Recognition of Defective Mineral Wool Using Pruned ResNet Models

• URL: http://arxiv.org/abs/2211.00466v1
• Date: Tue, 1 Nov 2022 13:58:02 GMT
• Title: Recognition of Defective Mineral Wool Using Pruned ResNet Models
• Authors: Mehdi Rafiei, Dat Thanh Tran, Alexandros Iosifidis
• Abstract summary: We developed a visual quality control system for mineral wool. X-ray images of wool specimens were collected to create a training set of defective and non-defective samples. We obtained a model with more than 98% accuracy, which in comparison to the current procedure used at the company, it can recognize 20% more defective products.
• Score: 88.24021148516319
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mineral wool production is a non-linear process that makes it hard to control the final quality. Therefore, having a non-destructive method to analyze the product quality and recognize defective products is critical. For this purpose, we developed a visual quality control system for mineral wool. X-ray images of wool specimens were collected to create a training set of defective and non-defective samples. Afterward, we developed several recognition models based on the ResNet architecture to find the most efficient model. In order to have a light-weight and fast inference model for real-life applicability, two structural pruning methods are applied to the classifiers. Considering the low quantity of the dataset, cross-validation and augmentation methods are used during the training. As a result, we obtained a model with more than 98% accuracy, which in comparison to the current procedure used at the company, it can recognize 20% more defective products.

Related papers

• Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing [0.0]
  One of the most promising use-cases for machine learning in industrial manufacturing is the early detection of defective products. We evaluate current vision transformer models together with anomaly detection methods. We give guidelines for choosing a suitable model architecture for a quality control system in practice.
  arXiv  Detail & Related papers  (2024-11-22T14:12:35Z)
• Training on the Test Model: Contamination in Ranking Distillation [14.753216172912968]
  We investigate the effect of a contaminated teacher model in a distillation setting. We find that contamination occurs even when the test data represents a small fraction of the teacher's training samples.
  arXiv  Detail & Related papers  (2024-11-04T17:11:14Z)
• Improved Distribution Matching Distillation for Fast Image Synthesis [54.72356560597428]
  We introduce DMD2, a set of techniques that lift this limitation and improve DMD training. First, we eliminate the regression loss and the need for expensive dataset construction. Second, we integrate a GAN loss into the distillation procedure, discriminating between generated samples and real images.
  arXiv  Detail & Related papers  (2024-05-23T17:59:49Z)
• Wafer Map Defect Patterns Semi-Supervised Classification Using Latent Vector Representation [8.400553138721044]
  The demand for defect detection during integrated circuit fabrication stages is becoming increasingly critical. Traditional wafer map defect pattern detection methods involve manual inspection using electron microscopes. We propose a model capable of automatically detecting defects as an alternative to manual operations.
  arXiv  Detail & Related papers  (2023-10-06T08:23:36Z)
• Voxel-wise classification for porosity investigation of additive manufactured parts with 3D unsupervised and (deeply) supervised neural networks [5.467497693327066]
  This study revisits recent supervised (UNet, UNet++, UNet 3+, MSS-UNet) and unsupervised (VAE, ceVAE, gmVAE, vqVAE) DL models for volumetric analysis of AM samples from X-CT images. It extends them to accept 3D input data with a 3D-patch pipeline for lower computational requirements, improved efficiency and generalisability. The VAE/ceVAE models demonstrated superior capabilities, particularly when leveraging post-processing techniques.
  arXiv  Detail & Related papers  (2023-05-13T11:23:00Z)
• Post-training Model Quantization Using GANs for Synthetic Data Generation [57.40733249681334]
  We investigate the use of synthetic data as a substitute for the calibration with real data for the quantization method. We compare the performance of models quantized using data generated by StyleGAN2-ADA and our pre-trained DiStyleGAN, with quantization using real data and an alternative data generation method based on fractal images.
  arXiv  Detail & Related papers  (2023-05-10T11:10:09Z)
• An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design [55.62660894625669]
  Lithography modeling is a crucial problem in chip design to ensure a chip design mask is manufacturable. Recent developments in machine learning have provided alternative solutions in replacing the time-consuming lithography simulations with deep neural networks. We propose a litho-aware data augmentation framework to resolve the dilemma of limited data and improve the machine learning model performance.
  arXiv  Detail & Related papers  (2022-10-27T20:53:39Z)
• Fake It Till You Make It: Near-Distribution Novelty Detection by Score-Based Generative Models [54.182955830194445]
  existing models either fail or face a dramatic drop under the so-called near-distribution" setting. We propose to exploit a score-based generative model to produce synthetic near-distribution anomalous data. Our method improves the near-distribution novelty detection by 6% and passes the state-of-the-art by 1% to 5% across nine novelty detection benchmarks.
  arXiv  Detail & Related papers  (2022-05-28T02:02:53Z)
• Outlier-Robust Learning of Ising Models Under Dobrushin's Condition [57.89518300699042]
  We study the problem of learning Ising models satisfying Dobrushin's condition in the outlier-robust setting where a constant fraction of the samples are adversarially corrupted. Our main result is to provide the first computationally efficient robust learning algorithm for this problem with near-optimal error guarantees.
  arXiv  Detail & Related papers  (2021-02-03T18:00:57Z)
• Toward Enabling a Reliable Quality Monitoring System for Additive Manufacturing Process using Deep Convolutional Neural Networks [0.0]
  We propose an automated quality grading system for the Additive Manufacturing (AM) process using a deep convolutional neural network (CNN) model. The CNN model is trained offline using the images of the internal and surface defects in the layer-by-layer deposition of materials and tested online by studying the performance of detecting and classifying the failure in AM process at different extruder speeds and temperatures. The proposed online model adds an automated, consistent, and non-contact quality control signal to the AM process that eliminates the manual inspection of parts after they are entirely built.
  arXiv  Detail & Related papers  (2020-03-06T20:49:20Z)

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.