Phased Array Ultrasonic Testing (PAUT) Weld Inspection – Pattern Recognition
Machines are trained by humans to seek patterns in large volumes of data in order to discriminate between different data types, classes, or categories. A pattern recognition classifier (PRC), an artificial intelligence tool, is trained using data that can be traced back to a given class. Multiple data sets are used to train the PRC to differentiate between classes. This type of generic description applies to any type of information, or data, but how does one apply artificial intelligence and pattern recognition to non-destructive testing or more specifically, phased array ultrasonic testing (PAUT). The practice has been around since the 1980’s but we revisit the importance of input robust data nd principles here for phased array ultrasonic testing of welds. The article summarizes some of the challenges that are being assessed to automated PAUT weld defect classification.
Weld Defects and Phased Array Patterns
Weld defects can be generally categorized as volumetric and planar flaws. Volumetric weld flaws include porosity, slag, and inclusions. Planar weld flaws include lack of fusion (LOF), heat affected zone cracks, center line cracks, base metal cracks, and others. Some examples of weld defects that are routinely found with phased array ultrasonic testing (PAUT) are show in Figure 1.