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Record W2953571830 · doi:10.22260/isarc2019/0174

3D Printing Architectural Freeform Elements: Challenges and Opportunities in Manufacturing for Industry 4.0

2019· article· en· W2953571830 on OpenAlexaboutno aff
Marjo Niemelä, Anqi Shi, Sara Shirowzhan, Samad M. E. Sepasgozar, Chang Liu

Bibliographic record

VenueProceedings of the ... ISARC · 2019
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsnot available
Fundersnot available
Keywords3D printingDigitizationManufacturing engineeringResource (disambiguation)ManufacturingVariety (cybernetics)Computer scienceEmerging technologiesEngineeringArchitectural engineeringBusinessTelecommunicationsMechanical engineering

Abstract

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3D Printing Architectural Freeform Elements: Challenges and Opportunities in Manufacturing for Industry 4.0 Marjo Niemelä, Anqi Shi, Sara Shirowzhan, Samad Sepasgozar and Chang Liu Pages 1298-1304 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Three-dimensional (3D) printing, as one of the additive manufacturing (AM) technologies, is transforming the design and manufacture of products and components across a variety of disciplines, however, architectural design and the construction industry have only recently begun to adopt these technologies for construction purposes. AM is considered one of the core technological advances in the paradigm shift to Industry 4.0 (the fourth industrial revolution). This term used to describe digitization and automation of the manufacturing environment and is widely recognized as a disruptive technology that could transform architectural design and the construction industry. The potential advantages of 3D printing in the construction sector are significant. They include not only improved environmental and financial resource efficiencies, but also, the capacity to produce complex customized designs for aesthetic and structural applications. As the cost of building houses continues to rise, it is crucial to find innovative ways to build houses efficiently and cost effectively. The earliest records of 3D printing date back to the 1980’s and many industries—from manufacturing to medicine—were early adopters of the technologies resulting in many significant technological advances in those sectors from organ printing to aircraft fabrication. Currently available 3D printing technologies can be adopted for building construction and this paper discusses the applications, advantages, limitations and future directions of 3D printing as a viable solution for affordable house construction with a focus on printing architectural freeform elements. 3D printing offers a new and innovative method of house construction. For this study, an analytical, as well as a numerical model were specifically designed for 3D printing. Previous studies conducted found that the construction of a 3D printed truss-like roof in a cement mixture with high-density polyethylene (HDPE), spanning the entire structure, was structurally feasible in the absence of steel reinforcements. These results led us to investigate the feasibility of 3D printing an entire house without the use of reinforcements. Investigations were also performed on comparing flat-roof and arch-roof structures and found that whilst maximum tensile stresses within flat-roof would cause the concrete truss structure to fail, the HDPE cement mix in an arch-roof structure had reduced the maximum tensile stresses to an acceptable range to withstand loadings. At the time of writing this paper, several 3D printing techniques could be adopted for the purposes of 3D printing an entire house, and the team believes that future adaptations of existing technologies and printing materials could eliminate the current limitations of 3D printing and become common practice in house construction. Keywords: 3D printing; design; architectural freeform elements; construction; Fusion 360 Software; G-code; human-interface; Industry 4.0 DOI: https://doi.org/10.22260/ISARC2019/0174 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.003
Scholarly communication0.0080.007
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0490.023

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 head0.032
GPT teacher head0.220
Teacher spread0.188 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations28
Published2019
Admission routes1
Has abstractyes

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Same venueProceedings of the ... ISARCSame topicAdditive Manufacturing and 3D Printing TechnologiesFrench-language works237,207