ArticlePolymers2026
Machine Learning-Based Estimation of Mechanical Properties of 3D-Printed PLA Composites Under Annealing Treatment.
Article in Polymers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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Abstract
This study presents a novel, data-driven framework for predicting and optimizing the mechanical performance of 3D-printed polylactic acid (PLA) composites reinforced with date pit (DP) particles under controlled annealing conditions. This work uniquely integrates both effects into a unified machine learning (ML) surrogate modeling approach, enabling simultaneous property prediction and design optimization from a minimal experimental dataset. Three ML algorithms, Gaussian Process Regression (GPR), Random Forest Regression (RFR), and Decision Tree Regression (DTR), were systematically developed to predict Young's modulus (E), ultimate compressive strength (UCS), and Shore D hardness as functions of DP weight fraction (0-10 wt.%) and annealing duration (0-20 h at 100 °C). Among the models, GPR demonstrated superior generalization and uncertainty-aware prediction capability, achieving test R
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