4D printing and optimization of biocompatible poly lactic acid/poly methyl methacrylate blends for enhanced shape memory and mechanical properties

Doostmohammadi, H, Kashmarizad, K, Baniassadi, M, Bodaghi, M ORCID: https://orcid.org/0000-0002-0707-944X and Baghani, M, 2024. 4D printing and optimization of biocompatible poly lactic acid/poly methyl methacrylate blends for enhanced shape memory and mechanical properties. Journal of the Mechanical Behavior of Biomedical Materials, 160: 106719. ISSN 1751-6161

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Abstract

This study introduces a novel approach to 4D printing of biocompatible Poly lactic acid (PLA)/poly methyl methacrylate (PMMA) blends using Artificial Neural Network (ANN) and Response Surface Methodology (RSM). The goal is to optimize PMMA content, nozzle temperature, raster angle, and printing speed to enhance shape memory properties and mechanical strength. The materials, PLA and PMMA, are melt-blended and 4D printed using a pellet-based 3D printer. Differential Scanning Calorimetry (DSC) and Dynamic Mechanical Thermal Analysis (DMTA) assess the thermal behavior and compatibility of the blends. The ANN model demonstrates superior prediction accuracy and generalization capability compared to the RSM model. Experimental results show a shape recovery ratio of 100% and an ultimate tensile strength of 65.2 MPa, significantly higher than pure PLA. A bio-screw, 4D printed with optimized parameters, demonstrates excellent mechanical properties and shape memory behavior, suitable for biomedical applications such as orthopaedics and dental implants. This research presents an innovative method for 4D printing PLA/PMMA blends, highlighting their potential in creating advanced, high-performance biocompatible materials for medical use.

Item Type:	Journal article
Publication Title:	Journal of the Mechanical Behavior of Biomedical Materials
Creators:	Doostmohammadi, H., Kashmarizad, K., Baniassadi, M., Bodaghi, M. and Baghani, M.
Publisher:	Elsevier
Date:	December 2024
Volume:	160
ISSN:	1751-6161
Identifiers:	Number Type 10.1016/j.jmbbm.2024.106719 DOI 2223292 Other
Rights:	© 2024 the authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	23 Sep 2024 14:17
Last Modified:	23 Sep 2024 15:26
URI:	https://irep.ntu.ac.uk/id/eprint/52282

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