Visual Inspection and 3D Scanning Systems for Quality Assurance

The oldest form of visual inspection is manual assessment by humans. With the advent of industrial image processing, this process has become increasingly automated.

However, classical systems reach their limits when conditions get difficult to control: they require complex, manually defined rules and the expertise of skilled personnel.

Technological advances in Artificial Intelligence (AI) and Machine Learning (ML) now enable new solutions that make visual inspection more efficient and more robust — even under challenging conditions.

In addition to surface inspection, 3D measurement of components is used for quality assurance. It is applied to verify dimensional accuracy and shape conformity using tactile or non-contact measurement methods.

Robot-based systems improve repeatability but are often complex and time-consuming to program or to teach.

With our developments at Fraunhofer IGD, we make inspection processes more efficient and more robust — creating tangible added value for our customers.

Your Benefits for Quality Assurance

Efficient Quality Assurance

  • Automated optical quality control with high reliability
  • Meeting process speed and cycle-time requirements
  • Reduced inference times
  • Reduced costs and training time through optimized AI/ML models (including pruning)
  • Less effort required for collecting data and images through training data synthesi

Flexibility and Autonomy in 3D Acquisition and Processing

  • Autonomous robot-based 3D scanning – no teaching, CAD models, scan plans, or manual programming required
  • Complete capture of all visible outer surfaces
  • High accuracy (up to 30 micrometers) and excellent color fidelity
  • 3D models directly usable without post-processing for quality assurance, 3D printing, web visualizations, and more
  • Automated surface treatment of scanned components, e.g., coating removal or paint stripping

Optimization of the Training Process

  • Training AI models exclusively with  images from good parts (OK parts)
  • Synthetic training data generation — including from 3D CAD models
  • Arbitrary object views generated from CAD data for pose estimation
  • raining data available even before physical components exist
  • Simulation of defects such as wear or incorrect assembly
  • Acceleration of algorithms and AI models, enabling fast training cycles

To fully realize these benefits in practice, several technological challenges must be addressed and this is exactly where our solutions come in.

Typical Challenges in Automated Quality Assurance and Our Contribution

Visual inspection is a proven method in quality assurance — traditionally performed manually, now increasingly automated through computer vision.

Classical systems require rigid rules and significant expert knowledge. Advances in AI and ML have opened new ways to make visual inspection more robust, flexible, and automated — even under difficult conditions.

Key Challenges We Address

  • High effort required to produce training data and train AI models
  • Strict inference-time requirements for inline inspection
  • Quality and robustness of visual inspection across varying conditions
  • Domain gap between synthetic and real-world images
  • Data drift caused by changing environmental factors
  • Automation of 3D capturing without manual teaching
  • Handling of difficult surface/material properties (e.g., gloss, reflections)

Fraunhofer IGD: Your Reliable Partner

We develop customized systems for optical quality control.

Our solutions combine AI-driven image processing, simulation-based data generation, and robot-assisted 3D scanning technologies.

We simulate, configure, and implement solutions tailored precisely to your requirements.

If you want to automate your quality assurance or discuss a concrete project, get in touch — we look forward to working with you!