# Physics-informed data-driven machine health monitoring for two-photon lithography

arXiv: https://arxiv.org/abs/2510.15075 · HTML: https://mlsanity.com/paper/2510.15075/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 36 references that are in the map.
- First posted on arXiv 16 Oct 2025.

## Builds on (most cited first, 1 of 1)

- [Federated Transfer Learning with Task Personalization for Condition Monitoring in Ultrasonic Metal Welding](https://mlsanity.com/paper/2404.13278/) (2024)

## Built on by (most cited first, 2 of 2)

- [Hybrid Synthetic Data Generation with Domain Randomization Enables Zero-Shot Vision-Based Part Inspection Under Extreme Class Imbalance](https://mlsanity.com/paper/2512.00125/) (2025)
- [Adaptive few-shot learning for robust part quality classification in two-photon lithography](https://mlsanity.com/paper/2601.08885/) (2026)

## Abstract (arXiv)

Two-photon lithography (TPL) is a sophisticated additive manufacturing technology for creating three-dimensional (3D) micro- and nano-structures. Maintaining the health of TPL systems is critical for ensuring consistent fabrication quality. Current maintenance practices often rely on experience rather than informed monitoring of machine health, resulting in either untimely maintenance that causes machine downtime and poor-quality fabrication, or unnecessary maintenance that leads to inefficiencies and avoidable downtime. To address this gap, this paper presents three methods for accurate and timely monitoring of TPL machine health. Through integrating physics-informed data-driven predictive models for structure dimensions with statistical approaches, the proposed methods are able to handle increasingly complex scenarios featuring different levels of generalizability. A comprehensive experimental dataset that encompasses six process parameter combinations and six structure dimensions under two machine health conditions was collected to evaluate the effectiveness of the proposed approaches. Across all test scenarios, the approaches are shown to achieve high accuracies, demonstrating excellent effectiveness, robustness, and generalizability. These results represent a significant step toward condition-based maintenance for TPL systems.