Characterization of 3D Printers and X-Ray Computerized Tomography

• URL: http://arxiv.org/abs/2206.00041v1
• Date: Fri, 27 May 2022 11:06:08 GMT
• Title: Characterization of 3D Printers and X-Ray Computerized Tomography
• Authors: Sunita Khod, Akshay Dvivedi, Mayank Goswami
• Abstract summary: Thirty-eight samples are printed using four commercially available 3D printers, namely: (a) Ultimaker 2 Extended+, (b) Delta Wasp, (c) Raise E2, and (d) ProJet MJP. The sample profiles contain uniform and non-uniform distribution of the assorted size of cubes and spheres with a known amount of porosity. It is found that ProJet MJP gives the best quality of printed samples with the least amount of surface roughness and almost near to the actual porosity value.
• Score: 3.333810206561284
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The 3D printing process flow requires several inputs for the best printing quality. These settings may vary from sample to sample, printer to printer, and depend upon users' previous experience. The involved operational parameters for 3D Printing are varied to test the optimality. Thirty-eight samples are printed using four commercially available 3D printers, namely: (a) Ultimaker 2 Extended+, (b) Delta Wasp, (c) Raise E2, and (d) ProJet MJP. The sample profiles contain uniform and non-uniform distribution of the assorted size of cubes and spheres with a known amount of porosity. These samples are scanned using X-Ray Computed Tomography system. Functional Imaging analysis is performed using AI-based segmentation codes to (a) characterize these 3D printers and (b) find Three-dimensional surface roughness of three teeth and one sandstone pebble (from riverbed) with naturally deposited layers is also compared with printed sample values. Teeth has best quality. It is found that ProJet MJP gives the best quality of printed samples with the least amount of surface roughness and almost near to the actual porosity value. As expected, 100% infill density value, best spatial resolution for printing or Layer height, and minimum nozzle speed give the best quality of 3D printing.

Related papers

• 3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion [86.25111098482537]
  We introduce 3DTopia-XL, a scalable native 3D generative model designed to overcome limitations of existing methods. 3DTopia-XL leverages a novel primitive-based 3D representation, PrimX, which encodes detailed shape, albedo, and material field into a compact tensorial format. On top of the novel representation, we propose a generative framework based on Diffusion Transformer (DiT), which comprises 1) Primitive Patch Compression, 2) and Latent Primitive Diffusion. We conduct extensive qualitative and quantitative experiments to demonstrate that 3DTopia-XL significantly outperforms existing methods in generating high-
  arXiv  Detail & Related papers  (2024-09-19T17:59:06Z)
• 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)
• VividDreamer: Towards High-Fidelity and Efficient Text-to-3D Generation [69.68568248073747]
  We propose Pose-dependent Consistency Distillation Sampling (PCDS), a novel yet efficient objective for diffusion-based 3D generation tasks. PCDS builds the pose-dependent consistency function within diffusion trajectories, allowing to approximate true gradients through minimal sampling steps. For efficient generation, we propose a coarse-to-fine optimization strategy, which first utilizes 1-step PCDS to create the basic structure of 3D objects, and then gradually increases PCDS steps to generate fine-grained details.
  arXiv  Detail & Related papers  (2024-06-21T08:21:52Z)
• 3D object quality prediction for Metal Jet Printer with Multimodal thermal encoder [46.85584046139531]
  Various factors during metal printing affect the printed parts' quality. With the large data gathered from HP's MetJet printing process, AI techniques can be used to analyze, learn, and effectively infer the printed part quality metrics.
  arXiv  Detail & Related papers  (2024-04-17T21:57:29Z)
• 3DTopia: Large Text-to-3D Generation Model with Hybrid Diffusion Priors [85.11117452560882]
  We present a two-stage text-to-3D generation system, namely 3DTopia, which generates high-quality general 3D assets within 5 minutes using hybrid diffusion priors. The first stage samples from a 3D diffusion prior directly learned from 3D data. Specifically, it is powered by a text-conditioned tri-plane latent diffusion model, which quickly generates coarse 3D samples for fast prototyping. The second stage utilizes 2D diffusion priors to further refine the texture of coarse 3D models from the first stage. The refinement consists of both latent and pixel space optimization for high-quality texture generation
  arXiv  Detail & Related papers  (2024-03-04T17:26:28Z)
• Consistent3D: Towards Consistent High-Fidelity Text-to-3D Generation with Deterministic Sampling Prior [87.55592645191122]
  Score distillation sampling (SDS) and its variants have greatly boosted the development of text-to-3D generation, but are vulnerable to geometry collapse and poor textures yet. We propose a novel and effective "Consistent3D" method that explores the ODE deterministic sampling prior for text-to-3D generation. Experimental results show the efficacy of our Consistent3D in generating high-fidelity and diverse 3D objects and large-scale scenes.
  arXiv  Detail & Related papers  (2024-01-17T08:32:07Z)
• Instant Multi-View Head Capture through Learnable Registration [62.70443641907766]
  Existing methods for capturing datasets of 3D heads in dense semantic correspondence are slow. We introduce TEMPEH to directly infer 3D heads in dense correspondence from calibrated multi-view images. Predicting one head takes about 0.3 seconds with a median reconstruction error of 0.26 mm, 64% lower than the current state-of-the-art.
  arXiv  Detail & Related papers  (2023-06-12T21:45:18Z)
• 3D-EDM: Early Detection Model for 3D-Printer Faults [0.0]
  It is difficult to use a 3D printer with accurate calibration. Previous studies have suggested that these problems can be detected using sensor data and image data with machine learning methods. Considering actual use in the future, we focus on generating the lightweight early detection model with easily collectable data.
  arXiv  Detail & Related papers  (2022-03-23T02:46:26Z)
• Towards Smart Monitored AM: Open Source in-Situ Layer-wise 3D Printing Image Anomaly Detection Using Histograms of Oriented Gradients and a Physics-Based Rendering Engine [0.0]
  This study presents an open source method for detecting 3D printing anomalies by comparing images of printed layers from a stationary monocular camera with G-code-based reference images of an ideal process generated with Blender, a physics rendering engine. Recognition of visual deviations was accomplished by analyzing the similarity of histograms of oriented gradients (HOG) of local image areas. The implementation of this novel method does not require preliminary data for training, and the greatest efficiency can be achieved with the mass production of parts by either additive or subtractive manufacturing of the same geometric shape.
  arXiv  Detail & Related papers  (2021-11-04T09:27:10Z)

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.