A Review on Geometry and Surface Inspection in 3D Concrete Printing
- URL: http://arxiv.org/abs/2503.07472v1
- Date: Mon, 10 Mar 2025 15:48:17 GMT
- Title: A Review on Geometry and Surface Inspection in 3D Concrete Printing
- Abstract summary: This study explores the various aspects of geometry and surface quality control for 3D concrete printing (3DCP)<n>Four categories of data capture technologies are investigated and their advantages and limitations in the context of AMC are discussed.<n>Various quality control strategies are explored across different stages of the fabrication cycle of the printed object including: (i) During printing, (ii) Layer-wise, (iii) Preassembly, and (iv) Assembly.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Given the substantial growth in the use of additive manufacturing in construction (AMC), it is necessary to ensure the quality of printed specimens which can be much more complex than conventionally manufactured parts. This study explores the various aspects of geometry and surface quality control for 3D concrete printing (3DCP), with a particular emphasis on deposition-based methods, namely extrusion and shotcrete 3D printing (SC3DP). A comprehensive overview of existing quality control (QC) methods and strategies is provided and preceded by an in-depth discussion. Four categories of data capture technologies are investigated and their advantages and limitations in the context of AMC are discussed. Additionally, the effects of environmental conditions and objects' properties on data capture are also analyzed. The study extends to automated data capture planning methods for different sensors. Furthermore, various quality control strategies are explored across different stages of the fabrication cycle of the printed object including: (i) During printing, (ii) Layer-wise, (iii) Preassembly, and (iv) Assembly. In addition to reviewing the methods already applied in AMC, we also address various research gaps and future trends and highlight potential methodologies from adjacent domains that could be transferred to AMC.
Related papers
- 3D Reconstruction Techniques in the Manufacturing Domain: Applications, Research Opportunities and Use Cases [7.501305546365941]
This review examines the evolution and the current state of the art in three-dimensional (3D) reconstruction techniques in manufacturing applications.<n>Non-contact methods, particularly structured light scanning and stereo vision, have shown significant adoption in manufacturing.<n>The integration of deep learning has enhanced reconstruction accuracy and processing speed, particularly in feature extraction and matching.
arXiv Detail & Related papers (2026-04-30T16:11:02Z) - Three-dimensional reconstruction and segmentation of an aggregate stockpile for size and shape analyses [0.0]
This paper presents an innovative 3D imaging approach for potential field evaluation of large-sized aggregates.<n>Engineers can perform inspection by taking videos/images with mobile devices such as smartphone cameras.<n>The approach leverages Structure-from-Motion (SfM) techniques to reconstruct the stockpile surface as 3D spatial data.
arXiv Detail & Related papers (2026-03-04T02:25:03Z) - Deep Filament Extraction for 3D Concrete Printing [0.0]
This paper presents an automated procedure for quality control (QC) of filaments in extrusion-based and SC3DP printing methods.<n>The paper also describes a workflow that is independent of the sensor used for data acquisition, such as a camera, a structured light system (SLS) or a terrestrial laser scanner (TLS)
arXiv Detail & Related papers (2025-11-26T14:17:33Z) - Registration-Free Monitoring of Unstructured Point Cloud Data via Intrinsic Geometrical Properties [0.4651750987298772]
We present a novel registration-free approach for monitoring PCD of complex shapes.<n>Our proposal consists of two alternative feature learning methods and a common monitoring scheme.
arXiv Detail & Related papers (2025-11-06T23:13:03Z) - Point Cloud Compression and Objective Quality Assessment: A Survey [22.27629022031786]
3D point cloud data is driven by applications in autonomous driving, robotics, and immersive environments.<n>Unlike traditional 2D media, point clouds present unique challenges due to their irregular structure, high data volume, and complex attributes.
arXiv Detail & Related papers (2025-06-28T14:34:24Z) - AI-powered Contextual 3D Environment Generation: A Systematic Review [49.1574468325115]
This study performs a systematic review of existing generative AI techniques for 3D scene generation.<n>By examining state-of-the-art approaches, it presents key challenges such as scene authenticity and the influence of textual inputs.
arXiv Detail & Related papers (2025-06-05T15:56:28Z) - Embodied Intelligence for 3D Understanding: A Survey on 3D Scene Question Answering [28.717312557697376]
3D Scene Question Answering represents an interdisciplinary task that integrates 3D visual perception and natural language processing.<n>Recent advances in large multimodal modelling have driven the creation of diverse datasets.<n>We provide the first comprehensive and systematic review of 3D SQA.
arXiv Detail & Related papers (2025-02-01T07:01:33Z) - No-Reference Point Cloud Quality Assessment via Graph Convolutional Network [89.12589881881082]
Three-dimensional (3D) point cloud, as an emerging visual media format, is increasingly favored by consumers.
Point clouds inevitably suffer from quality degradation and information loss through multimedia communication systems.
We propose a novel no-reference PCQA method by using a graph convolutional network (GCN) to characterize the mutual dependencies of multi-view 2D projected image contents.
arXiv Detail & Related papers (2024-11-12T11:39:05Z) - LLM-3D Print: Large Language Models To Monitor and Control 3D Printing [6.349503549199403]
Industry 4.0 has revolutionized manufacturing by driving digitalization and shifting the paradigm toward additive manufacturing (AM)
FDM, a key AM technology, enables the creation of highly customized, cost-effective products with minimal material waste through layer-by-layer extrusion.
We present a process monitoring and control framework that leverages pre-trained Large Language Models (LLMs) alongside 3D printers to detect and address printing defects.
arXiv Detail & Related papers (2024-08-26T14:38:19Z) - A Comprehensive Survey of 3D Dense Captioning: Localizing and Describing
Objects in 3D Scenes [80.20670062509723]
3D dense captioning is an emerging vision-language bridging task that aims to generate detailed descriptions for 3D scenes.
It presents significant potential and challenges due to its closer representation of the real world compared to 2D visual captioning.
Despite the popularity and success of existing methods, there is a lack of comprehensive surveys summarizing the advancements in this field.
arXiv Detail & Related papers (2024-03-12T10:04:08Z) - A Quantitative Evaluation of Score Distillation Sampling Based
Text-to-3D [54.78611187426158]
We propose more objective quantitative evaluation metrics, which we cross-validate via human ratings, and show analysis of the failure cases of the SDS technique.
We demonstrate the effectiveness of this analysis by designing a novel computationally efficient baseline model.
arXiv Detail & Related papers (2024-02-29T00:54:09Z) - Occlusion-Aware 2D and 3D Centerline Detection for Urban Driving via
Automatic Label Generation [4.921246328739616]
This research work seeks to explore and identify strategies that can determine road topology information in 2D and 3D under highly dynamic urban driving scenarios.
To facilitate this exploration, we introduce a substantial dataset comprising nearly one million automatically labeled data frames.
arXiv Detail & Related papers (2023-11-03T17:20:34Z) - PointHPS: Cascaded 3D Human Pose and Shape Estimation from Point Clouds [99.60575439926963]
We propose a principled framework, PointHPS, for accurate 3D HPS from point clouds captured in real-world settings.
PointHPS iteratively refines point features through a cascaded architecture.
Extensive experiments demonstrate that PointHPS, with its powerful point feature extraction and processing scheme, outperforms State-of-the-Art methods.
arXiv Detail & Related papers (2023-08-28T11:10:14Z) - AI-Generated Content (AIGC) for Various Data Modalities: A Survey [15.826383156183033]
AIGC methods aim to produce text, images, videos, 3D assets, and other media using AI algorithms.<n>This paper provides a comprehensive review of AIGC methods across different data modalities, including both single-modality and cross-modality methods.
arXiv Detail & Related papers (2023-08-27T18:38:57Z) - Geometric Deep Learning for Structure-Based Drug Design: A Survey [83.87489798671155]
Structure-based drug design (SBDD) leverages the three-dimensional geometry of proteins to identify potential drug candidates.
Recent advancements in geometric deep learning, which effectively integrate and process 3D geometric data, have significantly propelled the field forward.
arXiv Detail & Related papers (2023-06-20T14:21:58Z) - An adaptive human-in-the-loop approach to emission detection of Additive
Manufacturing processes and active learning with computer vision [76.72662577101988]
In-situ monitoring and process control in Additive Manufacturing (AM) allows the collection of large amounts of emission data.
This data can be used as input into 3D and 2D representations of the 3D-printed parts.
The aim of this paper is to propose an adaptive human-in-the-loop approach using Machine Learning techniques.
arXiv Detail & Related papers (2022-12-12T15:11:18Z) - FPIC: A Novel Semantic Dataset for Optical PCB Assurance [7.027992770055891]
We review state-of-the-art automated optical inspection (AOI) techniques and observe the strong, rapid trend toward machine learning (ML) solutions.
These require significant amounts of labeled ground truth data, which is lacking in the publicly available PCB data space.
We propose the FICS Image Collection (FPIC) dataset to address this bottleneck in available large-volume, diverse, semantic annotations.
arXiv Detail & Related papers (2022-02-17T02:29:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.