The scalable Q1250 uses machine learning for lead end reconstruction, significantly enlarging the application range and improving detection efficiency.

The application range and efficiency of quality monitoring with the scalable Q1250 have been significantly increased through machine learning. The BASIC unit automatically monitors whether the stripping has worked and whether the seal and crimp are present. The device is scalable on demand: Should your requirements change, the Q1250 can be expanded with additional licenses and/or a dome light. This means that by integrating just one tool, you can conduct comprehensive quality testing and respond to future requirements without major changeovers. Parameter control is fully automated during operation, independent of the user. Visual inspection and costly retooling are no longer necessary, resulting in considerable time savings and improved quality.

Full Integration into Cable Production Machines

Full machine integration cuts manual checks and changeover time. Production data with quality parameters steers the process, automating control and optimizing efficiency.

Operator-independent Quality Control

Pre-define limits by parameters for automatic quality observation during production. This unburdens the operator’s quality check, increasing efficiency and minimizing human errors.

Comprehensive Quality Control with Integrated Machine Learning

The Q1250 scalable system checks all common quality characteristics. Its lead end reconstruction, based on a machine-learned network, boosts adaptability, efficiency, and cuts scrap.