arXiv AI
Jun 19
Speeding up the annotation process in semantic segmentation industrial applications
arXiv:2606. 19934v1 Announce Type: cross Abstract: Current machine learning models commonly require large and well-annotated datasets.
Predicting Steel Fatigue Life from Micrographs Using Physics-Informed Deep Learning
arXiv:2607. 28695v1 Announce Type: cross Abstract: Here is the plain text version optimized for arXiv's submission form.
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arXiv Machine Learning
Jun 18
Investigation of Neural Network Methods for Reconstruction and Classification of Texture Images Under Conditions of Incomplete Information
arXiv:2204. 14224v3 Announce Type: replace-cross Abstract: The automated analysis of heterogeneous natural textures is frequently hindered by physical damage and data loss, presenting a significant challenge to computer vision.
arXiv Machine Learning
Jun 9
Balancing Real and Synthetic Data for CNN-based Masonry Crack Detection
arXiv:2606. 08033v1 Announce Type: cross Abstract: Cracks are a critical indicator of building health, and early stage identification is fundamental to prevent harmful damages.
arXiv Machine Learning
Jun 24
Machine Learning Modeling for Real-Time Melt Pool Monitoring in Laser Powder Bed Fusion Additive Manufacturing: A Hybrid Approach
arXiv:2606. 23851v1 Announce Type: new Abstract: This work investigates the implementation of artificial intelligence and machine learning (AI/ML) for real-time monitoring in laser powder bed fusion (LPBF) additive manufacturing.
arXiv Machine Learning
Jun 26
A Multi-Fidelity Convolutional Autoencoder-Transfer Learning Framework for Guided-Wave-Based Damage Diagnosis Using Large Simulated and Limited Experimental Datasets
arXiv:2606. 27304v1 Announce Type: new Abstract: Guided wave-based structural health monitoring (GWSHM) with onboard transducers offers significant potential for the early diagnosis of damage in engineering structures.
Hugging Face Trending Papers
Jul 6
ICME 2026 Grand Challenge on Cross-Scenario Defect Detection and Fine-Grained Severity Grading for High-Precision Manufacturing
This paper presents the IEEE International Conference on Multimedia and Expo (ICME) 2026 Grand Challenge on Cross-Scenario Defect Detection and Fine-Grained Severity Grading for High-Precision Manufacturing. The challenge is motivated by two key limitations of existing industrial defect inspection systems: (1) current deep learning-based methods often suffer significant performance degradation when deployed in unseen production scenarios, and (2) most benchmarks neglect severity-aware assessment, which is critical for risk control and yield optimization.
arXiv Machine Learning
Aug 11
Distribution-Free Conformal Prediction for Steel Fatigue Strength: Marginal Validity Is Not Enough
arXiv:2608. 07589v1 Announce Type: cross Abstract: Predicting fatigue failure in steel components experimentally is costly because it requires testing across multiple compositions and processing conditions.
Hugging Face Trending Papers
Jun 25
A Multi-Fidelity Convolutional Autoencoder-Transfer Learning Framework for Guided-Wave-Based Damage Diagnosis Using Large Simulated and Limited Experimental Datasets
Guided wave-based structural health monitoring (GWSHM) with onboard transducers offers significant potential for the early diagnosis of damage in engineering structures. However, the practical deployment of deep learning models is often hindered by the limited availability of labelled experimental data and the high computational cost of generating large-scale high-fidelity simulation datasets.
arXiv Machine Learning
Jun 9
Mesh Graph Neural Network Framework for Accelerating Finite Element Simulation for Arbitrary Geometries
arXiv:2606. 08287v1 Announce Type: new Abstract: Finite element analysis (FEA) is essential for structural design but remains computationally expensive, particularly when evaluating multiple design iterations or load scenarios.
arXiv AI
Jun 29
Applicability of memorization indicators for early spotting of overfitting while recalibrating sEMG-decoders on low sample sizes
arXiv:2606. 27855v1 Announce Type: cross Abstract: Deep learning models for surface electromyography (sEMG) can benefit substantially from subject-specific (re-)calibration, since no sufficiently large and diverse datasets are available to train fully generic decoders.
arXiv AI
Jun 30
FedLAS: Feature-Modulated Bidirectional Label Smoothing for Neural Network Calibration
arXiv:2606. 28654v1 Announce Type: cross Abstract: Deep Neural Network (DNN) classifiers suffer from poor calibration when their softmax outputs (predictive confidence) deviate from the empirical likelihoods.
arXiv AI
Jul 23
SynSur: An end-to-end generative pipeline for synthetic industrial surface defect generation and detection
arXiv:2604. 26633v2 Announce Type: replace-cross Abstract: Industrial surface defect inspection suffers from a fundamental data bottleneck: defects are rare, annotations require expert knowledge, and collecting balanced training sets is slow and costly.