​​​Artificial Intelligence in quality control enhancing the potential of Qualitron

Data is the fuel of Artificial Intelligence. Industrial processes generate millions of signals every day and when organized they train models to recognize patterns and improve decisions. The quality of the dataset defines the quality of the AI and nowhere is this more critical than in quality control, where even the smallest variation matters.

In ceramics, inspection is a key passage to validate the quality of a product. With surfaces, decorations and finishes so diverse, each deviation must be judged precisely. System Ceramics responds with Qualitron, its advanced vision system. Traditional algorithms remain a solid starting point, but the integration of AI trained on real-world image datasets enhances the system’s ability to interpret tolerances, recognize subtle variations, and tailor inspection to the true complexity of ceramic production processes.

Current inspection methods and their limitations

Today, quality control in ceramics relies largely on image comparison. Each tile is matched with a reference sample and deviations are highlighted. The method ensures consistency, yet it depends on thresholds defined in advance. The system can show that something is different, but it cannot determine whether that difference is a defect or an acceptable irregularity. In ceramics, even a slight misalignment between the digital decoration and the tile body can compromise quality.  

AI can enhance this process. With a large, classified dataset of images, models could learn to separate critical defects from tolerable imperfections. The challenge lies in the dataset itself: every image requires high resolution, careful annotation and validation.

System Ceramics is already testing how AI can expand Qualitron’s scope in areas where rule-based systems face limits.

Hybrid approaches combining vision algorithms with AI

The future of quality control in ceramics is likely to follow a hybrid model. A first stage uses conventional comparison to quickly flag deviations. A second stage relies on AI to interpret those deviations, distinguishing between defects, acceptable variations and borderline cases.

This layered approach keeps the speed and stability of traditional algorithms while adding the intelligence of machine learning. False rejects decrease, manual intervention becomes less frequent and production flows with fewer interruptions. The inspection process becomes not only more accurate but also more adaptable to production diversity.

Challenges specific to ceramic inspection

Applying AI to ceramics comes with unique technical demands. Inspection images must capture extremely fine details, requiring very high resolutions that strain machine learning models. The decorative variety of collections demands datasets broad enough to reflect countless patterns and finishes. Without such coverage, AI risks inconsistencies in its recognition.

Industrial adoption will require further refinement, but the direction is clear: AI can take inspection beyond rigid comparison and toward more flexible, intelligent evaluation.

The creation of comprehensive datasets also faces challenges related to data ownership, as customers are often protective of visual data — a factor that can slow down dataset expansion and model training. System Ceramics continues to invest in AI solutions that learn from production data and evolve toward predictive improvement, while maintaining full data protection and confidentiality.

How AI enhances the value of Qualitron

Qualitron already ensures reliable vision control through established algorithms. With AI integration, it becomes capable of identifying subtle imperfections, segmenting complex profiles and aligning decorations with greater accuracy. Training on curated datasets of real production images allows the system to build a knowledge base that strengthens consistency across shifts and lines.

The benefits are tangible: setup time decreases, inspection becomes steadier and operators spend less time on manual validation. Customers gain confidence in quality and production reaches standards more consistently.

Strategic direction and future outlook

System Ceramics presents AI development as a gradual but determined journey. Today, System Ceramics is enforcing artificial intelligence in its technologies, such as I-GV navigation and graphic deformation correction. The next step focuses on defect recognition through large image databases. In the long term, integration with management software could allow inspection not only to detect defects but also to predict potential issues, suggest corrections and feed intelligence back into plant optimization.

This strategy requires perseverance and technical depth, yet it ensures that immediate progress coexists with a broader vision.

By combining proven vision algorithms with ongoing AI research, System Ceramics continues to advance inspection technologies in a sector where accuracy and reliability define competitiveness.

Artificial Intelligence driving predictive inspection

The integration of AI into Qualitron signals a move toward inspection systems that combine accuracy, adaptability and learning. Models gain the ability to classify defects with nuance, handle decorative variability and reduce manual oversight. At the same time, ongoing research prepares the ground for predictive inspection, capable of anticipating issues before they appear and helping plants maintain efficiency.

For customers, this means more consistent quality control and stronger support in managing the complexity of ceramic production. For System Ceramics, it represents a path that unites present performance with future readiness.

Discover Qualitron and how it can enhance ceramic production.