Conventional quality inspection methods, while reliable, often prove to be laborious and time-intensive. However, a notable challenge has surfaced: the manual inspection process is beset with inherent limitations, including scalability constraints, susceptibility to human error, and the occurrence of false positives.
By leveraging machine learning-driven Convolutional Neural Network (CNN) models or artificial intelligence (AI) algorithms, a multitude of defects in glass vials can now be comprehensively analysed in a single process, within a remarkably short timeframe. These advanced technologies enable the detection and classification of diverse defects, ranging from particles such as hair or stainless steel, to cracks in vials, as well as issues like incorrect stoppers or seals, and even leakage in closure systems.
Offering Includes:
Automated optical visual inspection machine with various rotational capabilities.
High-grade
industrial
camera for
precise imaging.
Deep learning
model for
efficientobject
identification.
Machine learning
model for defect detection, ensuring accuracy and reliability.
High throughput for
quality detection,
optimizing
productivity.
