the quality of parts and could even damage the 3D printer. This paper proposes automated feature extraction and hyperparameter optimization in a closed-loop in-process system to monitor warping in fused filament fabrication (FFF). The feature extraction is based on G-code analysis, and map matching between the build platform and the captured image. This allows the warping detection algorithm to be applied to different camera angles, part locations, and corner geometries. Bayesian optimization is adopted to determine the best hyperparameters for the classification model. This model, based on a convolutional neural network (CNN), is executed in a Raspberry Pi pre-configured with OctoPrint, with plugins coordinating and controlling the camera, 3D printer, and microcomputer. Based on 16 tests carried out, the warping monitoring system was determined to be 99.2% accurate.

Development of automated feature extraction and convolutional neural network optimization for real-time warping monitoring in 3D printing