COGNEX In-Sight SnAPP

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The In-Sight SnAPP vision sensor from Cognex rapidly automates quality control and supports production processes without requiring technical expertise. Leveraging pre-trained AI and an intuitive user interface, it can handle various inspection applications, improving accuracy and reducing errors through accurate detection and variation management.

Description + Details

The sensor In-Sight SnAPP represents an advanced and versatile solution for automating detection and quality control operations in industrial settings. Through the use of pre-trained artificial intelligence and image-based analysis, it is capable of handling complex tasks such as anomaly detection, false positive/negative reduction, and detection of features or parts in different locations. These sensors are ideal for improving product quality and optimizing machinery performance.

The system also offers quick and intuitive configuration via a web interface, eliminating the need to install specific software. The device can be connected and managed remotely, making installation easy even for inexperienced users and easily adapting new production applications without having to replace hardware. Sensor flexibility allows the replacement of various sensing tools, such as photocells or laser sensors, with a single advanced vision solution, facilitating integration and predictive maintenance.

Main Technical Specifications:

  • Weight: 141 g (6.2 mm lens); 169 g (16 mm lens); 50 g added for right-angle configuration
  • Power supply: 24 V DC ±10%, or USB 5V (power consumption ≤7.5W)
  • Operating Temperature: 0 to 40 °C; IP67 rating for dust and water resistance
  • Storage Capacity: supports up to 20 different applications
  • Resolution and Image Sensor: 1/2.8″ CMOS with 2.8 μm pixels, resolution of 1440 x 1080 (1.6 MP)
  • Connectivity: 10/100/1000 full/half duplex Ethernet for fast and stable connection

 

The In-Sight SnAPP lends itself to expanding automation possibilities in quality control, contributing to production line efficiency and minimizing manual errors.

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