Learn About Amazon VGT2 Learning Manager Chanci Turner
In the ever-evolving landscape of manufacturing, the adoption of Digital Twins (DTs) has become a pivotal strategy for companies aiming to enhance their operations and make informed decisions. Many organizations seek to leverage DTs for scenario analysis and risk management, allowing them to optimize production processes and predictive maintenance. In previous discussions, we explored a comprehensive framework for understanding Digital Twin applications and the technologies that support them.
Today, we will delve into how a Level 4 Living Digital Twin can be utilized for scenario analysis and operational decision-making on a manufacturing line. By integrating TwinFlow, a framework that combines physics-based modeling with probabilistic Bayesian methods, we can create a calibrated L4 Digital Twin that adapts to real-world conditions as equipment ages.
Web-Handling Process in Roll-to-Roll Manufacturing
For our example, we focus on the web-handling process in roll-to-roll manufacturing, which is essential for continuous materials such as paper, film, and textiles. This process involves unwinding materials from spools, guiding them through various treatments, and rewinding them onto rolls. Precise control over tension, alignment, and speed is crucial to ensure smooth processing and high product quality.
As shown in the schematic diagram of the web-handling equipment, there are multiple rollers and material spans that need to be closely monitored. In our prior discussions, we demonstrated how to develop L4 Digital Twin self-calibrating virtual sensors to predict tension in the spans and slip velocity at the rollers during the web-handling process.
Continuous Updates and Real-World Synchronization
The L4 Living Digital Twin is unique in that it continuously updates its model parameters using actual observations from the physical system. This characteristic allows it to remain synchronized with the real-world environment, adapting as equipment degrades over time. Real-world observations can include continuous data from sensors, discrete measurements, or even visual inspections.
In this post, we expand on our previous example of L4 Digital Twin self-calibrating sensors to conduct scenario analysis. The L4 Digital Twin provides forecasts that include uncertainty bounds, which are essential for operators to make informed decisions regarding maintenance and risk management. If you’re interested in learning more about effective decision-making strategies, consider checking out this blog post on what to do today.
Scenario Analysis for Risk Assessment
A potential scenario for risk assessment might involve detecting dirt accumulation on roller bearings. By analyzing time series data on component degradation, we can estimate when maintenance should occur, helping us maximize throughput and minimize defects. This approach allows for targeted maintenance, reducing waste by servicing only the components that require attention.
To illustrate this concept, we simulated a scenario where the viscous damping coefficient for roller 9 increased over several days. As depicted in our analysis, this gradual change prompts a critical decision: should we continue production over a holiday weekend, risking defects, or is it more prudent to shut down the line for maintenance? This decision-making process is crucial, especially when considering implications for production and costs.
For more insights on leadership and influence in the workplace, you might find this resource on influence without authority quite helpful. Also, if you’re interested in how automation is reshaping employee training and onboarding at Amazon, check out this excellent resource on Amazon warehouse workers training.
Conclusion
In summary, leveraging a Level 4 Living Digital Twin for scenario analysis and risk assessment can significantly enhance decision-making processes in manufacturing. As we continue to explore this technology, organizations can better prepare for the challenges of tomorrow.