Machine vision for cutting equipment

Client info:

The world’s leading manufacturer of multifunctional digital equipment for cutting (flatbed cutting plotters)

Task:

Development of a machine vision system that automates object recognition on printed materials to improve speed, accuracy, and efficiency

Headquarters:

Altstetten (Switzerland)

Zünd is a Swiss company specializing in high-precision cutting systems. With a focus on innovation and automation, Zünd provides cutting solutions for a wide range of materials, including textiles, leather, composites, wood, and cardboard. Their cutting systems are widely used across industries such as packaging, graphics, automotive, and textiles, making them a leader in automated cutting technology.

Zünd approached the Volya team to develop a machine vision system for their cutting equipment.

Challenges & solutions

A system is needed to automatically recognize marked cutting objects on printing (print) material using a high-resolution camera positioned above the plotter’s work surface.

Purpose of the system:

The software automates the production, minimizing the necessary manual operations thus significantly speeding up the cutting process. Reduces the wear of the tool mechanisms by eliminating the need to search for registration marks on the camera on the cutting mechanism.
Volya team has developed highly effective algorithms for cutting equipment machine vision utilizing machine learning methods.

How does it work?

Intelligent recognition algorithms are able to reconstruct missing registration marks, if for example, covered by a casing, and also to adapt to changes in recognition conditions (illumination and shadows, material shape, quality of printing).
Autonomous work is possible even in the absence of preset label templates, as well as in the absence of labels in general, if the material is placed with polygraphy down. In such situations, the recognition of the edges of the material is used.

Efficiency of the system

Technologies used

Programming language: C / C ++. The graphical user interface (GUI) was created using Qt Framework. Calibration is based on OpenCV algorithms. As an auxiliary algorithm for jobs identification Coherent Point Drift (CPD) was used. The project also used the Eigen library (library of linear algebra for C ++), Inter Process Communications (IPC), Component Object Model Technologies (COM).

Feedback from client

Andreas Grüter - Software Engineer

“ The project is about machine vision. Based on images of a high resolution camera, the position of objects is determined fast and accurate by detecting register marks in the image. Working together with the young developers of the Volya team is straightforward, fast and cooperative. Our ideas and wishes are fulfilled and Volya developers come up with proactive solutions. Especially
I want to note the short reaction time to our requests. “