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Cited by 22Architecture and Computational DesignInnovations in Concrete and Construction MaterialsAdditive Manufacturing and 3D Printing Technologies

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A Conceptual Framework for AI-Enhanced 3D Printing in Architectural Component Design

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Bankole, Adeshola Oladunni, Nwokediegwu, Zamathula Sikhakhane, Okiye, Sidney Eronmonsele·2021·Journal of Frontiers in Multidisciplinary Research
DOI 10.54660/.ijfmr.2021.2.2.103-119
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Abstract

This paper proposes a conceptual framework that integrates artificial intelligence (AI) with 3D printing to advance architectural component design, leveraging the rapid expansion of generative design tools in architecture post-2020. The convergence of AI and additive manufacturing enables the creation of complex, optimized, and customizable architectural elements that address contemporary demands for innovation, sustainability, and efficiency in the built environment. The framework outlines key mechanisms by which AI enhances design exploration, fabrication adaptability, and performance optimization in architectural 3D printing. At the core of the framework is the utilization of AI-driven generative design algorithms including machine learning, evolutionary computation, and neural networks that facilitate the automated generation of diverse architectural forms. These algorithms optimize multiple design criteria simultaneously, such as structural integrity, material usage, aesthetic appeal, and environmental responsiveness. By navigating vast design spaces, AI tools empower architects and engineers to develop components tailored to unique project requirements and constraints. The framework also highlights the integration of AI with real-time 3D printing process control, enabling adaptive fabrication through sensor feedback and predictive analytics. This integration supports dynamic adjustments to printing parameters, improving accuracy, reducing material waste, and enhancing build quality. Furthermore, the model emphasizes seamless interoperability between AI design platforms and 3D printing hardware, promoting efficient workflow automation and collaborative design-fabrication cycles. Attention is given to the potential of AI-enhanced 3D printing to produce functionally graded and architecturally complex components that transcend traditional construction limitations. The approach fosters innovation in architectural aesthetics, structural performance, and sustainable building practices, while accelerating project timelines and reducing costs. This review synthesizes current advances in AI and additive manufacturing, providing a roadmap for future research and application in architectural fabrication. The proposed conceptual framework serves as a foundational guide for leveraging intelligent technologies to transform architectural design and construction, enabling smarter, more efficient, and more creative solutions in the evolving landscape of digital fabrication.

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Metadata

Authors
Bankole, A. O., Nwokediegwu, Z. S., & Okiye, S. E.
Venue
Journal of Frontiers in Multidisciplinary Research
Publisher
Anfo Publication House
Published
2021
Volume
2
Issue
2
Pages
103-119
ISSN
  • 3050-9718 (ISSN-L)
  • 3050-9726
DOI
10.54660/.ijfmr.2021.2.2.103-119
OpenAlex
W4412652344
Cited by
19
References
0
Citations by year
2026: 2 · 2024: 8 · 2023: 5 · 2022: 2 · 2021: 2
FWCI
28.04
Type
journal article
Language
en
Open access
Licence
other-oa
Locations
Journal of Frontiers in Multidisciplinary Research (publishedVersion, other-oa)
Topics
  • Physical Sciences › Engineering › Architecture › Architecture and Computational Design
  • Physical Sciences › Engineering › Building and Construction › Innovations in Concrete and Construction Materials
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Additive Manufacturing and 3D Printing Technologies
Keywords
generative design, additive manufacturing, machine learning, evolutionary computation, artificial neural networks, real-time process control, sensor feedback, functionally graded materials, sustainable construction, design optimization, material waste reduction

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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