Visual inspection has and will remain to be an integral part of the manufacturing quality control and assessment process, yet, human classification of defects is typically inconsistent and inaccurate due to distractions or fatigue, resulting in many man-hours wasted on manual visual inspections. While Automated Optical Inspection (AOI) systems have mostly addressed the shortcomings in manual quality control, rule-based AOIs tend to over-reject simple flaws as defects, resulting in substantial yield loss.
Visual Artificial Intelligence is able to accurately and consistently identify defects, leveraging the data-rich environment of manufacturing - translating to a smaller margin of error during defect analysis.
This technology is an in-line visual inspection Artificial Intelligence (AI) platform that utilises image-based data obtained from any existing automated image capture systems (for quality control and assurance), to conduct automated inspections at a higher rate than a human being for highly accurate, consistent defect classification and yield improvement.
This technology is designed and built to automate defect analysis for a large number of images from a variety of image-based sources e.g. Automated Visual Inspection (AVI) machine, Energy-Dispersive X-Ray Spectroscopy (EDX), Automated Optical Inspection (AOI) machine, Advanced 3D X-Ray Inspection (AXI) machine, Complementary Metal Oxide Semiconductor (CMOS) cameras and Scanning Electron Microscopes (SEM), at a time.
It has the following key features:
This technology has applications in the following sectors/industries:
The technology owner is keen to collaborate with high-value, complex manufacturing companies through R&D collaboration, new product/service co-development, test-bedding, and/or licensing.
Additionally, the technology owner is also keen to work with technology partners to co-develop enhanced Artificial Intelligence (AI) root-cause analysis and predictive analytics capabilities.