# Deep Learning-Assisted Microscopic Polarization Inspection of Micro-Nano Damage Precursors: Automatic, Non-Destructive Metrology for Additive Manufacturing Devices

## TL;DR

This paper introduces a new deep learning framework for detecting micro-nano damage in 3D-printed parts using polarization microscopy, improving accuracy and reducing reliance on large datasets.

## Contribution

The novel YOLOv11-LSF framework combines multi-scale perception and physical feature simulation to enable accurate, small-sample micro-nano damage detection in additive manufacturing.

## Key findings

- The YOLOv11-LSF model achieves 99% accuracy for porosity detection and 94% for crack detection.
- The model outperforms baselines with 1.6% higher precision and 2.8% higher mAP50-95.
- A virtual-real integrated training strategy reduces reliance on large labeled datasets.

## Abstract

Additive Manufacturing (AM), as a revolutionary breakthrough in advanced manufacturing paradigms, leverages its unique layer-by-layer construction advantage to exhibit significant technological superiority in the fabrication of complex structural components for aerospace, biomedical, and other fields. However, when addressing industrial-grade precision manufacturing requirements, key challenges such as the multi-scale characteristics of surface damage precursors, interference from background noise, and the scarcity of high-quality training samples severely constrain the intelligent transformation of AM quality monitoring systems. This study proposes an innovative microscopic polarization YOLOv11-LSF intelligent inspection framework, which establishes an automated non-destructive testing methodology for AM device micro-nano damage precursors through triple technological innovations, effectively breaking through existing technical bottlenecks. Firstly, a multi-scale perception module is constructed based on the Large Separable Kernel Attention mechanism, significantly enhancing the network’s feature detection capability in complex industrial scenarios. Secondly, the cross-level local network VoV-GSCSP module is designed utilizing GSConv and a one-time aggregation method, resulting in a Slim-neck architecture that significantly reduces model complexity without compromising accuracy. Thirdly, an innovative simulation strategy incorporating physical features for damage precursors is proposed, constructing a virtual and real integrated training sample library and breaking away from traditional deep learning reliance on large-scale labeled data. Experimental results demonstrate that compared to the baseline model, the accuracy (P) of the YOLOv11-LSF model is increased by 1.6%, recall (R) by 1.6%, mAP50 by 1.5%, and mAP50-95 by 2.8%. The model hits an impressive detection accuracy of 99% for porosity-related micro-nano damage precursors and remains at 94% for cracks. Its unique small sample adaptation capability and robustness under complex conditions provide a reliable technical solution for industrial-grade AM quality monitoring. This research advances smart manufacturing quality innovation and enables cross-scale micro-nano damage inspection in advanced manufacturing.