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Record W3125408679 · doi:10.1108/rpj-07-2020-0174

Advances in additive manufacturing of shape memory polymer composites

2021· article· en· W3125408679 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

VenueRapid Prototyping Journal · 2021
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsShape-memory polymerFiller (materials)3D printingMaterials scienceLaminationCeramicComputer scienceSmart materialMechanical engineeringStereolithographyManufacturing engineeringPolymerProcess engineeringNanotechnologyComposite materialEngineering

Abstract

fetched live from OpenAlex

Purpose A review on additive manufacturing (AM) of shape memory polymer composites (SMPCs) is put forward to highlight the progress made up to date, conduct a critical review and show the limitations and possible improvements in the different research areas within the different AM techniques. The purpose of this study is to identify academic and industrial opportunities. Design/methodology/approach This paper introduces the reader to three-dimensional (3 D) and four-dimensional printing of shape memory polymers (SMPs). Specifically, this review centres on manufacturing technologies based on material extrusion, photopolymerization, powder-based and lamination manufacturing processes. AM of SMPC was classified according to the nature of the filler material: particle dispersed, i.e. carbon, metallic and ceramic and long fibre reinforced materials, i.e. carbon fibres. This paper makes a distinction for multi-material printing with SMPs, as multi-functionality and exciting applications can be proposed through this method. Manufacturing strategies and technologies for SMPC are addressed in this review and opportunities in the research are highlighted. Findings This paper denotes the existing limitations in the current AM technologies and proposes several directions that will contribute to better use and improvements in the production of additive manufactured SMPC. With advances in AM technologies, gradient changes in material properties can open diverse applications of SMPC. Because of multi-material printing, co-manufacturing sensors to 3D printed smart structures can bring this technology a step closer to obtain full control of the shape memory effect and its characteristics. This paper discusses the novel developments in device and functional part design using SMPC, which should be aided with simple first stage design models followed by complex simulations for iterative and optimized design. A change in paradigm for designing complex structures is still to be made from engineers to exploit the full potential of additive manufactured SMPC structures. Originality/value Advances in AM have opened the gateway to the potential design and fabrication of functional parts with SMPs and their composites. There have been many publications and reviews conducted in this area; yet, many mainly focus on SMPs and reserve a small section to SMPC. This paper presents a comprehensive review directed solely on the AM of SMPC while highlighting the research opportunities.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.251
Teacher spread0.240 · 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