based on the WIT-T:2020 syllabus Share public link

Labor shortages and strict safety protocols in 2020 and 2021 drove rapid adoption of automated and remote inspection hardware. Unmanned Inspection Crawlers

All technological advances during this period operated within a robust framework of industry standards. The following key standards were active or newly issued:

The benefits of non-contact inspection were particularly evident in the 2020 work on for wire + arc additive manufactured (WAAM) samples, published in Ultrasonics . The LU system, comprising a pulsed laser and a laser interferometer, achieved non-contact inspection of artificial defects—cracks, flat-bottom holes, and through-holes—without requiring surface machining. This approach is ideal for additively manufactured components and high-value parts where surface preparation is impractical. Research on finite element simulation of laser-generated ultrasonic waves for quantitative detection of internal weld defects, published in Optik (DOI: 10.1016/j.ijleo.2020.165361), enabled detailed analysis of the interaction between laser-generated Rayleigh waves and internal defects.

Perhaps the most significant forward leap during this biennial period was the integration of automated data analysis. In 2020, the industry saw an influx of inspection software designed to reduce human error. However, by 2021, the conversation had shifted toward Artificial Intelligence (AI) and Machine Learning (ML).

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Evaluate the soundness and flexibility of the weld metal and heat-affected zone (HAZ).

Mandated that all digital UT and RT data must be stored in a non-proprietary format (e.g., DICONDE). This drove the demand for software solutions documented in many 2020-2021 technical PDFs.

It offers exceptional speed and accuracy for thick-walled pressure vessels and piping welds, independent of the flaw orientation. Computed Radiography (CR) and Digital Radiography (DR)

: Traditional X-ray films were increasingly replaced by digital imaging. Computer-Aided Detection (CAD) and Automatic Defect Recognition (ADR) algorithms improved the efficiency of X-ray inspections by automatically locating and classifying flaws.