Effective testing of surfaces in production
Surfaces made of metal or plastic should be as smooth as possible in industrial production. Every scratch, every unevenness must be detected and repaired at an early stage. Up to now, quality assurance has been carried out by humans. But the more complex the surface structure, the more error-prone the analysis with the human eye. To solve this problem, more and more manufacturers are relying on the use of Artificial Intelligence.
High-quality surfaces are characterized by their evenness. The smallest deviations and irregularities can lead to expensive defects in the end. Reliable and effective material testing is therefore essential to ensure optimum quality. However, quality assurance performed with the naked eye is often prone to errors and involves relatively high costs. It is also too slow for existing production processes. Due to fluctuations in attention, erroneous results inevitably occur time and again.
New benchmarks in quality assurance
Artificial Intelligence is now set to significantly improve the work of quality assurance personnel. To this end, elunic, a Munich-based software company, has further developed current quality control methods. The AI.SEE AI-based system enables accurate, fast, reliable and cost-effective quality assurance for metallic and various other materials-based surfaces. Using computer vision in combination with self-learning machine learning algorithms, better results are achieved.
The software makes it possible to immediately locate the smallest cracks on heterogeneous or reflective surfaces. Detected damage and defects for further processing are immediately forwarded to the responsible department. AI.SEE can enrich production in various industries, such as automotive, pharmaceutical and solar, for corporations and medium-sized companies.
Sensors and Communication
- Quality Control