# Automated Non-Destructive Inspection of Fused Filament Fabrication Components Using Thermographic Signal Reconstruction

## TL;DR

This paper introduces an automated, AI-based non-destructive testing method using thermographic signal reconstruction for Fused Filament Fabrication components, achieving high accuracy in detecting subsurface delamination defects efficiently.

## Contribution

It presents a novel combination of thermographic signal processing and deep learning for high-accuracy, automated defect detection in FFF components, enabling reliable quality control.

## Key findings

- Deep Neural Network achieves 95.4% accuracy in delamination thickness classification.
- Automated inspection provides 100% defect detection in FFF components.
- Method reduces inspection time and cost for critical applications.

## Abstract

Manufacturers struggle to produce low-cost, robust and complex components at manufacturing lot-size one. Additive processes like Fused Filament Fabrication (FFF) inexpensively produce complex geometries, but defects limit viability in critical applications. We present an approach to high-accuracy, high-throughput and low-cost automated non-destructive testing (NDT) for FFF interlayer delamination using Flash Thermography (FT) data processed with Thermographic Signal Reconstruction (TSR) and Artificial Intelligence (AI). A Deep Neural Network (DNN) attains 95.4% per-pixel accuracy when differentiating four delamination thicknesses 5mm subsurface in PolyLactic Acid (PLA) widgets, and 98.6% accuracy in differentiating acceptable from unacceptable condition for the same components. Automated inspection enables time- and cost-efficient 100% inspection for delamination defects, supporting FFF's use in critical and small-batch applications.