# AI-Powered Thermal Fingerprinting: Predicting PLA Tensile Strength Through Schlieren Imaging

## TL;DR

This study introduces a low-cost method using thermal imaging and machine learning to predict the strength of 3D-printed objects during printing.

## Contribution

The paper introduces 'thermal fingerprinting,' a novel non-destructive technique combining Schlieren imaging and machine learning for real-time tensile strength prediction in FDM printing.

## Key findings

- Thermal fingerprinting successfully classified cooling conditions with 100% accuracy.
- Initial validation showed strong correlation between thermal data and tensile strength (R2 = 0.808).
- Cross-validation highlighted the need for larger datasets to improve generalization (R2 = 0.301).

## Abstract

Fused deposition modeling (FDM) suffers from unpredictable mechanical properties in nominally identical prints. Current quality assurance relies on destructive testing or expensive post-process inspection, while existing machine learning approaches focus primarily on printing parameters rather than real-time thermal environments. The objective of this proof-of-concept study is to develop a low-cost, non-destructive framework for predicting tensile strength during FDM printing by directly measuring convective thermal gradients surrounding the print. To accomplish this, we introduce thermal fingerprinting: a novel non-destructive technique that combines Background-Oriented Schlieren (BOS) imaging with machine learning to predict tensile strength during printing. We captured thermal gradient fields surrounding PLA specimens (n = 30) under six controlled cooling conditions using consumer-grade equipment (Nikon D750 camera, household hairdryers) to demonstrate low-cost implementation feasibility. BOS imaging was performed at nine critical layers during printing, generating thermal gradient data that was processed into features for analysis. Our initial dual-model ensemble system successfully classified cooling conditions (100%) and showed promising correlations with tensile strength (initial 80/20 train–test validation: R2 = 0.808, MAE = 0.279 MPa). However, more rigorous cross-validation revealed the need for larger datasets to achieve robust generalization (five-fold cross-validation R2 = 0.301, MAE = 0.509 MPa), highlighting typical challenges in small-sample machine learning applications. This work represents the first successful application of Schlieren imaging to polymer additive manufacturing and establishes a methodological framework for real-time quality prediction. The demonstrated framework is directly applicable to real-time, non-contact quality assurance in FDM systems, enabling on-the-fly identification of mechanically unreliable prints in laboratory, industrial, and distributed manufacturing environments without interrupting production.