As additive manufacturing (AM) continues to scale into production, one challenge consistently stands in the way: ensuring part quality without slowing everything down.
Quality assurance (QA) is the foundation for solving that challenge. However, in additive manufacturing, QA requires a different approach, one that moves beyond post-build inspection and into real-time, in-process understanding.
What is Quality Assurance?
Quality assurance is a proactive, process-orientated approach focused on preventing defects before they occur to ensure products meet specified qualification standards.
It focuses on:
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Preventing defects
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Controlling processes
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Driving repeatability
Put simply: QA is the process of ensuring a product meets defined requirements before it reaches the end user.
How Does Quality Assurance Apply to Additive Manufacturing?
In additive manufacturing, quality assurance means monitoring, measuring, and understanding the build process as it happens.
Unlike traditional manufacturing, AM introduces unique challenges:
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Parts are built layer by layer
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Defects can form internally and remain hidden
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Small process variations can significantly impact part performance
As a result, quality assurance in additive manufacturing must go beyond visual checks or final inspection and focus on layer-level measurement, in-situ inspection and data-driven process control.
Why Does Quality Assurance Matter?
For industries like aerospace, defense, and medical, quality assurance is critical.
Without robust QA:
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Defects are discovered too late
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Scrap and rework increase
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Qualification timelines extend
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Production scalability is limited
Traditionally, manufacturers have relied heavily on post-process inspection methods like CT scanning. While effective, these approaches are time consuming, expensive, and reactive.
To truly scale additive manufacturing, quality needs to move upstream into the build process itself.
Key Components of Quality Assurance in Manufacturing
A strong quality assurance strategy in additive manufacturing typically includes:
1. Process Monitoring
Tracking machine parameters, environmental conditions, and build behaviour.
2. In-Situ Inspection
Capturing real-time data during the build to detect anomalies as they occur.
3. Data Analysis
Turning raw process data into actionable insights.
4. Traceability
Maintaining detailed records for compliance and certification.
5. Qualification Criteria
Defining measurable thresholds for part acceptance or rejection.
Implementing Quality Assurance in Additive Manufacturing
Implementing quality assurance in additive manufacturing is focused on changing how decisions are made.
The traditional approach includes:
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Monitoring signals
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Inspection after the build
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Reacting to failures
The modern approach:
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Measure the build in real time
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Detect defects as they form
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Make decisions based on data
This shift enables manufacturers to move from trial and error, to controlled, repeatable production.
Quality Assurance vs Quality Control
As mentioned, quality assurance is a proactive process focused on preventing defects before they happen, while quality control (QC) is reactive and focuses on inspecting final parts. In short, QA ensures the process is right, and QC ensures the part is right.
Examples:
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QA: In-situ inspection (e.g. Fringe Inspection™)
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QC: CT scanning, destructive testing
QC still plays a role, but QA is what enables scalable production.
The Benefits of Implementing Quality Assurance
A well-executed QA strategy delivers:
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Reduced scrap and rework
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Faster qualification cycles
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Lower inspection costs
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Improved part consistency
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Increased confidence in production
Most importantly, it allows manufacturers to trust their process, and not simply verify the outcome.
Quality Assurance KPIs
To measure success, manufacturers typically track:
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Defect detection rate
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Scrap rate
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Build success rate
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Time to qualification
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Inspection time per build
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Cost per part
With in-situ, measurement-based QA, improvements can be seen across all of these metrics.
Best Practices for Quality Assurance in Additive Manufacturing
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Shift from monitoring to measurement-based inspection
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Define clear, quantitative thresholds for defects
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Integrate QA into the digital thread
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Reduce reliance on manual, post-process inspection
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Use data to inform decisions, not assumptions
Real-World Example: Why Quality Assurance Matters
In one application, a manufacturer was relying on visual image review to identify defects across builds. This required manually reviewing thousands of layer images – a time-consuming and inconsistent process.
By implementing Fringe Inspection™, they were able to:
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Replace subjective review with quantitative measurement
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Reduce review time significantly
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Improve detection of critical anomalies
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Move from post-build image review to real-time, measurement-based analysis using the Fringe Inspection Quality Chart. This provided quantitative visibility into anomalies and allowed precise identification of when they occurred during the build.
Read the full case study here.
Conclusion
Quality assurance in additive manufacturing is evolving. To move from prototyping to production, manufacturers must shift from reactive inspection to proactive, measurement-driven quality assurance.
This is how quality becomes scalable, and how additive manufacturing reaches its full potential.
FAQs
What is the difference between quality assurance and quality control?
Quality assurance focuses on preventing defects during production, while quality control identifies defects after the part is complete.
Why is quality assurance more important in additive manufacturing?
As defects can form during the build and remain hidden, doing post-process inspection alone is insufficient.
Can additive manufacturing rely only on CT scanning?
No. CT scanning is valuable, but it’s too slow and expensive to support scalable production on its own.
What does “in-situ” mean in additive manufacturing?
It refers to inspection or measurement that happens during the build process, not after.
How does Fringe Inspection™ support quality assurance?
Fringe Inspection™ provides layer-by-layer, unit-based measurements that allow manufacturers to detect, quantify, and act on defects in real time.
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