Transforming AI-powered inspection in aerospace manufacturing with synthetic data

Minor surface defects can have major consequences in aerospace. Synthetic data enables AI to detect rare anomalies with >99% accuracy without disrupting production.

PHOTOS COURTESY ZETAMOTION

Turning the tide on manufacturing waste

In 2025, sustainability isn’t just a goal – it’s a metric of resilience. Aerospace manufacturers face extraordinary pressure to reduce emissions, minimize raw material waste, and increase production efficiency without compromising safety or performance.

With aircraft component tolerances measured in microns and certification requirements growing stricter every year, aerospace manufacturers must ensure quality while also pursuing leaner, greener operations. And yet, even in this high-precision field, production waste remains a significant challenge.

Artificial intelligence (AI)-powered inspection is emerging as a key enabler. While predictive maintenance and digital twins get much of the spotlight, some of the most impactful gains in aerospace sustainability are being realized through advances in quality control – particularly through synthetic data.

Quality control: A critical checkpoint

In aerospace, quality control safeguards more than performance – it protects lives, reputations, and bottom lines. Defects, however small, can delay programs, ground fleets, or require full component rework. Early detection and prevention are vital.

Yet traditional quality systems – even those using early forms of AI– struggle to exceed the 80% accuracy threshold. That’s simply not enough when the cost of an undetected surface flaw could run into millions, or worse, lead to structural compromise. Aerospace parts operate in extreme environments and must meet rigorous standards of integrity and reliability. The smallest oversight can have disproportionately large consequences.

Data gaps are holding back AI

The bottleneck isn’t the algorithm. It’s the data. According to MIT Technology Review Insights, 57% of manufacturing executives cite poor data quality as the primary barrier to AI success. In aerospace, where parts vary by supplier, condition, and material batch, inconsistency is especially high.

Gathering enough labeled defect examples – particularly for rare, safety-critical anomalies – is time-consuming and often impossible without disrupting production. Assembling these datasets in-house requires time and deep domain expertise. These constraints often delay AI deployments and limit the models’ ability to generalize across product variants.

Moreover, traditional data collection methods are expensive and prone to inconsistency. Lighting variations, camera positioning, surface reflectivity, and human labeling errors all reduce the reliability of the data being fed into models. As a result, AI systems may perform well in labs or controlled settings but fail to generalize effectively on the production floor.

In short: bad or limited data = bad AI.

Synthetic data: Virtual inspections, real results

Synthetic data changes the equation. Instead of waiting months to collect edge-case defects, manufacturers can simulate surface flaws, lighting conditions, part textures, and more – with precision.

For aerospace, this means being able to train inspection models on thousands of defect variations without exposing real parts to damage. Models get better, faster – without interrupting production lines.

Synthetic data enables a new paradigm in training: a curated environment where edge cases are not just possible, but easily replicable. It transforms model training from an exercise in patience to one of precision.

The benefits extend beyond accuracy. With synthetic data, manufacturers gain full control over dataset balance, ensuring models aren’t overfitted to common cases while underperforming on critical rare anomalies.

From proof of concept to production: A strategic advantage

Across advanced manufacturing, synthetic data is becoming the cornerstone of scalable AI systems. Aerospace is no exception. Where legacy data collection takes months, synthetic approaches enable inspection systems to be deployed in weeks – with 99% accuracy achievable from day one.

This acceleration is vital in aerospace, where product life cycles are long but variant-specific certifications and changing supplier inputs require fast, flexible adaptation. AI models trained on synthetic data can be easily re-trained or tuned for new components or specifications without starting from scratch.

And manufacturers no longer need to do this themselves. AI inspection partners can deliver trained systems built on aerospace-specific synthetic datasets, eliminating the need for lengthy in-house data collection and reducing onboarding time. These ready-to-deploy solutions help manufacturers hit the ground running.

That means faster response to customer-specific variants, certification cycles, and regulatory changes – without retraining from scratch.

This also improves return on investment: synthetic-data-enabled systems require less manual oversight, dramatically reduce false positives, and integrate with manufacturing execution systems (MES) to generate actionable insights that can drive continuous process improvement.

Human-in-the-loop: Essential for aerospace integrity

Even the best AI needs skilled oversight. Human-in-the-loop (HITL) systems ensure critical aerospace knowledge – what constitutes a defect, what can be tolerated – is preserved. Experienced engineers validate, refine, and contextualize AI outputs.

Think of it as giving your most experienced inspectors the world’s smartest assistant. They still decide – but now they can move faster, with better data.

HITL frameworks foster trust between AI systems and human teams. In high-stakes aerospace environments, that trust is crucial. Engineers can gradually shift from reviewing every image to intervening only on edge cases, scaling expertise across operations.

This synergy improves model performance and ensures traceability and regulatory compliance – essential in aviation manufacturing. With full defect traceability, audit trails, and explainable AI logic, HITL-enabled systems help manufacturers meet stringent aerospace quality standards, such as AS9100 and NADCAP.

The strategic role of partnerships

Synthetic data isn’t just a workaround – it’s a foundational shift in how AI-powered inspection is built and scaled. With the right inspection partner, aerospace manufacturers can:

• Deploy new inspection systems faster
• Cut waste and rework
• Preserve safety-critical domain knowledge
• Increase agility when adapting to new variants or specs
• Access ongoing model updates and dataset improvements

Success in aerospace doesn’t just depend on having the right tools – it’s about having the right partners who understand your workflows, your regulatory landscape, and your pace of innovation.

A capable partner brings more than technology – they bring aerospace-specific experience, curated datasets, and a roadmap for scaling AI inspection across your product lines.

Conclusion: Accuracy, speed, and sustainability – no longer a trade-off

With synthetic data and human-centered AI systems, aerospace manufacturers no longer must choose between safety, speed, and sustainability. These technologies enable all three – and future-proof inspection processes in an increasingly demanding environment.

From faster deployment timelines to higher first-pass yield rates, synthetic data is enabling a transformation in how quality is defined and delivered. And unlike older AI models that required massive datasets and months of training, synthetic data allows for agile adaptation to evolving manufacturing challenges.

As synthetic data moves from R&D into everyday aerospace operations, the manufacturers who embrace it early will lead not only in quality – but in resilience, reputation, and readiness for what’s next.

In the high-stakes world of aerospace manufacturing, the true differentiator is no longer who has access to AI – it’s who has the best data. And synthetic data, built for accuracy, speed, and relevance, is the fuel powering a new era of intelligent, responsible production.

Zetamotion Inc.
https://zetamotion.com

About the author: Dr. Wilhelm Klein is CEO of Zetamotion. He can be reached at klein@zetamotion.com.