In industrial production processes, conventional vision systems reach their limits in certain tasks that are not a problem for humans.
Despite the limitations and fluctuations of human sensory perception, human judgment is still the best solution here.
The solution: Intelligent image processing systems with self-learning algorithms – deep learning.
Our Deep Learning technology area uses leading technologies based on state-of-the-art machine learning algorithms (machine learning or deep learning).
This makes it possible to overcome challenges such as …
Data volume too large to scale
Tasks too complex to program
Non-existent or limited laws and rules
Irregular or unknown deviations from the norm
Reliable identification of natural products where there are no identical parts, only unique ones
Our DL technologies enable three basic “human” tasks to be solved fully automatically and highly efficiently:
Discover all kinds of qualitative anomalies in your components. For example, scratches on difficult or textured backgrounds, faulty assembly or weaving defects in textiles.
Our HMV technologies identify these and many other anomalies on the basis of an expected image that is learned using the permissible appearance of an object, including tolerated tolerances.
Detect and identify individual or multiple features in an image.
For example, highly deformed characters on a noisy background or complex objects in bulk.
Our HMV technologies locate and identify complex features and objects by learning from labeled images on which the target features are marked.
Classify objects or an entire scene.
For example, identify objects based on their appearance or packaging.
Or classify acceptable and unacceptable defects.
Our HMV technologies learn to reliably distinguish between the different classes based on labeled images.
A key feature of deep learning is a self-learning system (deep learning) based on representative sample images.
This significantly improves recognition performance compared to conventional industrial image processing methods.
In addition, the development time and time-to-market of DL solutions are considerably shortened – with significant cost benefits.
Feasibility studies can also be carried out in hours instead of days.
Images of good samples are used to train a so-called expected image (permissible appearance, including tolerated and permissible tolerances).
The imaging source is irrelevant.
Images of the test specimens are evaluated against the expected image in the series test.
This means
Here you will find a selection of possible applications for our DL technologies:
Tasks and challenges in the inspection of machined workpieces
Screws
Inspection of quality defects (scratches, nicks, pressure marks, stains)
To inspect this type of screw, it is rotated around the horizontal axis.
The images are captured and processed directly in the HMV system.
The model used for processing and evaluation is based on a representative selection of images without defects, but with the permissible manufacturing scatter.
The model takes into account acceptable variations in surface texture and the very complex shape of the self-tapping screw.
After training, the system is ready for the test.
Errors on the test specimens can be identified and marked very quickly and reliably.
Carbide tools
Inspection of wear, damage or breakage (chipping)
To inspect carbide tools, they are rotated around the horizontal axis.
The images are recorded and processed and evaluated directly in the HMV system.
The model used for processing and evaluation is based on images of OK samples.
The model takes into account acceptable variations in surface texture and the very complex shape of the tool.
After training, the system is ready for the test.
Defects on the test specimens can be identified and marked very quickly and reliably.
As the model is trained on the basis of “good patterns”, unknown (new) defects are also recognized.
Tasks and challenges in the inspection of pad prints
Dials
Inspection of the quality of the print (faulty, incomplete)
To inspect dials, images are taken and processed directly in the HMV system.
The model used for processing and evaluation is based on a representative selection of images of OK samples.
The model takes into account acceptable variations in print or surface texture as they occur in the selection of OK samples.
After training, the system is ready for testing.
Errors are quickly and reliably identified and marked.
Button
Inspection of the quality of the print (faulty, incomplete)
Images are captured and processed directly in the system to inspect buttons.
The underlying model for processing and evaluation is based on a representative selection of images of OK samples.
The model takes acceptable variations in pressure or surface texture into account.
After training, the system is ready for testing.
Defects are quickly and reliably identified and marked.
Tasks and challenges in the automatic inspection of electroluminescence images
Solar cells
Automatic inspection of solar cells using electroluminescence images (e.g. micro-cracks)
To inspect solar cells, electroluminescence images are captured and processed directly in the system.
The underlying model for processing is based on a representative selection of images without defects and on labeled images with defects.
Due to the highly structured or textured background, micro-cracks are the most difficult to detect.
The model learns to distinguish micro-cracks and other defects from similar appearing structures in the background.
After training, the system is ready for testing.
Errors on the cells can be identified and marked very quickly and reliably.
Tasks and challenges in the inspection of fabrics / woven fabrics / textiles
Textiles
To inspect textiles, images are captured and processed directly in the system.
The underlying model for processing is based on a representative selection of images of good samples.
These represent weaving patterns, yarn properties, color characteristics and tolerated imperfections.
The model learns to distinguish faults from textiles with similar structures.
After training, the system is ready for testing.
Defects on the textiles can be identified and marked very quickly and reliably.
Tasks and challenges in the inspection of textured surfaces
Textured surfaces
Inspection of textured surfaces (scratches, knocks, pressure marks, intolerable damage)
To inspect textured surfaces, images are captured and processed directly in the system.
The model used for processing and evaluation is based on a representative selection of images without defects and on labeled images with defects.
The model learns to distinguish defects from similar appearing structures of the surfaces.
After training, the system is ready for testing.
Defects on the textured surfaces can be identified and marked very quickly and reliably.
The aim is for participants to be able to independently adapt existing models to changes.
Contents:
Learn the basic options of the ViDi software (ViDi red, ViDi blue, ViDi green).
Customer-specific / application-specific topics, depending on the implementation
Duration: 2 days
Training location: i-mation Rottweil or at the customer’s premises
Number of participants: max. 3
Would you like to find out more about the possible applications and benefits of deep learning?
Just give us a call or send us an e-mail.
info@i-mation.de
Tel.
+49 741 942 286-00
i-mation GmbH
Neckartal industrial estate
Neckartal 250
78628 Rottweil
+49 741 942 286-00
+49 741 942 286-90
info@i-mation.de