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IoT & Artificial Intelligence in Textile Industries

Dr. Asim TewariG.K. Devarajulu Chair ProfessorDepartment of Mechanical Engineering Indian Institute of Technology Bombay, Mumbai, India

Composites Technology Research at IITB

Δ𝜃Total = 2.76°

Permeability Model Resin Infusion Model Distortion Model

Composite Drilling

Product Design

Fatigue Model

Composite Manufacturing facility

VaRTM

Composite Machining

Composite 3D PrintingAutoclave

Composite Simulation and Modeling

Composite Post Processing facility

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A.ShrivastavaV. Kulkarni S. TripathiA. Tewari M. Kulkarni

Group Faculty

Yogesh Nakhate Aniket Adsule

M.Tech, D.D., B.Tech Students and researchers

Bhupendra Solanki

A. Guha

Mohanish Verma

Aadarsh Pratik Chandak Rajesh

Meghana Verma

S. Mishra

Amey Suryawanshi

Piyush Shukla Sourabh Wagale Kiran PatilChawda Darshan

Nikunj ShahSwapnil KumarFranklin VargheseSumit RuparelLov Kush

Vishali palav Rajkumar Prajapati

Nikhil Jose

Ankit Katariya Abhninav Jain Divya Pattisapu Shefali Gokarn

Yash Sanghvi

CYBER PHYSICAL SYSTEM & DATA ANALYTICS GROUP

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What is Artificial Intelligence ?

Machine learning is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

Artificial Intelligence : (Merriam-Webster ) The capability of a machine to imitate intelligent human behavior.

What is Machine Learning ?

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• Information-based Learning– Decision Trees

– Shannon’s Entropy

– Information Gain

• Similarity-based Learning– Feature Space

– Distance Metrics

• Probability-based Learning– Naïve Bayes Model

– Markovian model

• Error-based Learning– Multivariable Regression

– Linear discriminate analysis

– Multinomial Logistic Regression

– Support Vector Machines

• Expert-system based learning

Machine Learning Techniques in Data analytics

7Data Pe

rfo

rman

ce

Conventional ML

Deep Learning Convolutional neural network Recurrent neural network

• Artificial Intelligence : (Merriam-Webster ) The capability of a machine to imitate intelligent human behavior.

Artificial Intelligence

AI

Artificial Narrow

Intelligence

ANI

Artificial General

Intelligence

AGI

Artificial Super Intelligence

ASI

First Wave Second Wave Third Wave

Activity Approach Driver Capability and performance

AI Perform a task Rule based Definite cost function

Domain specific; lower that human performance

ANI Perform a task Self learned (ML) Non-explicitly (RL) Domain specific; surpasses human performanceAutomatic

AGI Overarching goal

Self learned (ML) Goal Universal domain; equivalent to humanperformance

Evolution of Artificial Intelligence

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Image of retina

Biological SexPredicted: Female

Actual: Female

SmokingPredicted: Non-smoking

Actual: Non-smoking

AgePredicted: 59.1 years

Actual: 57.6 years

BMIPredicted: 24.1 kg/m

Actual: 26.3 kg/m

Systolic blood PressurePredicted: 148.0 mmHg

Actual: 148.5 mmHg

A1CPredicted: Non-diabetic

Actual: Non-diabetic 9

Google DL Retinopathy

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Lip-reading AI

Google’s DeepMind• AI trained on 5000 hours of TV• 118,000 sentences

Other resources: LipNet AI, WAS

Deep image reconstruction: Reading the brain

Ref: Prof. Yukiyasu Kamitani, University of Kyoto Japan 10

Self-driving Cars

Waymo’s self-driving carshas driven 13 Million Km

Dominos self-driving pizza delivery vehicle

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DEEP LEARNING FOR SYMBOLIC MATHEMATICS

13Lample and Charton arXiv: 1912.01412

Examples of problems that DL model is able to solve, on which Mathematica and Matlabwere not able to find a solution. For each equation, DL model finds a valid solution with greedy decoding.

Comparison of our model with Mathematica, Maple and Matlabon a test set of 500 equations

Progress in AI

• Google AlphaGo beats Go World Champion

• Microsoft and Kyoto University developed a poet AI

• AI creates music, songs and painting

• Japanese AI Writes a Novel, which nearly Wins Literary Award

• Facebook’s AI is writing short stories

• Atari-playing AI wins by cheating

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Video Analytics Research at IITB

• Face detection, unique persons

• Classify based on gender, age, dress color, etc.

• Track a person across many cameras

• Worker ID

• Safe and hazardous situation assessment

• Work protocol conformity assessment

• Cycle-time and efficiency determination

• Loss time assessment

Shop-floor Video Analytics

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Real time Posture identification

Motion direction estimation

Video Analytics Research at IITB

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• Objective: Develop a high level Deep Learning tool for image segmentation.

• Motivation :Spatial data in the form of image is available in all walks of technology, segmentation based on Deep learning would be the first step in data comprehension.

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All our Code is available on github

Language: Python

Framework: Pytorch

Current implementations:

PSPNet, FCN, Segnet

Deep learning approach

SegnetTrue labelT1CE Image Predicted label

Trained using FCN on 4 GPUs

Image Analytics Research at IITB

Cyber Twin of

3-axis CMC

Physical Machine &

Virtual Model

Current, voltage and

Acceleration Sensor

A cyber twin is a virtual realization of a physical machine. These are the building blocks for industry 4.0 to create a seamlessly connected factory that interacts with the real world as an intelligent, self-contained, autonomous entity.

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Equipment condition

monitoring

Plant Operations Monitoring

INDUSTRY

4.0Tool wear

monitoring

Adaptive

control

Quality prediction/ Monitoring

Smart Factory:

Machine Monitoring and Analytics for

Total Productive Maintenance (TPM)

Smart machines facilitate productioncontrol on the shop-floor

Reactive control:Opportunistic maintenanceReactive schedulingReactive quality control plans

Smart Mach[i]nes

Informative control:Performance assessment

In-advance ControlApplied just before start

Predictive ControlApplied much in advance

Reactive ControlApplied while in progress

Informative ControlApplied after completion

Predictive control:Machine PrognosticsTool life prediction

In-advance control:Selective maintenanceProduction schedulingQuality control plans

Smart Mach[i]nesAssessment

Data Processing

Data Acquisition

IIoT Devices

ML based Process-

control advisements

Monitor equipment

performance metrics

Non-intrusive

sensorization

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STREAMING DATA

IIOT Devices

Data from Controllers

PERSISTENT DATA

System architecture

Ontology

IOT DATA SERVER

WEB SERVER

PHP, HTML, CSS,

Shiny- R

ANALYTICS WORKSTATION

Python, R, C++

SQL server

TCP/IP

UDP/IP

Event trigger m

od

ule

DOMAIN

CONTROLLER

System Admin

ANALYTICS EXPERT

Designer

Programmer

WEB BROWSER

JavaScript

Canvas

MACHINE FEEDBACK

Machine Controllers

Actuators

HUMAN INPUTS

NCAIRIoT

NCAIRIoT

NCAIRIoT

IoT ServerData analytics works stationWeb server

Shop Floor OperationsShop Floor

Plant Management

• Machine ranking

• Weekly/Monthly Statistics

Machine Efficiency

Monitoring system

• Machine Efficiency• Analysis on breakdown time, setting time and

other losses• Integration with ERP

• Breakdown status and analysis• Day/shift wise• Operator wise

• Automated email/SMS to call for service • Environment monitoring for temperature & humidity• Reports on ( efficiency wise, and breakdown wise)

ShiftQuality Problem (B)

1st

2nd

3rd

Setup change / Tool change / other

change ( A)

No

material

( K )

Management losses

Total

losses in

minutes

Losses details in Min

PM

/CLITA

( E )

Operation Losses

Tooling not

available ( F)

Gauges Not

vailable (G)No operator (H)

Power

Failure

( I)

No Plan (J)

M/c

Breakdown

(C)

Tooling

Failure

(D)

Grid view

Machine On and Cutting

Machine OFF

Plant Power Management System (PPMS)

• IIOT Based data analytics solution• Power consumption

• Load power factor

• Machine utilization percentage

• Power distribution

• RMS current value

• RMS voltage value

• Power anomaly count

• Voltage spike

• Current spikes

• Low voltage alarm

• Sinewave quality

• Power source frequency stability

• Power outage

• Machine vibrations

• Ambient temperature

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a-sysmo: Advanced system monitoring device

Staff productivity measurement in an IT office

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Selective Maintenance Decision Analytics tool

1. Peace time scenario 3. War scenario 1 (With adv notice)2. War Exercise 4. War scenario 2 (Immediately)Obj Function: Min cost Constraints: Mission Reliability > target reliability

Maintenance time < available time

US Army:

Operational

Readiness

(95%)=>Mission

Reliability (75%)

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Equipment reliability analyzer, INSMA, Indian Navy

1. Based on history of each-subsystem, what is the reliability of the complete platform

2. For a given mission profile, which platform has the highest reliability

Data analytics in tool condition monitoring• Objective: Develop an analytical solution to predict real time tool wear

• Motivation: By detecting the condition of tool at real time, machine downtime, product quality can be improved significantly resulting in improved efficiency

• Sensor Data:

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Good Tool One edge broken Tool

Vibration, Force, Acoustics, current

Breakout detection in continuous casting • Objective: Develop a tool that can detect change-point in time series data

• Motivation :By doing change point analysis we can find breakout detection in continuous casting process.

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The Change-Point Problem

Let X1, X2, ... , Xn be a sequence of independent random

vectors (variables) with probability distribution functions

P1,P2,P3…,Pn, respectively.

Then, in general, the change point problem is to test the

following hypothesis

• Null hypothesis:

Ho :P1=P2=…Pn

• Verses the alternative:

H1: P1 = ... = Pk1≠ Pk1+1 = ... = Pk2 ≠ Pk2+1 = ... Pkq ≠ Pkq

+l ... = Pn

where 1 < k1 < k2 < ... < kq < n, q is the unknown number of

change points and k1 k2, ... , kq are the respective unknown

positions that have to be estimated.

Schematic diagram of continuous casting process

Schematic diagram of sticking type breakout

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28• https://www.i-scoop.eu/internet-of-things-guide/industrial-internet-things-iiot-saving-costs-innovation/

• https://www.i-scoop.eu/internet-of-things-guide/industrial-internet-things-iiot-saving-costs-innovation/ 29

Value Drivers for IoT and AI in Textile Industry

Ref: McKinsey Digital Compass 30

Summary• AI can provide decisive business advantage

• IoT is needed to generates data to feed AI

• Domain knowledge is needed to monetize AI• Cheaper, better, faster

• MSME is best suited for IoT AI deployment• Local customized solution (supervised learning)

• Cost effective

• Technology agile

Prof. Asim Tewari, IIT Bombay