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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
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