Posted by Vidisco, April 19, 2023
Measuring twice to cut once: Non-Destructive Testing (NDT) methods are a crucial step in quality control processes for industries ranging from automotive to art. But evaluating and testing materials for defects is a technological challenge, with time consuming and labor-intensive workflows that have been difficult to automate – until now.
As the artificial intelligence and fourth industrial revolutions unfold, NDT methodologies like digital radiography are being refined and optimized for the future. Here, we’re taking a look at the journey NDT has taken to get here, and what it stands to gain through the adoption of AI.
The NDT revolution and its consequences
In its time, NDT represented a seismic shift from older, destructive methods of evaluating materials and detecting defects. With the advent of non-destructive techniques, this critical step in quality control became more accurate, more economical, and much more scalable. Radiography was just one of these game-changing techniques, alongside visual and optical aids, ultrasonic testing, electromagnetic testing and dye or liquid penetrants.
Right now, we’re living through another seachange that is reshaping NDT for the better, and paving the way for better, more scalable quality control. As AI-enhanced NDT – commonly known as NDT 4.0 – takes hold, industries stand to make significant gains in efficiency and accuracy.
NDT without AI: the current state of the art, and why it’s ripe for change
Most NDT methods require specialized and sensitive ndt equipment that has to be calibrated and maintained on a continual basis. This is especially true of NDT techniques like industrial radiography. For example, flat panel detectors that use either gamma rays or x-rays (especially for medical imaging), require proper handling and operation in order to yield optimal results. NDT personnel who use these tools are highly skilled, and their expertise comes at a significant cost.
Even with the most qualified personnel, factors like human error and fatigue introduce inaccuracy and inefficiency to the process. And in industries with very low tolerance for error, this really matters. Airbus A380 operators are still inspecting aircraft in the wake of wing cracks detected in 2019.
By leveraging AI, NDT can move beyond these technical and human limitations. Businesses who adopt these new methods are beginning to reap the benefits, both in terms of accuracy and cost-cutting efficiency.
From NDT 3.0 to NDT 4.0: why the future is bright
AI is already improving the efficiency and accuracy of NDT techniques while reducing inspection times and minimizing human errors. At the same time, intelligent algorithms can increase the probability of detection, a key metric in NDT systems.
How AI impacts the Probability of Detection (POD)
This refers to the likelihood that a system, device, or process will correctly identify a particular target or event. POD is usually expressed as a percentage or a decimal value. For example, if a detection system correctly identifies a target in 80 out of 100 trials, the probability of detection would be 0.8 or 80%.
In NDT, POD is crucial, because it suggests the reliability of a testing method. A low POD means the method may not detect all defects that are present. This can lead to false negatives, resulting safety hazards or costly repairs down the line.
By reducing human error and increasing the precision of inspections, AI ensures a higher POD. For example, through machine vision, AI algorithms can enhance and denoise images, foregrounding the most salient features. Human inspectors can then make more accurate calls, in less time. This helps inspectors of all experience levels to mitigate the impact of fatigue and variable perception.
AI can even analyze information from these images to detect material flaws autonomously through Automatic Defect Recognition (ADR). By flagging and labeling these defects, AI reduces the chance that they will escape the notice of inspectors. Taken together, image enhancement and ADR both close the net around easy-to-miss defects.
AI-enabled predictive maintenance: the stitch in time that saves nine (or more)
AI also creates possibilities for predictive maintenance. Algorithms can analyze real-time data to flag the possibility of failure before it actually occurs. That allows manufacturers to keep vital assets running at capacity, with the assurance of an early warning system. One of the ways this is done is by leveraging data from IoT sensors that continually monitor key indicators of machine failure. Through AI-enabled measures like these, companies can minimize disruption to manufacturing processes, and boost overall operational efficiency.
Detection at a distance: improving remote inspections through AI
Remote visual inspection (RVI) is a non-destructive testing technique that uses robotic crawlers and remotely operated cameras. Algorithms can analyze the data these systems collect, for more effective and fully autonomous inspections.
In addition to removing human error and fatigue, remote inspections remove safety risks for personnel. Although much safer than destructive testing methods, NDT techniques do entail some risks, and human inspectors need to be properly equipped throughout. Remote inspection is just one of the AI applications that enable skilled professionals to reclaim time from manual processes and reinvest it in critical decision making.
How Vidisco empowers NDT professionals to discover with confidence
The need to measure, evaluate and guarantee quality is among the most pressing challenges for manufacturing, transportation and pharmaceutical industries. At Vidisco, we have brought together some of the brightest minds in a wide variety of applications to help businesses eliminate doubt and guesswork, enabling them to make the right decisions, when the stakes are too high for anything else.
Our range of NDT solutions put cutting edge technology in the hands of the people who need it, for faster and more precise inspections and quality control. To learn more about how Vidisco could transform your NDT processes, schedule a live demo with our team.
The Renaissance of AI in NDT
With the merging of artificial intelligence and non-destructive testing, we’re on the brink of a renaissance in material inspection and quality control. The two domains, once considered to be poles apart, now dovetail beautifully. The synergy of AI with NDT promises a future where inspectors are better equipped, more informed, and capable of achieving unparalleled accuracy in their assessments.
Yet, as with all groundbreaking shifts, the marriage of AI and NDT is not just about technology; it’s about the people behind it. NDT professionals are no longer just technical experts; they are now information managers, interpreting intricate data sets, understanding AI outputs, and making critical decisions accordingly. The success of this collaboration rests on continuous learning and adaptation, ensuring that as AI evolves, so do the methodologies and expertise of those harnessing it. Vidisco is at the forefront of this evolution, not just as a technology provider but as an enabler, helping industries and professionals navigate this exciting paradigm shift.
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