Our solution at the Digital Capability Center Venice
We have implemented an AI-powered machine vision solution based on the United Manufacturing Hub data infrastructure at the Digital...
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Our Use Cases
Use our platform to build AI vision-based factory applications without any coding to significantly improve your productivity.
How it works
Increase your asset productivity
by up to 20%
Reduce maintenance costs
by up to 10%
Increase defect detection rate
by up to 90%
Increase your asset productivity
by up to 20%
Reduce maintenance costs
by up to 10%
Increase defect detection rate
by up to 90%
Keen to see how AI can help your business?
Success stories
Discover selected projects from our installations across industries that show how our solution is used in practice.
Multi-Sensor Anomaly Detection
By converting sound, vibration, and temperature data into hyperspectorgrams, combined with the more advanced AI-Vision algorithms, very high accuracies can be achieved in the detection and identification of maintenance needs. With this predictive approach, downtimes can be decreased.
for predictive maintenance in textile industry
Work Safety Inspection
A potentially hazardous but unfenced, human-robot collaboration area is permanently supervised by a camera. An artificial neural network is trained to identify the presence of persons and the absence of personal safety equipment. Eventually, warnings are displayed on a dashboard screen and signal light.
of an assembly cell in electronics Industry
In-Line Quality Inspection
Using Convolutional Neural Networks (CNN) the in-line quality inspection solution detects different surface error categories (e.g., cracks, colors, scratches). By this, the inspection task will be fully automated.
of sealings in metals industry
News
Keen to find out how AI can help your business?
Phase 1
Explore
Gather an overview of the most exciting use cases of AI and evaluate whether your use case is suitable for AI-powered Machine Vision
Phase 2
Define
Get an proposal for an application-specific hard- and software concept for your AI solution.
Phase 3
Build
Train top-notch machine learning models with no code on the world’s leading training provider
Phase 4
Deploy
Embed and update your model in the ANTICIPATE HUB with one click
Outcome
ANTICIPATE INSPECT
Bring your company up to speed with customized workshops and self-learning opportunities
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Independent
No vendor lock-in effect for hardware and software
End-to-End
Support of the full implementation process
Tailored
Tailored Hardware setup for specific industry problems
No Code
AI specialists are limited and very expensive
Secure
Meets the highest data protection standards
Flexible
Flexible deployment options from on-premise to cloud
Scalable
Horizontal scaling, through microservice architecture
Our Core Features
Run your own models on our industry graded operating software, which is designed to overcome the most critical hurdles of AI vision implementations in manufacturing.
Scalable
Horizontal scaling, through microservice architecture
Secure
Meets the highest data protection standards
Secure
Meets the highest data protection standards
Secure
Meets the highest data protection standards
Secure
Meets the highest data protection standards
Scalable
Horizontal scaling, through microservice architecture
Solution
We developed a no-code platform which enables engineers to realize AI vision solutions within days instead of months.
Phase 1
Explore
Gather an overview of the most exciting use cases of AI and evaluate whether your use case is suitable for AI-powered Machine Vision
Assembly Verification
With low-cost cameras, manual processes can be monitored with high precision using AI. Individual motions are detected and compared with the target process in order to validate the process sequence and measure cycle times.
of a Packaging Process in Consumer Electronics Industry
BUILD YOUR OWN INDUSTRIAL
AI VISION SOLUTIONS
Scalable
Horizontal scaling, through microservice architecture
Flexible
Flexible deployment options from on-premise to cloud
Secure
Meets the highest data protection standards
Automate expensive expert-related repetitive work by analyzing sensor and camera data automatically with AI.
Multi-Sensor
Anomaly Detection
Automate expensive expert-related repetitive work by analyzing sensor and camera data automatically with AI.
Situation
In hazardous, open-plan shopfloors, measures must be taken to prevent accidental misbehavior regar-ding PSE (personal safety equipment). A human-based supervision regarding PSE usage is very resource intensive.
Solution
A potentially hazardous but unfenced, human-robot collaboration area is permanently supervised by a camera. An artificial neural network is trained to identify the presence of persons and the absence of personal safety equipment. Eventually, warnings are displayed on a dashboard screen and signal light.
Result
10% reduction of time losses by interruptions caused by accidents due to missing protective equipment
15% less injuries and thus significantly higher safety on your shopfloor due to early safety risk detection.
Situation
Currently, inspection processes in manufacturing require a lot of manual intervention and experience to achieve good quality in reasonable time. On the market is no solution to continuously control the products quality presently.
Solution
Using Convolutional Neural Networks (CNN) the in-line quality inspection solution detects different surface error categories (e.g., cracks, colors, scratches). By this the inspection task will be fully automated.
Result
10% increased yield, as defective parts are detected with a higher accuracy.
20% reduced operating time for manual inspection in assembly process.
Situation
Errors made in manual processes, e.g., during assembly, are usually discovered late in the process or even by the customer, which leads to customer dissatisfaction, high costs for error correction and customer service.
Solution
With low-cost cameras, manual processes can be monitored with high precision using AI. Individual motions are detected and compared with the target process in order to validate the process sequence and measure cycle times.
Result
20% operating cost reduction through early fault detection, since error correction is more expensive in the later process.
40% reduction of customer return rate of finished products due to error prevention in the manual manufacturing processes.
Situation
Long downtimes due to unprepared changeover processes lead to high costs due to a reduced throughput rate and are a problem in the manufacturing industry.
Solution
By converting sound, vibration and temperature data into hyperspectorgrams, combined with the more advanced AI-Vision algorithms, very high accuracies can be achieved in the detection and identification of maintenance needs. With this predictive approach downtimes can be decreased.
Result
45% reduction in downtime. As maintenance needs are known and actions can be prepared in beforehand.
30% reduction in maintenance costs, due to faster identification of maintenance root causes and more efficient actions.
Automate expensive expert-related repetitive work by analyzing sensor and camera data automatically with AI.
Work Safety
Recognition
Detection of Personal Protective Equipment (PPE) and dangerous situations increases the work safety of your employees.
Situation
In hazardous, open-plan shopfloors, measures must be taken to prevent accidental misbehavior regar-ding PSE (personal safety equipment). A human-based supervision regarding PSE usage is very resource intensive.
Solution
A potentially hazardous but unfenced, human-robot collaboration area is permanently supervised by a camera. An artificial neural network is trained to identify the presence of persons and the absence of personal safety equipment. Eventually, warnings are displayed on a dashboard screen and signal light.
Result
10% reduction of time losses by interruptions caused by accidents due to missing protective equipment
15% less injuries and thus significantly higher safety on your shopfloor due to early safety risk detection.
Situation
Currently, inspection processes in manufacturing require a lot of manual intervention and experience to achieve good quality in reasonable time. On the market is no solution to continuously control the products quality presently.
Solution
Using Convolutional Neural Networks (CNN) the in-line quality inspection solution detects different surface error categories (e.g., cracks, colors, scratches). By this the inspection task will be fully automated.
Result
10% increased yield, as defective parts are detected with a higher accuracy.
20% reduced operating time for manual inspection in assembly process.
Situation
Errors made in manual processes, e.g., during assembly, are usually discovered late in the process or even by the customer, which leads to customer dissatisfaction, high costs for error correction and customer service.
Solution
With low-cost cameras, manual processes can be monitored with high precision using AI. Individual motions are detected and compared with the target process in order to validate the process sequence and measure cycle times.
Result
20% operating cost reduction through early fault detection, since error correction is more expensive in the later process.
40% reduction of customer return rate of finished products due to error prevention in the manual manufacturing processes.
Situation
In hazardous, open-plan shopfloors, measures must be taken to prevent accidental misbehavior regar-ding PSE (personal safety equipment). A human-based supervision regarding PSE usage is very resource intensive.
Solution
A potentially hazardous but unfenced, human-robot collaboration area is permanently supervised by a camera. An artificial neural network is trained to identify the presence of persons and the absence of personal safety equipment. Eventually, warnings are displayed on a dashboard screen and signal light.
Result
10% reduction of time losses by interruptions caused by accidents due to missing protective equipment.
15% fewer injuries and thus significantly higher safety across your shopflor due to early safety risk detection.
Automate expensive expert-related repetitive work by analyzing sensor and camera data automatically with AI.
In-Line Quality
Inspection
Automated quality inspection and optimization increase throughput and decrease costs significantly.
Situation
In hazardous, open-plan shopfloors, measures must be taken to prevent accidental misbehavior regar-ding PSE (personal safety equipment). A human-based supervision regarding PSE usage is very resource intensive.
Solution
A potentially hazardous but unfenced, human-robot collaboration area is permanently supervised by a camera. An artificial neural network is trained to identify the presence of persons and the absence of personal safety equipment. Eventually, warnings are displayed on a dashboard screen and signal light.
Result
10% reduction of time losses by interruptions caused by accidents due to missing protective equipment
15% less injuries and thus significantly higher safety on your shopfloor due to early safety risk detection.
Situation
Currently, inspection processes in manufacturing require a lot of manual intervention and experience to achieve good quality in reasonable time. On the market is no solution to continuously control the products quality presently.
Solution
Using Convolutional Neural Networks (CNN) the in-line quality inspection solution detects different surface error categories (e.g., cracks, colors, scratches). By this the inspection task will be fully automated.
Result
10% increased yield, as defective parts are detected with a higher accuracy.
20% reduced operating time for manual inspection in assembly process.
Situation
Errors made in manual processes, e.g., during assembly, are usually discovered late in the process or even by the customer, which leads to customer dissatisfaction, high costs for error correction and customer service.
Solution
With low-cost cameras, manual processes can be monitored with high precision using AI. Individual motions are detected and compared with the target process in order to validate the process sequence and measure cycle times.
Result
20% operating cost reduction through early fault detection, since error correction is more expensive in the later process.
40% reduction of customer return rate of finished products due to error prevention in the manual manufacturing processes.
Situation
Currently, inspection processes in manufacturing require a lot of manual intervention and experience to achieve good quality in reasonable time. On the market is no solution to continuously control the products quality presently.
Solution
Using Convolutional Neural Networks (CNN) the in-line quality inspection solution detects different surface error categories (e.g., cracks, colors, scratches). By this the inspection task will be fully automated.
Result
10% increased yield, as defective parts are detected with a higher accuracy.
20% reduced operating time for manual inspection in assembly process.
Automate expensive expert-related repetitive work by analyzing sensor and camera data automatically with AI.
Assembly
Verification
Inspection of manual assembly tasks increases transparency about cycle times and detects errors early in the process.
Situation
In hazardous, open-plan shopfloors, measures must be taken to prevent accidental misbehavior regar-ding PSE (personal safety equipment). A human-based supervision regarding PSE usage is very resource intensive.
Solution
A potentially hazardous but unfenced, human-robot collaboration area is permanently supervised by a camera. An artificial neural network is trained to identify the presence of persons and the absence of personal safety equipment. Eventually, warnings are displayed on a dashboard screen and signal light.
Result
10% reduction of time losses by interruptions caused by accidents due to missing protective equipment
15% less injuries and thus significantly higher safety on your shopfloor due to early safety risk detection.
Situation
Currently, inspection processes in manufacturing require a lot of manual intervention and experience to achieve good quality in reasonable time. On the market is no solution to continuously control the products quality presently.
Solution
Using Convolutional Neural Networks (CNN) the in-line quality inspection solution detects different surface error categories (e.g., cracks, colors, scratches). By this the inspection task will be fully automated.
Result
10% increased yield, as defective parts are detected with a higher accuracy.
20% reduced operating time for manual inspection in assembly process.
Situation
Errors made in manual processes, e.g., during assembly, are usually discovered late in the process or even by the customer, which leads to customer dissatisfaction, high costs for error correction and customer service.
Solution
With low-cost cameras, manual processes can be monitored with high precision using AI. Individual motions are detected and compared with the target process in order to validate the process sequence and measure cycle times.
Result
20% operating cost reduction through early fault detection, since error correction is more expensive in the later process.
40% reduction of customer return rate of finished products due to error prevention in the manual manufacturing processes.
Situation
Errors made in manual processes, e.g., during assembly, are usually discovered late in the process or even by the customer, which leads to customer dissatisfaction, high costs for error correction and customer service.
Solution
With low-cost cameras, manual processes can be monitored with high precision using AI. Individual motions are detected and compared with the target process in order to validate the process sequence and measure cycle times.
Result
20% operating cost reduction through early fault detection, since error correction is more expensive in the later process.
40% reduction of customer return rate of finished products due to error prevention in the manual manufacturing processes.
Automate expensive expert-related repetitive work by analyzing sensor and camera data automatically with AI.
Multi-Sensor
Anomaly Detection
Work Safety
Recognition
In-Line Quality
Inspection
Assembly
Verification
AI-Powered Automation Speeds Up Time to Production
With our platform, you can explore the right use cases, define a tailored hardware concept, build your own AI models and deploy the model in your factory.
Your AI vision solutions are made easy to operationalize at the proper scale for load and number of models all within one, no-code, easy to manage platform.
End-to-End
Support of the full implementation process
Tailored
Tailored hardware configuration for specific industry problems
End-to-End
Support of the full implementation process
End-to-End
Support of the full implementation process
Secure
Meet the highest data protection standards
Flexible
Flexible deployment options from on-premise to cloud
End-to-End
Support of the full implementation process