Predicting 3D printed plastic part properties: A deep learning approach with thermographic and vibration data fusion

Abstract

Additive Manufacturing (AM) holds transformative potential for the manufacturing industry, yet its widespread adoption is hindered by inconsistent product properties. This study addresses this challenge by pioneering a novel predictive method to assess the impact of in-process sensing on part property prediction in Fused Deposition Modeling (FDM), serving as a representative case study. Focused on five critical mechanical properties, including surface roughness, tensile strength, elongation at break, micro-hardness, and warpage, we systematically explore various sensor combinations by integrating Inertial Measurement Unit (IMU) sensors and a thermal camera with machine setting parameters. Utilizing deep learning models, specifically hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) architectures, we encode time-series sensor data into signal images and evaluate performance across different sensor configurations. Our best-performing model achieves an impressive 99% correlation in predicting tensile strength, albeit with less robust performance in predicting warpage and micro-hardness, suggesting additional influencing factors. Furthermore, the employed signal imaging technique outperforms a handcrafted feature selection alternative. These findings carry significant implications for advancing the broader adoption of AM in critical applications, addressing challenges associated with inconsistent product properties and unlocking its full potential.