Adaptive few-shot learning for robust part quality classification in two-photon lithography

TL;DR

This paper introduces an adaptive computer vision framework for two-photon lithography that effectively detects new defect classes, adapts to new domains, and updates models with minimal data, enhancing quality control in dynamic manufacturing environments.

Contribution

It proposes a comprehensive adaptive CV framework combining novelty detection, few-shot incremental learning, and domain adaptation for robust part quality classification in TPL.

Findings

Hypothesis testing achieved 99-100% accuracy in novelty detection.

Incremental learning reached 92% accuracy with only 20 samples.

Domain adaptation achieved 96.19% accuracy with 5 shots.

Abstract

Two-photon lithography (TPL) is an advanced additive manufacturing (AM) technique for fabricating high-precision micro-structures. While computer vision (CV) is proofed for automated quality control, existing models are often static, rendering them ineffective in dynamic manufacturing environments. These models typically cannot detect new, unseen defect classes, be efficiently updated from scarce data, or adapt to new part geometries. To address this gap, this paper presents an adaptive CV framework for the entire life-cycle of quality model maintenance. The proposed framework is built upon a same, scale-robust backbone model and integrates three key methodologies: (1) a statistical hypothesis testing framework based on Linear Discriminant Analysis (LDA) for novelty detection, (2) a two-stage, rehearsal-based strategy for few-shot incremental learning, and (3) a few-shot Domain-Adversarial Neural Network (DANN) for few-shot domain adaptation. The framework was evaluated on a TPL dataset featuring hemisphere as source domain and cube as target domain structures, with each domain categorized into good, minor damaged, and damaged quality classes. The hypothesis testing method successfully identified new class batches with 99-100% accuracy. The incremental learning method integrated a new class to 92% accuracy using only K=20 samples. The domain adaptation model bridged the severe domain gap, achieving 96.19% accuracy on the target domain using only K=5 shots. These results demonstrate a robust and data-efficient solution for deploying and maintaining CV models in evolving production scenarios.