ArticlePolymers2023
Investigating the Mechanical Properties of Annealed 3D-Printed PLA-Date Pits Composite.
Article in Polymers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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.
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Who cites it
5 citing papers in PubMed.
- Machine Learning-Based Estimation of Mechanical Properties of 3D-Printed PLA Composites Under Annealing Treatment.Polymers · 2026Article
- Effect of Al₂O₃ nanoparticle reinforcement and annealing on PMMA composite performance for prosthetic feet.Scientific reports · 2026Article
- Article
- Review
- The Development of Poly(lactic acid) (PLA)-Based Blends and Modification Strategies: Methods of Improving Key Properties towards Technical Applications-Review.Materials (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Biomedical applications are crucial in rehabilitation medicine, assisting individuals with disabilities. Nevertheless, materials failure can sometimes result in inconvenience for users. Polylactic Acid (PLA) is a popular 3D-printed material that offers design flexibility. However, it is limited in use because its mechanical properties are inadequate. Thus, this study introduces an artificial intelligence model that utilizes ANFIS to estimate the mechanical properties of PLA composites. The model was developed based on an actual data set collected from experiments. The experimental results were obtained by preparing samples of PLA green composites with different weight fractions of date pits, which were then annealed for varying durations to remove residual stresses resulting from 3D printing. The mechanical characteristics of the produced PLA composite specimens were measured experimentally, while the ANSYS model was established to identify the composites' load-carrying capacity. The results showed that ANFIS models are exceptionally robust and compatible and possess good predictive capabilities for estimating the hardness, strength, and Young's modulus of the 3D-printed PLA composites. The model results and experimental outcomes were nearly identical.
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Registered trials
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