Evidence map›Paper›PMID 39684013›Full record

ArticlePolymers2024

Optimizing Mechanical Properties of Recycled 3D-Printed PLA Parts for Sustainable Packaging Solutions Using Experimental Analysis and Machine Learning.

Maria Tănase, Alexandra Ileana Portoacă, Alin Diniță, Gheorghe Brănoiu, Florin Zamfir, Elena-Emilia Sirbu, Cătălina Călin

Abstract read
In one paragraph

Article in Polymers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Maria TănaseMechanical Engineering Department, Petroleum-Gas University of Ploiești, 100680 Ploiesti, Romania.ORCID 0000-0002-5563-6554
Alexandra Ileana PortoacăMechanical Engineering Department, Petroleum-Gas University of Ploiești, 100680 Ploiesti, Romania.ORCID 0000-0003-1986-8419
Alin DinițăMechanical Engineering Department, Petroleum-Gas University of Ploiești, 100680 Ploiesti, Romania.ORCID 0000-0002-9242-9431
Gheorghe BrănoiuPetroleum Geology and Reservoir Engineering Department, Petroleum-Gas University of Ploiești, 100680 Ploiesti, Romania.ORCID 0000-0002-9562-7564
Florin ZamfirAutomation, Computers and Electronics Department, Petroleum-Gas University of Ploiești, 100680 Ploiesti, Romania.ORCID 0000-0003-2776-9234
Elena-Emilia SirbuChemistry Department, Petroleum-Gas University of Ploiești, 100680 Ploiesti, Romania.
Cătălina CălinChemistry Department, Petroleum-Gas University of Ploiești, 100680 Ploiesti, Romania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Increasing environmental concerns and the need for sustainable materials have driven a focus towards the utilization of recycled polylactic acid (PLA) in additive manufacturing as PLA offers advantages over other thermoplastics, including biodegradability, ease of processing, and a lower environmental impact during production. This study explores the optimization of the mechanical properties of recycled PLA parts through a combination of experimental and machine learning approaches. A series of experiments were conducted to investigate the impact of various processing parameters, such as layer thickness and infill density, as well as annealing conditions, on the mechanical properties of recycled PLA parts. Machine learning algorithms have proven the possibility to predict tensile behavior with an average error of 6.059%. The results demonstrate that specific combinations of processing parameters and post-treatment annealing differently improve the mechanical properties (with 7.31% in ultimate tensile strength (UTS), 0.28% in Young's modulus, and 3.68% in elongation) and crystallinity (with 22.33%) of recycled PLA according to XRD analysis, making it a viable alternative to virgin PLA in various applications such as sustainable packaging solutions, including biodegradable containers, clamshell packaging, and protective inserts. The optimized recycled PLA parts exhibited mechanical properties and crystallinity levels comparable to those of their virgin counterparts, highlighting their potential for reducing environmental impact and saving costs. For both as-built and annealed samples, the optimal settings for achieving high composite desirability involved a 0.2 mm layer thickness, with 75% infill for the as-built samples and 100% infill for the annealed samples. This study provides a comprehensive framework for optimizing recycled PLA in additive manufacturing, contributing to the advancement of sustainable material engineering and the circular economy.

Indexed as

3D printingannealingDSCmachine learningmechanical performancePLAprinting parametersrecyclingXRD

Identifiers

PMID39684013
PMCPMC11644781

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.