Artificial intelligence is beginning to find its place on the aerospace MRO shopfloor, particularly where engineers are handling large volumes of inspection data and making decisions about component condition and repair. Recent discussion among senior aerospace MRO leaders has pointed towards predictive maintenance, machine vision, automated inspection, additive repair and digital MRO as areas attracting investment. Much of the attention is now on where these technologies can save engineering time and increase repair capacity. That is familiar territory for Derby-based AddQual. The company has been developing MiDAS around a relatively straightforward problem: aerospace businesses generate substantial amounts of measurement data, yet the commercial value comes from how quickly that information can be turned into an engineering decision.
“There is already a huge amount of good data being generated in aerospace inspection,” said AddQual Managing Director Ben Anderson. “The question we are interested in is what happens next. If an engineer can get from measurement to a qualified decision more quickly, there is a direct benefit to the repair process and ultimately to turnaround time.”
MRO demand puts inspection time under the spotlight
Aircraft remaining in service for longer are contributing to demand for more complex, high-quality maintenance and repair work. At the same time, MRO businesses are investing in capacity as operators focus closely on turnaround times and aircraft availability. Inspection sits directly in that equation. A component may pass through dimensional inspection, engineering review and qualification before a repair decision can progress. The more effectively information moves between those stages, the more productive the overall process can become. AddQual provides metrology, part verification, inspection planning, FAIR and LAIR support, engineering investigations and re-engineering services. Its digital development work is extending those capabilities by looking at how inspection information can be captured, structured and reused. MiDAS has been developed to bring measurement data into a digital environment where engineers can work with it more effectively. Rather than treating inspection as an isolated activity, the platform is intended to help make the information generated during measurement useful further along the engineering process Anderson said: “If you can remove time between inspecting the component, understanding the result and making the engineering decision, you create more capacity without simply measuring productivity by how many parts can be inspected in a shift. “That matters in MRO because the component is already part of a much bigger commercial timetable. The inspection process has to give the engineer the information they need in a form they can use.”
AI is moving closer to the engineering task
The aerospace sector is already exploring machine vision for hardware inspection and AI tools capable of interrogating service bulletins and engineering databases. Predictive maintenance is another established area of development, using operational information to give engineers a better understanding of when maintenance may be required. For AddQual, these developments make the quality of the underlying engineering data particularly important. AI has greater practical value when it is working with structured, traceable information. Metrology provides a rich source of that information because it describes the physical condition and geometry of the component itself.
“The interesting part of AI for us is its application to a real engineering task,” Anderson said. “We are looking at where it can help an engineer interrogate information, identify what matters and reach the right decision sooner. “Aerospace has very clear requirements around quality and traceability, so any technology we develop has to work within that engineering environment. That keeps the focus on useful applications rather than technology for its own sake.”
Digital twins give repair data a longer life
Digital twins are also becoming relevant to repair as MRO businesses look for better ways to retain and use information about individual components. AddQual has already explored this through its work with Turbine Repair Technologies on repair digital twins. The project examined how digital inspection and process data could support decisions at different stages of the repair value chain. For a sector accustomed to generating large quantities of inspection information, the approach changes the useful lifespan of that data. Measurements taken during inspection can form part of a digital record that supports subsequent engineering activity and provides a clearer picture of the component.
Anderson said: “A measurement result has value at the point of inspection, but it can have value beyond that point as well. Building a digital history around a component gives engineers more context and creates opportunities to learn from previous repairs. “That becomes particularly interesting when you start combining digital twins with automated inspection and AI. You have better information available to the engineer and more ways of using it.”
Repair technology is changing alongside inspection
The same MRO discussion identified additive repair as another technology likely to have an increasing role, particularly for components such as turbine blades where the condition of each part can vary. That makes accurate inspection and verification central to the process. Repair technologies may become more automated and adaptive, while aerospace businesses still need confidence in component condition, dimensional conformity and the evidence supporting qualification. AddQual's work across metrology, qualification and inspection places the business at this intersection between physical engineering and digital information. For Anderson, the direction of travel is already visible in the work taking place across aerospace MRO.
“Manufacturers and repair organisations are looking closely at turnaround time, capacity and how they make best use of experienced engineers,” he said. “Metrology, automation, digital twins and AI all have a role in that. Our job at AddQual is to make those technologies useful within the actual inspection and repair process, where saving engineering time and making good data easier to use can have a measurable impact on the operation.”
