MétaCan
Menu
Back to cohort
Record W4402910956 · doi:10.1016/j.rineng.2024.103023

Compressive and bending properties of 3D-printed wood/PLA composites with Re-entrant honeycomb core

2024· article· en· W4402910956 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueResults in Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComposite materialMaterials scienceCore (optical fiber)HoneycombBendingHoneycomb structure3d printedStructural engineeringEngineering

Abstract

fetched live from OpenAlex

This study investigates the mechanical behavior of sandwich structures comprising a re-entrant honeycomb core structure created using wood/polylactic acid (PLA) filaments via fused deposition modeling (FDM) technology. The sandwich structures were manufactured with different face layer thicknesses (0 mm, 0.8 mm, and 1.6 mm) and various core topologies. Material characterizations included bending tests, compressive tests, and finite element analysis (FEA) to identify stress concentration areas. In-plane and out-of-plane re-entrant honeycomb core structure specimens were tested to examine the bending properties. In addition, flatwise and edgewise compressive tests were performed to investigate the structures' compressive properties. Furthermore, the modulus of elasticity for each specimen was determined using the finite element technique (FEM). The results reveal that increasing the thickness of the face layer significantly enhances the structure's resistance to bending forces. Furthermore, specimens with an in-plane orientation demonstrated better bending strength compared to those with an out-of-plane orientation due to increased material underloading. In flatwise compressive tests, specimens without a face layer exhibited the highest strength, attributed to their greater displacement. In contrast, edgewise compressive tests showed significant buckling behavior of the face sheet, the maximum stress increased proportionally with the thickness of the face layer, reaching its peak at a skin thickness of 1.6 mm. The findings are validated by ANSYS analysis, which closely mirrors the experimental results, providing insight into flexural modulus, modulus of elasticity, and stress concentration. These findings indicate that architected core structures could be efficiently utilized to improve bending/compressive characteristics and failure mechanisms, providing valuable insights into the mechanical response of sandwich structures for different industrial applications. • Thicker face layer enhances 3D printed wood/PLA composite's resistance to bending forces. • In-plane core orientation outperforms out-of-plane in bending strength, crucial for design. • Addition of face layer significantly increases compressive strength in flatwise and edgewise orientations. • Material exhibits anisotropic properties with varying failure modes under different stresses. • Experimental tests and Ansys simulations show good convergence, validating study's accuracy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.205
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it