For aerospace and defence manufacturers, using safety critical technologies, inspection becomes increasingly important. Every component must be verified against design intent, every deviation recorded, and every decision traceable for years after delivery. What is changing is how quality teams use the data generated by inspection — and what they expect it to tell them.
Modern metrology systems now capture vast volumes of dimensional, surface and visual data. A single complex component can generate thousands of measurements, point clouds and images. Traditionally, much of that information has been used only to make a binary decision: pass or fail. Increasingly, leading manufacturers are asking more fundamental questions.
What is changing in the process? Where is variation starting to creep in? And how can we intervene earlier? That shift — from inspection as a gate to inspection as intelligence — sits at the heart of AddQual’s “Beyond inspection” philosophy.“
The challenge for aerospace quality teams is making sense of what the data is really telling you about process stability, risk and repeatability. Inspection should inform decisions,” said Ben Anderson, Managing Director of AddQual.
From rules to insight
For decades, automated inspection has relied on fixed rules and thresholds: a tolerance limit, a surface roughness value, a go/no-go condition. While essential, those rules struggle to keep pace with today’s reality of low-volume production, complex geometries and frequent design evolution. Advanced analytics and AI now make it possible to look beyond isolated out-of-tolerance events and examine patterns over time. Subtle shifts in geometry, surface condition or measurement distributions can be detected long before parts fail specification.
AddQual’s MiDAS (Metrology Intelligent Data Analysis System) is designed around that principle. Rather than treating inspection as a static endpoint, MiDAS aggregates and structures data from multiple metrology sources — CMMs, 3D scanners, optical systems and NDT — to reveal trends, correlations and early indicators of variation. “MiDAS is about giving engineers visibility they’ve never had before — across machines, batches, suppliers and time. You still decide what action to take, but you’re doing it with far better evidence" explains Anderson.
Managing complexity and compliance
Aerospace quality operates under some of the most demanding regulatory frameworks in manufacturing. Standards such as AS9100, AS9145 and AS9102, along with NADCAP requirements, demand complete traceability — not just of parts, but of how inspection results were generated. As inspection technologies evolve, so does the documentation burden. Large datasets, version-controlled inspection routines and algorithm-driven analysis all have to remain auditable and defensible.
MiDAS addresses this by acting as a structured data backbone. Inspection results are linked to part serial numbers, process steps and revision states, creating a single source of truth that supports both operational decision-making and long-term compliance.“ Auditors want to know how you arrived at the result, with what equipment, under which conditions, and using which version of the method. Structured data is what makes that possible at scale" says Anderson.
From reaction to prevention
Historically, quality teams have spent much of their time reacting — investigating non-conformances, managing rework and explaining escapes. Data-led inspection enables a different model: prevention. By analysing process signatures and measurement trends, engineers can identify instability before it results in scrap or delay. Instead of chasing individual defects, they can focus on the root causes of variation. “When you can see drift developing across multiple parts or machines, you can act earlier,” Anderson said.
“That might mean adjusting a process, checking tooling, or working with a supplier before it becomes a programme issue. That’s where inspection starts to deliver real commercial value.”
Human oversight, amplified
Despite growing interest in AI and automation, aerospace manufacturers remain clear-eyed about the role of people. Inspection data may be analysed automatically, but decisions about airworthiness, safety and disposition remain firmly in human hands. AddQual’s approach reflects that reality. MiDAS enhances, rather than replaces, established quality disciplines such as measurement system analysis, change control and root-cause investigation. “This is about amplifying expertise,” Anderson said.
“Engineers spend less time collecting and formatting data, and more time applying their knowledge. That’s good for quality, and it’s good for retaining skilled people in the industry.”
Beyond inspection
As aerospace programmes become more complex and supply chains more distributed, the value of inspection data will only increase. The organisations that succeed will be those that treat metrology not as an isolated activity, but as a strategic source of insight. “Inspection will always be critical,” Anderson concluded. “However the real opportunity is what comes next — using data to understand, predict and improve.
That’s what beyond inspection really means.”With MiDAS, AddQual is positioning inspection data where it belongs: at the centre of decision-making in modern aerospace manufacturing.
