Compression of Pulsed Infrared Thermography Data With Unsupervised Learning for Nondestructive Evaluation of Additively Manufactured Metals

Additive manufacturing (AM) of high-strength metals, which is typically based on laser powder bed fusion (LPBF), can introduce microscopic pores in the AM metal. Pulsed Infrared Thermography (PIT) offers several advantages for nondestructive imaging of subsurface defects in AM structures because the method is one-sided, non-contact and scalable to structures of arbitrary size. However, high-resolution PIT imaging results in the generation of a large volume of thermography data (~TB), which creates challenges for the storage and transmission of data. Compression of thermography data requires an approach that achieves high data compression ratio while preserving weak thermal features corresponding to microscopic material defects. We investigate thermography data compression using several unsupervised learning (UL) algorithms, which include Principal Component Analysis (PCA), Independent Component Analysis (ICA), Exploratory Factor Analysis (EFA), Sparse Dictionary Learning (SDL), and a novel lightweight Thermography Compressive Sparse Autoencoder (TCSA). Algorithms are benchmarked using PIT experimental data obtained from imaging of a stainless steel plate with calibrated porosity defects imprinted with AM process. For all algorithms, we obtain compression ratio >30 (highest compression of 46 is achieved with TCSA), and peak signal-to-noise ratio for reconstruction accuracy >73dB. Compared to existing methods, advantages of UL algorithms include achieving high compression ratio while preserving weak features to allow extraction of microscopic material defects from images. UL-based methods have general applicability because they are adaptable to compression of different data types, and allow for memory-efficient training and rapid on-line augmentation of the model.