  High-density polyethylene (HDPE) is used in applications ranging from cooling
water pipelines in nuclear power plants and distribution pipelines for natural
gas and hydrogen to biomedical implants. Embedded crack-like flaws form within
HDPE during fabrication or operations. Non-visible flaws can cause catastrophic
failure if undetected. Large structures such as HDPE pipelines require a fast,
non-destructive evaluation (NDE) method where the sensor can move rapidly
across the structure. This is only possible if the flaw is evaluated using
microseconds of time signal. We propose and show the accuracy of a machine
learning-based Ultrasound NDE method that can rapidly and accurately predict
embedded crack length and position simultaneously in HDPE with only tens of
microseconds of time signal sensing. A method to quantify crack size in HDPE
and other polymers using a very short Ultrasound time signal is lacking. We
propose that an optimally trained machine learning model can decipher the crack
characteristics using short measures of time signal, but a lack of large,
well-distributed, and labeled datasets to train machine learning models
continues to be a major limitation. To overcome this limitation, we have
conducted computer simulations of ultrasound on HDPE to develop training data.
We show that fully simulations trained convolutional neural network (CNN) can
accurately predict crack lengths and positions in HDPE from experimentally
measured ultrasound A-scan microsecond signals. Our method is based on the 1D
time amplitude signal acquired over a very short time period and not based on
2D image analysis. The proposed methodology presents a pathway for training CNN
using computationally generated data and applying the trained CNN in the field
to quantify hidden cracks in large HDPE or other polymer structures.
