How can you validate AI for quality control before scaling it in production?
Sideius, a specialist in material characterization and industrial computed tomography (iCT) used AI-MATTERS to test and validate AI-driven defect detection in real manufacturing conditions. By leveraging AI-MATTERS testing and experimentation facilities, the company reduced inspection time and improved operational efficiency, while gaining confidence to scale the solution further.
About the Company
Sideius is a leading company in material characterization, providing both destructive and non-destructive testing services, including advanced industrial computed tomography (iCT) for quality control. The company operates in high-performance sectors such as aerospace, space, and automotive.
For their innovation roadmpa, Sideius aimed to strengthen AI-driven quality inspection capabilities and scale their existing solution across different components and use cases.
The challenge: scaling AI validation in production
While Sideius had already developed an AI-powered solution in the 3DAIQ project, scaling the solution posed several challenges:
- Extending the system to support component-specific AI model training
- Validating performance across different use cases and materials
- Ensuring reliability in real production environments, not just controlled conditions
This reflects a common challenge in manufacturing: AI solutions that perform well in lab settings often struggle to scale reliably in real production environments.
The approach: testing and validation with AI-MATTERS
To address these challenges, Sideius collaborated with AI-MATTERS, a European network of testing and experimentation facilities that enable companies to validate AI solutions in real-world manufacturing environments.
Through the service Real-time decision making in industrial environments, Sideius:
- Gained access to real production data and a testing infrastructure
- Developed functionality for local training of AI models per component
- Tested and optimized AI models for specific quality control use cases
AI-MATTERS provides this type of support by enabling companies to test AI technologies under realistic industrial conditions before deployment, reducing uncertainty and improving reliability.
What was tested
Technology
- AI-based defect detection models
- Industrial computed tomography (iCT) data
Use Case
- Automated quality inspection of metallic components
Experiment setup
- Real production data
- Component-specific AI model training
- Validation in operational conditions
The Results
The collaboration delivered clear and measurable results:
- Reduced time required for iCT quality inspection
- Improved operational efficiency
- Increased customer satisfaction
- Validated scalability of AI-driven quality control
“The collaborationwith AI-MATTERS allowed us toreduce the time required byiCTquality inspection with a positive impact on operations efficiency and on our customer’s satisfaction” says Fabio Esposito, R&D Manager at Sideius.
Key Insights
From this experiment, several important lessons emerged:
- AI must be validated in real production environments to ensure reliability
- Component-specific models significantly improve performance
- Data availability and quality are critial for successful AI adoption
- Testing and experimentation reduce risk before scaling
What’s next?
Following the successful validation phase, Sideus plans to:
- Expand AI-driven quality control to additional components
- Further optimize models using production data
- Continue scaling AI integration across operations
Why AI-MATTERS?
AI-MATTERS enables companies to:
- Test AI solutions in real-world manufacturing enivronments
- Access advanced infrastructure and expertise
- Validate performance before scaling or investing
By bridging the gap between prototype and deployment, AI-MATTERS helps companies move from experimentation to industrial implementation with reduced risk.
FAQs
By testing AI solutions in real production environments using facilities like AI-MATTERS, companies can evaluate performance, reliability, and integration before full deployment.
AI validation includes testing with real data, evaluating performance, identifying bottlenecks, and assessing scalability in operational conditions.
Many AI solutions fail because they are not tested in real-world environments where variability, safety, and integration challenges occur.
This depends on the use case, but structured experimentation allows companies to quickly assess feasibility and impact before scaling.
Would you like to know how our project managers can help your organisation?
Contact us for a non-binding conversation: https://ai-matters.eu/contact/