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

• URL: http://arxiv.org/abs/2111.02703v1
• Date: Thu, 4 Nov 2021 09:27:10 GMT
• Title: 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
• Authors: Aliaksei Petsiuk, Joshua M. Pearce
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 developed technique requires preliminary modeling of the working environment to achieve the best match for orientation, color rendering, lighting, and other parameters of the printed part. The output of the algorithm is a level of mismatch between printed and synthetic reference layers. Twelve similarity and distance measures were implemented and compared for their effectiveness at detecting 3D printing errors on six different representative failure types and their control error-free print images. The results show that although Kendall tau, Jaccard, and Sorensen similarities are the most sensitive, Pearson r, Spearman rho, cosine, and Dice similarities produce the more reliable results. This open source method allows the program to notice critical errors in the early stages of their occurrence and either pause manufacturing processes for further investigation by an operator or in the future AI-controlled automatic error correction. 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. It can be concluded this open source method is a promising means of enabling smart distributed recycling for additive manufacturing using complex feedstocks as well as other challenging manufacturing environments.

Related papers

• Geometry-Aware Score Distillation via 3D Consistent Noising and Gradient Consistency Modeling [31.945761751215134]
  We introduce 3D consistent noising, geometry-based gradient warping and novel gradient consistency loss. We successfully address the geometric inconsistency problems in text-to-3D generation task with minimal cost and being compatible with existing score distillation-based models.
  arXiv  Detail & Related papers  (2024-06-24T14:58:17Z)
• Generalization of pixel-wise phase estimation by CNN and improvement of phase-unwrapping by MRF optimization for one-shot 3D scan [0.621405559652172]
  Active stereo technique using single pattern projection, a.k.a. one-shot 3D scan, have drawn a wide attention from industry, medical purposes, etc. One severe drawback of one-shot 3D scan is sparse reconstruction. We propose a pixel-wise technique for one-shot scan, which is applicable to any types of static pattern if the pattern is regular and periodic.
  arXiv  Detail & Related papers  (2023-09-26T10:45:04Z)
• Semi-Siamese Network for Robust Change Detection Across Different Domains with Applications to 3D Printing [17.176767333354636]
  We present a novel Semi-Siamese deep learning model for defect detection in 3D printing processes. Our model is designed to enable comparison of heterogeneous images from different domains while being robust against perturbations in the imaging setup. Using our model, defect localization predictions can be made in less than half a second per layer using a standard MacBook Pro while achieving an F1-score of more than 0.9.
  arXiv  Detail & Related papers  (2022-12-16T17:02:55Z)
• Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator [68.0533826852601]
  3D-aware image synthesis aims at learning a generative model that can render photo-realistic 2D images while capturing decent underlying 3D shapes. Existing methods fail to obtain moderate 3D shapes. We propose a geometry-aware discriminator to improve 3D-aware GANs.
  arXiv  Detail & Related papers  (2022-09-30T17:59:37Z)
• Multi-View Object Pose Refinement With Differentiable Renderer [22.040014384283378]
  This paper introduces a novel multi-view 6 DoF object pose refinement approach focusing on improving methods trained on synthetic data. It is based on the DPOD detector, which produces dense 2D-3D correspondences between the model vertices and the image pixels in each frame. We report excellent performance in comparison to the state-of-the-art methods trained on the synthetic and real data.
  arXiv  Detail & Related papers  (2022-07-06T17:02:22Z)
• Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction [138.04956118993934]
  We propose a novel Transformer-based method, coarse-to-fine sparse Transformer (CST) CST embedding HSI sparsity into deep learning for HSI reconstruction. In particular, CST uses our proposed spectra-aware screening mechanism (SASM) for coarse patch selecting. Then the selected patches are fed into our customized spectra-aggregation hashing multi-head self-attention (SAH-MSA) for fine pixel clustering and self-similarity capturing.
  arXiv  Detail & Related papers  (2022-03-09T16:17:47Z)
• The KFIoU Loss for Rotated Object Detection [115.334070064346]
  In this paper, we argue that one effective alternative is to devise an approximate loss who can achieve trend-level alignment with SkewIoU loss. Specifically, we model the objects as Gaussian distribution and adopt Kalman filter to inherently mimic the mechanism of SkewIoU. The resulting new loss called KFIoU is easier to implement and works better compared with exact SkewIoU.
  arXiv  Detail & Related papers  (2022-01-29T10:54:57Z)
• Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning [97.56893524594703]
  Image-based 3D shape retrieval (IBSR) aims to find the corresponding 3D shape of a given 2D image from a large 3D shape database. metric learning with some adaptation techniques seems to be a natural solution to shape similarity learning. We develop a geometry-focused multi-view metric learning framework empowered by texture synthesis.
  arXiv  Detail & Related papers  (2020-10-23T08:52:00Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)
• Intrinsic Autoencoders for Joint Neural Rendering and Intrinsic Image Decomposition [67.9464567157846]
  We propose an autoencoder for joint generation of realistic images from synthetic 3D models while simultaneously decomposing real images into their intrinsic shape and appearance properties. Our experiments confirm that a joint treatment of rendering and decomposition is indeed beneficial and that our approach outperforms state-of-the-art image-to-image translation baselines both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2020-06-29T12:53:58Z)
• Open Source Computer Vision-based Layer-wise 3D Printing Analysis [0.0]
  The paper describes an open source computer vision-based hardware structure and software algorithm. It analyzes layer-wise the 3-D printing processes, tracks printing errors, and generates appropriate printer actions to improve reliability.
  arXiv  Detail & Related papers  (2020-03-12T08:33:10Z)

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.