NeuroCheck as a Platform
The Basis for your Deep Learning Application
Deep learning is becoming an increasingly important technology in the manufacturing industry. That‘s why NeuroCheck has been providing in-house developed backpropagation networks for image content and object feature classification for over 25 years. The necessary neural network structures have been an integral part of the NeuroCheck image processing software since 1994. With increasing computing power, cloud computing and fast Internet connections, new possibilities for the use of Deep Learning in industrial vision open up. Hard-to-solve inspection tasks become feasible.
NeuroCheck AI-Xtension gives the user the opportunity to use his own deep learning know-how productively. Self-developed and trained models can be quickly and easily integrated into production processes.
Neural networks can be integrated directly into NeuroCheck in the TensorFlow and ONNX format. Other model formats such as CNTK, Pytorch, Keras, Watson, and others are also possible. As a basis for your deep learning application, process integration (Profibus, Profinet, EthernetIP etc.), hardware support (2D, 3D cameras), and data acquisition (image data, XML, data base) have already been part of the standard functionality of NeuroCheck for a long time.
Main areas of application » Successful use when it really makes sense
Sample application » Inspect a rubber sleeve » Pseudo error rate reduced many times over
As part of an automated production step, a rubber sleeve is affixed to a carrier. This can cause pinches and back folding, which in turn may compromise leak tightness and thus the proper working of the assembly group. The challenging part of this inspection task is the high variety of fault appearances that can occur and that it is impossible to establish a quantitative classification of the faults. In addition, the component images differ in size, position, contrast, and brightness due to the production process. Traditional image procession produces a high rate of false alarms with an additional high risk for errors going undetected.
Inspection Rubber Sleeve » Correct » OK
Inspection Rubber Sleeve » Incorrect » NOK
Further examples of error patterns
Example Application » Identification and Localization of Screws
Neural Network solves Inspection Task
To control an automated screwing process, precise identification and localization of the screws is necessary. The station processes a high number of various assembly groups with varying numbers of screws, surfaces and geometries. In addition, the screws may be slightly covered or absent, which must lead to a termination of the screwing process. With this application as well, a number of complex environmental conditions need to be dealt with. The example images show various levels of brightness, various sizes of assembly parts, positions and levels of focus.
Variations in brightness because of overexposure
Example of another type
Recognition of screw despite soiling
Screw not completely visible
Screws of varying number and size
Traditional industrial vision processes require the creation and adjustment of special inspection programs for each assembly type. The high number of variants made this approach very time-consuming and entailed maintenance difficulties.
By using a neural network, the entire task was solved with a single program. In addition, robustness of recognition under changing environmental conditions was greatly improved compared with the traditional process.
As your competent partner, we look forward to be of assistance
Our work in this area is based on deep learning and machine learning algorithms and ranges from basic research to industrial knowledge transfer to the implementation of deep-learning systems.
We look forward to hearing from you
If you have any questions or require additional information regarding deep learning in industrial vision, do not hesitate to contact us using the contact form, via e-mail or phone +49 7146 8956-0. Our experienced experts will give competent and comprehensive advice.
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