Scroll Top
SENSURE to showcase smart vision inspection systems

At iba, SENSURE is showcasing its vision inspection solutions (STARGATE series), which use high-speed, non-contact imaging cameras and/or profilometer lasers to quantify a wide range of product quality parameters (shape, size, color, height, topping coverage, volume, etc.) at high speed. All solutions provide continuous and quantitative data that can be used to identify process issues and create a library of information for analysis.

The core of the STARGATE family of products is the SENSURE SYNAPSE software, which utilizes artificial intelligence and self-learning to ensure the highest quality control for bakery products, including biscuits, cookies, crackers, bread products (croissants, buns, muffins, etc.), cakes, pizza, snacks and confectionary, etc. The software’s artificial intelligence forms the foundation of the system, enabling it to perform numerous functions, including automatic feature selection for product control and optimization of tolerances for measurement control. SENSURE SYNAPSE has an intuitive dedicated graphic interface between the artificial system and the operator, also providing a complete defect analysis, explaining the logic behind the rejection process (explainability).

The SENSURE STARGATE family of products can be customized to meet specific product and inspection requirements, as well as production line needs. Designed for easy installation in both new and existing production lines, these products can be fully food-grade and are ready for harsh wash-down environments.

Equipment at the stand will include:

  • SENSURE STARGATE SL-C – The vision system for the quality control of highly variable products on a single line (for example, before the horizontal flow pack)
  • SENSURE SYNAPSE ANALYTICS – The software suite offers three different modules designed to provide customized historical reports, display and monitoring of live measured values, and real-time operating procedures, as well as support for continuous improvement activities through data analytics aimed at reducing waste, increasing plant efficiency, and improving process performance.