Uncertainty quantification in immersion ultrasound measurements using a Bayesian inferencing approach

Abstract

Ultrasound nondestructive evaluation (NDE) methods often use a deterministic inverse model to reconstruct material properties. Such techniques rely on accurate information about the material such as wave-speed and attenuation at different frequencies, as well as information about the measurement system such as transducer radiation properties and measurement noise. However, in reality there is uncertainty associated with each of these important quantities. This is particularly important for structures manufactured using advanced manufacturing techniques since the mechanical properties of materials in these structures can vary significantly across the manufactured object. Prior work in uncertainty quantification for ultrasound NDE has been mostly limited to either simulation datasets for guided wave or resonant ultrasound measurements in metals prepared using conventional subtractive manufacturing techniques. Here we describe a new process for quantifying and incorporating the uncertainty in metal additive manufacturing from immersion ultrasound measurements and demonstrate that this can better defect detection with higher accuracy and confidence.