Visualizing Data for Improved Repeatability
Problem: Roll manufacturing is a unique process due to the geometry of a wound roll making it difficult to analyze the wind properties of a roll. Due to the number of variables involved in manufacturing a roll, it has been extremely difficult to perform root cause analysis of roll properties and performance as these are not available until a roll unwinds.
Roll manufacturing has tended to be a craft-based industry with an aging work force. As the current operators are retiring, the knowledge is going out the door with the operator. There is a need to capture the craft knowledge in a context-driven, searchable electronic format for reference and training purposes.
Solution: RollDVR allows virtual replay of the roll wind in, and in reverse mode, visualization of how the roll will wind out. RollDVR provides deep-drill click visualization and analysis of roll manufacturing wind-in and wind-out properties. The project allows roll build replay with direct association of machine process points, recipe settings, and time series charting using a slider on a scaled vector graphic to select point-of-time for display. Through the use of a best-fit algorithm, roll-to-roll process point comparison is available to assist in root cause analysis.
The RollDVR recipe editor provides the ability to tune the recipe in context of the point-in-time manufacturing issue presented in the visualizer. Additionally, the built-in Knowledge Base Collection Manager creates a means to annotate analysis and create a searchable contextual repository for training and reference purposes.
Benefits: The user is able to use logic in context to determine the manufacturing properties of a roll. There is no more guessing, and rolls are replayed and analyzed prior to downstream processes, creating great efficiency due to quantifiable parameters such as run-ability. Additional benefits include upstream process traceability and chain-of-custody management. The knowledge base has proved to be a valuable item for bringing science and repeatability to what was once a craft-based process.
- 400 tags per machine (typical installation is five or more machines)
- Approximately 15 screens (varies according to Overbridge module)
- More than 20 clients open at any given time, typically
- Usually more than 100 alarms (in two categories: process alarms and recipe-deviation alarms)
- Usually 3 devices per machine
- Standard Architecture, Redundant
- Usually 50 historical data points per machine
- SQL Server and Oracle databases
Mark is Principal at Overbridge. He has developed roll substrate manufacturing monitoring systems for more than 30 years. He has been using Ignition since 2010.Created By:
Overbridge was founded in 2005, and it develops tightly integrated systems for monitoring roll manufacturing.
Roll Based Converting