Synthetic Defect Geometries of Cast Metal Objects Modeled via 2d Voronoi Tessellations
- URL: http://arxiv.org/abs/2602.05440v1
- Date: Thu, 05 Feb 2026 08:28:44 GMT
- Title: Synthetic Defect Geometries of Cast Metal Objects Modeled via 2d Voronoi Tessellations
- Authors: Natascha Jeziorski, Petra Gospodnetić, Claudia Redenbach,
- Abstract summary: In industry, defect detection is crucial for quality control. Non-destructive testing (NDT) methods are preferred as they do not influence the functionality of the object while inspecting.<n>To provide training data in sufficient amount and quality, synthetic data can be used. Rule-based approaches enable synthetic data generation in a controllable environment.
- Score: 0.10195618602298682
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In industry, defect detection is crucial for quality control. Non-destructive testing (NDT) methods are preferred as they do not influence the functionality of the object while inspecting. Automated data evaluation for automated defect detection is a growing field of research. In particular, machine learning approaches show promising results. To provide training data in sufficient amount and quality, synthetic data can be used. Rule-based approaches enable synthetic data generation in a controllable environment. Therefore, a digital twin of the inspected object including synthetic defects is needed. We present parametric methods to model 3d mesh objects of various defect types that can then be added to the object geometry to obtain synthetic defective objects. The models are motivated by common defects in metal casting but can be transferred to other machining procedures that produce similar defect shapes. Synthetic data resembling the real inspection data can then be created by using a physically based Monte Carlo simulation of the respective testing method. Using our defect models, a variable and arbitrarily large synthetic data set can be generated with the possibility to include rarely occurring defects in sufficient quantity. Pixel-perfect annotation can be created in parallel. As an example, we will use visual surface inspection, but the procedure can be applied in combination with simulations for any other NDT method.
Related papers
- MaskTerial: A Foundation Model for Automated 2D Material Flake Detection [48.73213960205105]
We present a deep learning model, called MaskTerial, that uses an instance segmentation network to reliably identify 2D material flakes.<n>The model is extensively pre-trained using a synthetic data generator, that generates realistic microscopy images from unlabeled data.<n>We demonstrate significant improvements over existing techniques in the detection of low-contrast materials such as hexagonal boron nitride.
arXiv Detail & Related papers (2024-12-12T15:01:39Z) - SYNOSIS: Image synthesis pipeline for machine vision in metal surface inspection [1.1802456989915404]
We introduce a complete pipeline which describes in detail how to approach image synthesis for surface inspection.
The pipeline is in detail evaluated for milled and sandblasted aluminum surfaces.
arXiv Detail & Related papers (2024-10-18T19:46:12Z) - Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verification [11.6055501181235]
We investigate the use of verification on synthesized data to prevent model collapse.
We show that verifiers, even imperfect ones, can indeed be harnessed to prevent model collapse.
arXiv Detail & Related papers (2024-06-11T17:46:16Z) - Visual Deformation Detection Using Soft Material Simulation for Pre-training of Condition Assessment Models [3.0477617036157136]
It proposes using Blender, an open-source simulation tool, to create synthetic datasets for machine learning (ML) models.<n>The process involves translating expert information into shape key parameters to simulate deformations, generating images for both deformed and non-deformed objects.
arXiv Detail & Related papers (2024-04-02T01:58:53Z) - A New Benchmark: On the Utility of Synthetic Data with Blender for Bare
Supervised Learning and Downstream Domain Adaptation [42.2398858786125]
Deep learning in computer vision has achieved great success with the price of large-scale labeled training data.
The uncontrollable data collection process produces non-IID training and test data, where undesired duplication may exist.
To circumvent them, an alternative is to generate synthetic data via 3D rendering with domain randomization.
arXiv Detail & Related papers (2023-03-16T09:03:52Z) - Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse
Data using a Learning-based Unscented Kalman Filter [65.93205328894608]
We learn the residual errors between a dynamic and/or simulator model and the real robot.
We show that with the learned residual errors, we can further close the reality gap between dynamic models, simulations, and actual hardware.
arXiv Detail & Related papers (2022-09-07T15:15:12Z) - CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of
Adversarial Robustness of Vision Models [61.68061613161187]
This paper presents CARLA-GeAR, a tool for the automatic generation of synthetic datasets for evaluating the robustness of neural models against physical adversarial patches.
The tool is built on the CARLA simulator, using its Python API, and allows the generation of datasets for several vision tasks in the context of autonomous driving.
The paper presents an experimental study to evaluate the performance of some defense methods against such attacks, showing how the datasets generated with CARLA-GeAR might be used in future work as a benchmark for adversarial defense in the real world.
arXiv Detail & Related papers (2022-06-09T09:17:38Z) - Multitask AET with Orthogonal Tangent Regularity for Dark Object
Detection [84.52197307286681]
We propose a novel multitask auto encoding transformation (MAET) model to enhance object detection in a dark environment.
In a self-supervision manner, the MAET learns the intrinsic visual structure by encoding and decoding the realistic illumination-degrading transformation.
We have achieved the state-of-the-art performance using synthetic and real-world datasets.
arXiv Detail & Related papers (2022-05-06T16:27:14Z) - StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact
Context-encoding Variational Autoencoder [48.2010192865749]
Unsupervised anomaly detection (UAD) can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out of distribution samples.
This research proposes a compact version of the "context-encoding" VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA)
The proposed pipeline achieved a Dice score of 0.642$pm$0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859$pm$0.112 while detecting artificially induced anomalies.
arXiv Detail & Related papers (2022-01-31T14:27:35Z) - Simulated Adversarial Testing of Face Recognition Models [53.10078734154151]
We propose a framework for learning how to test machine learning algorithms using simulators in an adversarial manner.
We are the first to show that weaknesses of models trained on real data can be discovered using simulated samples.
arXiv Detail & Related papers (2021-06-08T17:58:10Z) - Synthetic training data generation for deep learning based quality
inspection [0.0]
We present a generic simulation pipeline to render images of defective or healthy (non defective) parts.
We assess the quality of the generated images by training deep learning networks and by testing them on real data from a manufacturer.
arXiv Detail & Related papers (2021-04-07T08:07:57Z) - A Novel Anomaly Detection Algorithm for Hybrid Production Systems based
on Deep Learning and Timed Automata [73.38551379469533]
DAD:DeepAnomalyDetection is a new approach for automatic model learning and anomaly detection in hybrid production systems.
It combines deep learning and timed automata for creating behavioral model from observations.
The algorithm has been applied to few data sets including two from real systems and has shown promising results.
arXiv Detail & Related papers (2020-10-29T08:27:43Z)
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.