Machine Learning
Transforming manufacturing with cutting-edge machine learning technology
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Transforming manufacturing with cutting-edge machine learning technology
Your first step to digitalization; capture and analyze your data, then get actionable insights
Reduce your downtime by a minimum of 15% using the power of AI
Automate your QC line by integrating AI-powered computer vision systems
Automated and flawless precision and consistency for your dimension checks
Eliminate the need for inconsistent and labor-intensive manual QC
Optimize the performance of your energy systems and lower your energy bills
Active monitoring is the first step to digitalization, as it provides the foundation for capturing and analyzing data in real-time. Active monitoring can visualize different aspects of machines and processes, including OEE, potential machine failures, and root-cause analysis.
Time Range: Live, Last Shift | Week | Month | Year
A critical motor failed. Millions of dollars in lost revenue. You don’t want it to happen under your watch, and predictive maintenance is the solution. It is the solution to any of your problems that involves someone having to watch over the machine or machine metrics, making sure nothing goes wrong.
Predictive, prescriptive, and preventive maintenance are different methods to reduce downtime in machines, by identifying assets that are most likely to fail and determining the best schedule for maintenance.
Time Range: Live, Shift | Week | Month | Year Ahead
AI-powered visual inspection systems can be seamlessly integrated into production lines to detect defects, product mismatches, check product dimensions, and provide 24/7 monitoring with consistent accuracy and without fatigue, enhancing the quality control process, reducing human error, and eliminating the need for manual inspections.
Take your manufacturing processes to the next level with cutting-edge machine learning algorithms and advanced computer vision technology, experience flawless precision and consistency with state-of-the-art cameras and sensors that effortlessly pinpoint deviations from exact specifications, catching inconsistencies in real-time production.
Machine learning powered computer vision systems have revolutionized defect detection in manufacturing, offering a game-changing advantage that eliminates the need for inconsistent and labor-intensive manual quality control. These systems are capable of tirelessly analyzing every image and every detail in the image, identifying the most subtle imperfections, while delivering unmatched speed and precision.
If you have a machine with frequent visible failures, you could benefit from a visual system that monitors your machine continuously and alarms your staff if it predicts an issue is about to occur. An example of this is monitoring calenders during the spinning process of fiber production. Fiber wrapping is a common problem in the pulp and paper industry, often caused by quality issues in the fiber itself. A deep-learning-based vision system can monitor the calenders in production and alert technicians before fiber wrapping occurs.
With the power of machine learning, enormous amounts of data from sensors and other resources can be analyzed, and the driven insights can help reduce costs and improve efficiency in ways that are impossible without machine learning algorithms. Unlike traditional programming, machine learning algorithms can extract patterns from historians and real-time data such as sensor data without explicit programming.
Bridge Automation specializes in data gathering, and seamlessly integrating cutting-edge machine learning models into your existing infrastructure. We will work with you to gather the required data for model development (or use the existing historical data you’ve collected), and conduct data analysis to gain a deep understanding of your data. Then, our team of machine learning engineers will train a machine learning model on provided data and finally, deploy and integrate it with your current infrastructure, minimizing your hardware costs and need for adjustments.
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