Machine Learning for Auto

Optimization

What is Machine Learning?

Definition:

“Machine learning refers to any system where the performance of a machine in performing a task improves by gaining more experience in performing that task”.

Experience refers to the data that we fed in to the algorithm and improvements refers to it outputwhich is considered as an action.

ML is intelligence acquired by a machine, which is similar to human natural intelligence.

ML use existing data to forecast future behaviors, outcomes, and trends.

ML involves using statistical / mathematical techniques.

Examples of Machine Learning

A computer program is said to learn from experience E with respect to task T and performance gauge P.

Optical Character Recognition: categorize images of hand written characters by the lettersrepresented.

Face detection: Find faces in Image.

Spam Filtering: identify email messages as spam or non spam.

ML Algorithm

Performance measuring(P)

Experiences (E)

Task (T)Traffic pattern(T)

Future Traffic pattern(P)

Historic Traffic pattern(E)

Applying Machine Learning to CNC Machines

Performance Improvement using Machine

Learning:

Thermal Displacement Compensation

Automatic Servo Tuning

Adaptive Control for optimizing cycle time.

Learning control for achieving high performance

machining.

Inertia Estimation, for higher acceleration to

reduce cycle time.

Smart Program Analysis – Acc/dec decided

dynamically

Preventive Maintenance using Machine

Learning:

Prediction of Failures

Data Analysis using AI - Pattern Analysis/

Waveform Analysis

Minimizing Downtime using AI

Thermal Displacement Compensation

Conventional Method

It is not easy to derive the relationship between temperature and displacement

necessary for thermal displacement compensation

：Temperature sensor

：Displacement sensor

Temp.

Disp.

Analysis, Formulation

Heat transfer analysis

Thermal fluid analysis.

etc.

Data Collection

Software development

Thermal Displacement Compensation

Using machine learning

Machine learning can derive the relations from the data of temperature and

displacement and can create thermal displacement model.

Model Development

Software

：Temperature sensor：Displacement sensor

Machine Learning

TDCModel

ThermalDisplacementCompensation

Thermal Displacement Compensation optionModel development tool

LearningData

DataCollectionSoftware

Temp.

Disp.

Temp.

Comp.

Automatic Servo Tuning

• Auto-tuning of servo gain and Acc/Dec time constant according to target work

piece

• Useful for machining optimization

Ethernet

Machine tools

Workpiece1

Collect Collect Restore

Workpiece 2

Workpiece 1 SERVO Tuning Data 1

SERVO Tuning Data 2

SERVO

Tuning Data 1

:Manage

Workpiece2

SERVO

Tuning Data 2

Workpiece1

SERVO

Tuning Data 1

Inertia Estimation, For higher acceleration to Reduce Cycle Time

• Can automatically estimates the inertia when Job changes.

• Can achieve optimum positioning time.

Adaptive Control for Optimizing Cycle Time

• Automatic Feed rate control according to spindle load and temperature.

• Controlling feed rate according to spindle load strikes a good balance between

shorted cycle time and longer life time of cutting tools.

Adaptive Control for Optimizing Cycle Time

Learning Control for Achieving high performance machining

Servo learning Control

• Suppress periodic machining disturbance.

Learning Control for Achieving high performance machining

Servo learning Oscillation

• Avoid chip Entanglement by oscillation cutting for chip shredding using servo

learning.

• Contribution to productivity improvement by continuous operation.

• Reduction of production costs by elimination of chip removal system.

Smart Program Analysis-Acc/Dec decided dynamically

• Artificial Intelligence Contour Control Function for reading small segments of program in

advance and will create smooth profile.

Prediction of Failures- AI Spindle Monitor

• Anomaly monitoring of spindle by machine learning.

• Can predict the spindle failure in advance.

Model creation at normal state

Calculation of

Anomaly score

Acquisition of

servo data

Data Analysis Using AI- Pattern Analysis/Waveform Analysis

• Monitor the servo and spindle loads and establish pattern(Signature) for the

component.

• Collect servo data with high speed

sampling (1ms) and to store with

file format

• Displays collected data for

analysis.

Collection of various sensors data and

servo data

• Collect data from various sensors

(temperature, shock etc.) via CNC

by using i/o units.

. . .

Database

Operation Management

software

Analog

interface

module

External sensor

Servo

data

VIEWER software

Servo data

Motor speed

Machine Acc.

MULTI SENSOR

I/O UNITTemperature sensorShock sensor

Sensor data

Applications

Minimizing Down time using AI

• Manages diagnosis information of Trouble Diagnosis and Machine Alarm

Diagnosis with final solutions when alarm occurs.

• When newly alarm occurs, indicate solution from similarly diagnosis information

Normal Trouble Diagnosis AI Trouble Diagnosis

• Operator implement diagnosis according to

CNC guidance/Manual.

• Operator needs to diagnose when multiple

estimation causes finally to be left

• AI indicate higher probability solution from past history data.

• Automatic judgment from countermeasure / treatment information in case of

multiple estimation causes remained.

Rapidly restoration at trouble

Collect

Alarm!

ActuallyMeasures/TreatmentsAdd

Indicate

Applying Machine Learning for Robotic Automation.

Faster Bin Picking application:

• Robots automatically learns the picking sequence of work piece.

• Drastically reduces the time for manual setting and tuning.

AI Bin

picking

Application

Applying Machine Learning for Robotic Automation.

Learning Vibration Control:

Learning robot realizes high speed smooth motion

with suppression of vibration

by LVC (Learning Vibration Control).

Learning robot merit

This function has

overcome vibration

issues of high speed

motion, which has not be

used before.

W/ LVCW/O LVC

Accelerometer

Cycle time can be reduced by high

speed motion.

(i.e. realization of higher performance

for each )

Vibration

Suppressed!!

Learning Control +

Sensor Technology

Prediction of Failures- Mechanical Failures

To eliminate unplanned downtime .

Maintenance health

Process Health

System Health

Grease replacement

Battery replacement

Greasing to the balancer bush

Vision detection result

Welding current monitor

Servo gun status monitor

Operational status

Memory usage

Alarm information

Increasingvibration of J2!

Reducer to be exchanged

next weekend.

proceed

production

proceed

production

proceed

production

proceed

production

Mechanical Health

Replace grease !

Alarms

Machine Learning on Standalone Vs network of Systems

Stand alone Machine with networking

Learning with experience is confined to

one machine.

Learning will be vast since all machines

will be sharing there data and solution

can be immediately found.

Server with ML

Machine learning With IOT

IoT- Connects Things – “Internet of Things”

IOT provides a platform on which number of devices are

connected and pushing down data in a centralized

system.

• IoT devices follow these five basic steps: measuring,

sending, storing, analyzing, acting.

• The collected datasets are fed into Machine learning

algorithms to take active decisions.

Cloud Computing

ON Premises

ON

CLOUD

2016 2017 2018

2016 2017 2018

• In IOT System, to save huge amount of data,known as Big Data, stack of storage devices arerequired.

• IOT data will be increasing exponentially &hence will require frequent hardware upgradation.

• To Run Machine learning/AI algorithms, highcomputation power processors are required andsingle processor is not sufficient.

Advantages of Cloud Computing

Flexibility

If your needs increase it’s

easy to scale up your cloud

capacity, drawing on the

service’s remote servers.

Disaster recovery

Businesses of all sizes

should be investing in

robust disaster recovery,.

Automatic software

updates

Suppliers take care of

servers for you and roll out

regular software updates.

Capital-expenditure Free

Cloud computing cuts out

the high cost of hardware.

Work from anywhere

With cloud computing, if you’ve got an

internet connection you can be at work.

FOG Computing

• FOG Computing is an intermediate layer between device and Cloud.

Cloud

FOG (T3 Time for processing)

IOT Devices(T1 time for data generation)

T2 S

ec

T4 Sec

On

-P

remise

s

No

n-C

riti

cal d

ata

sen

t d

irec

tly

Aft

er p

roce

ssin

g d

ata

is s

aved

in

clo

ud

Data Segment

Non- Critical Data

Critical Data

IOT/Cloud computing with ML

The only way to analyze the data generated by the IoT is with machine learning/AI.

FANUC Solutions for Machine learning & IOT.

AI and Cloud computing

Data collection and

Monitoring (IOT)

Visualization

MT-LINK iDiagnosis

Notification

Host system software

Communication interface

Data collection

Connecting

Collecting

Communicating

Smart/AI Features

FANUC Intelligent Edge Link & Drive system

FANUC MT-LINK i

AI Thermal displacement

compensation

AI Servo Tuning.

AI spindle monitoring.

AI contour control(AICC).

AI Bin Picking

Smart Adaptive Control

Smart Feed axis Acc/Dec

Servo learning Control.

Zero Down Time(ZDT)

Learning Vibration control

Thank You for your

Kind attention