Aliqua laboris adipisicing consectetur ex sunt duis eu non id aliqua sit duis. Ipsum minim ullamco exercitation est…

Get In Touch

Vision systems in practice

Industry 4.0 shows great potential to increase productivity, improve product quality, reduce operating costs or increase production flexibility.  Machine vision will be a key element of automation systems in Industry 4.0. Data available through vision equipment will be used to identify and mark defective products, understand their shortcomings and enable fast and effective intervention in the Industry 4.0 factory.
In order to introduce ATH students to the potential of vision systems, a unique Cognex vision systems laboratory was established in collaboration with the Engineering Centre. 
A project that, by utilising advanced smart cameras, code readers and dedicated software, will test the practical application of vision systems. Students in the lab will have the opportunity to study deep learning solutions used in automated manufacturing to inspect and identify parts, detect defects, verify product assembly and direct robots. 
The deep learning smart camera workstation solves difficult industrial OCR applications, verifies assembly and detects random defects anywhere on the production line. In industry, this includes being able to verify the completeness of products in a package, the fill level of a liquid in a bottle, or verifying the orientation of a component in a feeder.

Handheld code readers, located at another of the stations, decode the most difficult direct part marks (DPM) and label-based codes. In practice, they enable fast and reliable code reading on high-speed lines, 100% traceability of automotive parts, and minimise package delivery downtime by reading codes at angles greater than 85 degrees. They are used in the medical, electronics and aerospace industries,
amongst others.

The capabilities of a vision system cooperating directly with an industrial robot is demonstrated by the bin-picking station. The camera provides information about changes in the position of the workpiece that the robot is to pick up from the assembly line. This type of application means that the parts picked by the robot do not have to be precisely positioned, they can appear randomly in the workspace. 
A unique vision system presented at another of the stands combines 3D laser displacement technology with an intelligent camera, making it possible to undertake inspections quickly, accurately and economically. In industry, examples of the systems use are such as checking for surface defects, for example, gouges or cracks in floorboards, to check the uniformity of food products or to detect the correct alignment of car doors and bodies.

Another station includes the Cognex Deep Learning defect detection tool, which is used in various industries, in the automotive industry, for example, to verify textiles for defects or in the food industry to distinguish types of chocolates based on their size, shape and surface characteristics. With the dedicated VisionPro software, students can perform a wide range of functions, from locating and inspecting geometric objects to identification, measurement and alignment.

One of the stations demonstrates the use of artificial intelligence algorithms to reliably detect complex features and objects and verify the correct assembly of parts and assemblies based on their location in a predefined layout. The software is ideal for finding anomalies on complex parts and surfaces, even in situations where the appearance of defects can be unpredictable. In practice, the system is used, for
example, to verify the final assembly of cars, detect quality defects, correct the types of components used or check the completeness of packaging.

The lab also includes an ISO/IEC-compliant code quality checker – ideal for verifying codes, whether they are one- or two-dimensional, recessed or otherwise, difficult for other verifiers to access. The software will automatically detect the laser positioning pointer for perfect focus. Comprehensive diagnostic information that goes far beyond simply indicating barcode data informs users of quality issues and provides insight into any quality problems that need attention.

ATHLab is another example of university-industry collaboration. With test benches with cameras, as well as data processing equipment and analysis systems, together with instructional videos available online, students have the opportunity to see in practice the applications of vision systems in industry. Doing research, testing and analysis yourself is the best way to gain experience in the technical industry.

In the photos: members of the “Inżynier XXI wieku”.

Source (text/pictures):