Non-Destructive Testing Specialists

Prepare reports on non-destructive testing (NDT) results.

AI: Fully automatable - Generating standardized NDT reports from instrument data is highly automatable and widely implemented in software systems.

Document non-destructive testing (NDT) methods, processes, or results.

AI: Fully automatable - Documenting NDT methods, processes, and results is routine and can be fully automated into templates, logs, and databases.

Make radiographic images to detect flaws in objects while leaving objects intact.

AI: Fully automatable - AI-integrated radiography and computed tomography systems can acquire and analyze radiographic images to detect flaws non-destructively in many industrial applications.

Map the presence of imperfections within objects, using sonic measurements.

AI: Fully automatable - AI systems combined with ultrasonic sensors and robotic scanners can fully acquire, interpret, and map sonic-detected imperfections to produce defect maps in typical NDT contexts.

Visually examine materials, structures, or components for signs of corrosion, metal fatigue, cracks, or other flaws, using tools and equipment such as endoscopes, closed circuit television systems, and fiber optics.

AI: Fully automatable - Computer vision applied to endoscope/CCTV/fiber-optic feeds, often coupled with robotic camera deployment, can reliably detect and document corrosion, cracks, and similar flaws, enabling full automation in many inspections.

Identify defects in concrete or other building materials, using thermal or infrared testing.

AI: Fully automatable - Thermal and infrared imaging analysis for detecting concrete defects is routinely automated with drones, sensors, and AI algorithms that can identify delaminations and anomalies in most operational contexts.

Interpret or evaluate test results in accordance with applicable codes, standards, specifications, or procedures.

AI: Partial - AI can cross-reference results with codes and flag nonconformances, but final interpretation and certification typically require a qualified human due to nuance and liability.

Interpret the results of all methods of non-destructive testing (NDT), such as acoustic emission, electromagnetic, leak, liquid penetrant, magnetic particle, neutron radiographic, radiographic, thermal or infrared, ultrasonic, vibration analysis, and visual testing.

AI: Partial - AI tools can interpret outputs for many individual NDT modalities, but no single system reliably covers and certifies every method across all contexts.

Examine structures or vehicles such as aircraft, trains, nuclear reactors, bridges, dams, and pipelines, using non-destructive testing (NDT) techniques.

AI: Partial - Robotic and drone-mounted NDT sensors can perform many inspections, but access, setup, and on-site judgment in complex systems often need human technicians.

Select, calibrate, or operate equipment used in the non-destructive testing (NDT) of products or materials.

AI: Partial - AI can recommend, run calibration routines, and operate equipment automatically, but selection and complex field calibration still commonly require experienced human technicians.

Identify defects in solid materials using ultrasonic testing techniques.

AI: Partial - Automated ultrasonic inspection systems with machine learning can detect many defects, but complex geometries and ambiguous signals still require human judgment.

Produce images of objects on film, using radiographic techniques.

AI: Partial - Automated radiography systems can produce images, but the specific process of producing physical film images and some legacy workflows remain partly manual in many facilities.

Conduct liquid penetrant tests to locate surface cracks by coating objects with fluorescent dyes, cleaning excess penetrant, and applying developer.

AI: Partial - Liquid penetrant testing involves manual surface preparation, chemical handling, and nuanced physical application/cleaning steps—automation exists in controlled settings but AI cannot universally perform all physical tasks yet.

Develop or use new non-destructive testing (NDT) methods, such as acoustic emission testing, leak testing, and thermal or infrared testing.

AI: Partial - While AI can assist in using and optimizing established NDT methods and suggest experimental directions, developing genuinely new NDT techniques requires creative experimental design and domain expertise that AI cannot fully replicate.

Evaluate material properties, using radio astronomy, voltage and amperage measurement, or rheometric flow measurement.

AI: Partial - Automated instruments and AI can collect and process measurements like voltage/current and rheometry, but configuring complex tests, resolving atypical behaviors, and interpreting nuanced material properties still require human expertise.

Supervise or direct the work of non-destructive testing (NDT) trainees or staff.

AI: Not automatable - Supervising and directing trainees or staff involves leadership, accountability, and complex human judgment that AI cannot fully assume.