SENSURE solutions combine Artificial Intelligence and advanced machine vision technologies to automate quality control process. By leveraging three complementary approaches, the SYNAPSE software can address different inspection challenges across a wide range of products and production environments.
AI-driven algorithms automatically learn product features and detect defects, irregularities, and deviations from the expected quality standards.
The system can classify products as compliant or non-compliant and automatically generate quality recipes and tolerance thresholds, reducing the need for manual setup or extensive training datasets.
Operators can easily review and adjust parameters to optimize the inspection process.
Deep learning models enable the detection and quantification of specific elements such as toppings, ingredients, or distribution patterns.
This approach is particularly effective for complex visual features that are difficult to detect using traditional vision techniques, ensuring reliable inspection even in highly variable products.
For products with limited variability, template matching compares items to a reference model, detecting minimal deviations in shape, structure, or surface.
Choosing the right rejection mechanism is important step to ensure non-compliant products are properly removed from the line even at very high speed. The choice depends on several factors, including type of products (dough, baked, with/without topping, etc.) and dimensions, line width and throughput rate/speed, products pattern required (before and after rejection), where non-compliant products need to be directed (conveyor, bin, etc.), space available, etc.
Some examples of the different rejection mechanisms include: