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