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Predicting and preventing issues in roll-to-roll manufacturing using data analytics techniques
Aravind SeshadriRoll-2-Roll Technologies LLC2017 AIMCAL R2R Conference
What is Data Analytics?Data := collect ion of facts/observat ions
Analytics := science of creating meaningful insights from raw data
Data analytics involves the organization and analysis of data to draw conclusions from identified patterns.
BenefitsBenefits of data analytics for roll-to-roll manufacturing include:
➔ Product quality assurance
➔ Process defect detection
➔ Supplier defect tracking
➔ Maximizing yield
➔ Failure and downtime prediction
So why data analytics is NOT prevalent for roll-to-roll manufacturing?
Note
It is not just roll-to-roll manufacturing but pretty much any type of manufacturing.
VolumeVarietyVelocityVariabilityVeracity
Note
These are the challenges for any organization to adopt data analytics.
DATA
So how much would it COSTto implement data analytics for your operation?
It depends on application.● Data capture and preparation
● Cost of experts (Data Scientists)
● Cost of infrastructure to deploy and monitor
So how do we START?
Tip
Take baby steps
Five Key StepsThe key steps to implement a data analytics system:
➔ Understand and prepare data
➔ Create a Model
➔ Evaluate the Model
➔ Deploy the Model
➔ Measure and monitor effectiveness
Keys to success
Have clear objectives
Such as specific product quality assurance metrics or specific downtime prediction metrics.
Start small
Don’t collect all the data in your organization to make sense of it.
And know your data.
Build on iterations
Build on the early success to expand the scope of data analytics.
What can data analytics dofor roll-to-roll applications?● Identify web material issues
● Identify web process anomalies
● Identify machine issues
Data from sensors● Edge sensors
● Tension load cells
● Speeds from encoders
● Temperatures/humidity
● Other process specific sensors
● High level yield information
Models to predict● Anomaly detect ion
● Pattern recognit ion
An anomaly is a deviation from normal behavior.
Under normal conditions the sensor measurements is a gaussian distribution.
Anomaly patterns from sensor measurements can be used to build a model for relatable defect or issue.
Application Example:Data Analytics Using Edge Sensor Measurement
Experiments
● Sinusoidal disturbance
● Web flut ter
● Web splice
● Wrinkles
Collected 20 different measurement from two sensors
20 ms sampling rate
Normal Condition
Normal Condition
Sinusoidal Disturbance
Sinusoidal Disturbance
Flutter
Flutter
Splice
Splice
Wrinkle
Wrinkle
Next step is to automate the anomaly detection and pattern recognition for real-time application.
KOIOS
Data analytics platform for proactive control.
Additional higher level meaningful insights can be generated by combining additional data and pattern recognition.
AcknowledgementsDr. Carlo Branca