Learning in a Digital Factory Dr' Can John Saygin Associate Professor of Mechanical Engineering Rese

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Learning in a Digital FactoryDr. Can (John)
SayginAssociate Professor of Mechanical
EngineeringResearch Investigator, CAMLS Director
(x4) MSA CoE Machine Shop iTEC ME Graduate
ProgramsThe University of Texas at San
Antoniocan.saygin_at_utsa.edu 210-458-7614
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The Pipeline Issue

• K-12 Education page 15
• Fewer than 1/3 of US 8th-grade students performed
  at or above a level called "proficient" in
  mathematics
• About 1/5 of the 4th graders and 1/3 of the 8th
  graders lacked the competence to perform even
  basic mathematical computations
• In 1999, 69 of US 5-8th-grade students received
  instruction from a mathematics teacher who did
  not hold a degree or certification in
  mathematics.
• In 2000, 93 of students in grades 5-8 were
  taught physical science by a teacher lacking a
  major or certification in the physical sciences
• In 1995, US 12th graders performed below the
  international average for 21 countries on a test
  of general knowledge in mathematics and science
• Because the United States does not have a set of
  national curricula, changing K-12 education is
  challenging, given that there are almost 15,000
  school systems in the United States and the
  average district has only about six schools

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OUTLINE

• Introduce Manufacturing _at_ UTSA
• Growth of the Manufacturing Effort
• Digital Factory within 4-walls
• Digital Factory over the Internet
• iTEC

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Ingredients
STUDENTS
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Manufacturing _at_ San AntonioThe Big Picture
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Center for Advanced Manufacturing and Lean Systems
TECHNOLOGICALADVANCEMENTS

• A one-stop, unique source of expertise in
  flexible and lean technologies and systems,
  state-of-the-art technology applications in
  manufacturing and service industries.

PRODUCTS
PROCESSES
SYSTEMS
LEAN THINKING

• Advanced Manufacturing Systems effective and
  efficient integration and synthesis of automation
  technologies, human resources, and
  decision-making models for design, planning,
  scheduling, and control of production of goods
  and delivery of services.
• Lean Systems systematic elimination of waste
  (anything that does not add value) by using
  various lean and six-sigma tools and
  methodologies to continuously improve value
  creating processes in manufacturing and
  non-manufacturing sectors.

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Manufacturing _at_ UTSA(Aug 2006-Present)
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COE Machine Shop
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Flexible Manufacturing and Lean Systems
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Flexible Manufacturing and Lean Systems
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Flexible Manufacturing and Lean Systems
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Robotics and Intelligent Machines
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Robotics and Intelligent Machines
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Manufacturing Systems and Automation (Aug 2006)

• GOAL Build a laboratory environment that
• acts as a reconfigurable test-bed for research
  and development in the area of Digital
  Manufacturing and
• 2) facilitates multi-disciplinary, team-based
  learning

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VISION Digital Factory
Shrink the world to a manageable size via
operational visibility provided by effective
and efficient transformation from data to
information to knowledge
IT
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Manufacturing Systems and Automation (MSA) Lab
Robotic Assembly
Metrology
Automatic Control
RFID Antennas for Tracking
RFID-equipped Warehouse
Enterprise Resource Planning
Automated Manufacturing Cell
Flexible Assembly System
MSA consists of functional areas that a typical
manufacturing enterprise would have
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MSA Lab DIGITAL FACTORY
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Manufacturing Systems and Automation (MSA) Lab
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Manufacturing Systems and Automation (MSA) Lab
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Manufacturing Systems and Automation (MSA) Lab
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Manufacturing Systems and Automation (MSA) Lab
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Manufacturing Systems and Automation (MSA) Lab
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Manufacturing Systems and Automation (MSA) Lab
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Manufacturing Systems and Automation (MSA) Lab
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Manufacturing Systems and Automation (MSA) Lab
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Manufacturing Systems and Automation (MSA) Lab
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Manufacturing Systems and Automation (MSA) Lab
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Manufacturing Systems and Automation (MSA) Lab
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Web-Based Manufacturing
Web-Based Curriculum
Process Simulation
Live via Web-Cameras
REMOTE USER
Web-Based Control of Actual System
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Web-Based Manufacturing
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Web-Based Manufacturing
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Web-Based Manufacturing
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Web-Based Manufacturing
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Web-Based Manufacturing
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Web-Based Manufacturing
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Web-Based Manufacturing
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Web-Based Manufacturing
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Effective Learning

• Life-long learning
• Learning to learn

Learning is an interactive processPractice-Feedb
ackRight Balance Teaching/Learning/Assessment
Styles
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Levels of Interactionwith Knowledge Domain
Design/develop/operate manufacturing systems
using different brand name hardware and software,
which come from different vendors.
Integrate
Plug-and-Pray
LEVEL 3
INCREASING INTEGRATION Increasing complexity
Increasing risk/reward Longer to-do list for
instructor
Design/develop/operate mfg systems using modular
hw/sw, which usually come from a single vendor.
InterfacePlug-and-Play
LEVEL 2
Operate existing mfg equipment/systems
Follow the master

• LEVEL 1

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T-NESCOTestbed for Network-Enabled Supply Chain
Operations
PROJECT INVESTIGATORS
Dr. Jag Sarangapani Electrical and Computer Eng
Dept, MST Embedded Systems and Networking
Lab sarangap_at_mst.edu 573-341-6775
Dr. Can (John) Saygin Mechanical Engineering
Department, UTSA Manufacturing Systems and
Automation Lab can.saygin_at_utsa.edu 210-458-7614

• Objective
• Set up a Web-enabled testbed, which consists of
  two laboratories at UTSA and MST
• Demonstrate how Auto-ID technologies can be used
  for real-time monitoring of manufacturing
  operations and for decision-making across the
  supply chain.

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Laboratories
Manufacturing Systems Automation (MSA) Lab at
UTSA Embedded Systems Networking (ESNL) Lab at
MST
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3-COMPANY SUPPLY CHAIN
Internet
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ServersMySQLEPCIS
ServersMySQLEPCIS
Quasi - Simulated "Virtual" Enterprise
Smart Shelf
Dock-Door
Dock-Door
Smart Shelf
ESNL
MSA
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___________
T-NESCO Architecture
Real-Time Performance Monitoring
Asset Tracking
WIP Tracking
EPCIS
Operational Visibility on Demandat Supply Chain
Level
Web based Management
OMNITROL1
OMNITROL3
OMNITROL2

• RFID-integrated Dock Door
• Smart Shelf
• CNC Machining Automatic Inspection Robotic
  Assembly
• Barcode readers
• PLC PC-based Controllers

• RFID-integrated Dock Door
• Smart Shelf
• Mobile Handheld Devices
• Wireless Motes
• Mobile Robots

• Virtual Company
• Simulated Operations

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SCENARIOS
CASE 1 TYPICAL SUPPLY CHAIN Limited (or no)
Visibility
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Bill-of-Materials parts with RFID Tags
Products (P1, P2, P3)
P2
P3
P1
Missouri UTSA Virtual
Sub-Assemblies (SA1, SA2, SA3)
SA1 SA2 SA1
SA3 SA2 SA3
Raw Materials (A, B, C)
A B C A B
A C C A
C
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SMART SHELF
Sub-Assemblies (SA1/2/3)

• ROLE PLAYING
• PROCESS DESIGN
• TECHNOLOGY DEPLOYMENT

As
Bs
Cs
DOCK DOOR
PRODUCTION(Hand-held reader will be used in this
area)
out
in
Q
P
I
Authorized Personnel Production Supervisor
(P), Warehouse Manager (I), Quasi Delivery Person
(Q)
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• Smart Shelf (4 shelves, 1 motion sensor, 3
  lights)
• Dock Door (2 pairs of optical sensors, 4 lights)

• CAPABILITY What is on each shelf Who is there.
• Lights ? off
• Walk to the shelf (somebody w/ an RFID badge)
• Motion sensor ? on
• Light ? Yellow (a few seconds)
• Walk away from the shelf
• Light ? Green (a few seconds)
• Unauthorized Events
• If two people (w/ RFID badges) walk to the
  shelf
• Light ? Red

Sub-Assemblies (SA1/2/3)
As
Bs
Cs

• CAPABILITY Walking in, walking out, detect
  entities w/ tags
• Prod walks in with manufactured Sub-Assemblies
• Quasi walks in with A,B,C
• Inv walks in/out with A,B,C, Sub-Assemblies.
• Unauthorized Events
• Prod is not allowed to walk out with A,B,C
• Quasi can only walk in with A,B,C
• Sensor triggered, no personnel RFID tag is read

Sensor-out
Sensor-in
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Virtual Company
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Scenarios ? Events ? Building Blocks
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Scenarios ? Events ? Building Blocks

• Notifications (non-tag read events), such as PO,
  ASN, etc.
• Tag read events

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Technical Challenges Investigated

• Architectural Issues How to collect, organize,
  and share fine-grained and real-time RFID data
  coming across (shop floor to top floor) the
  supply chain in order to improve business
  processes without the hassle of facilitating
  connectivity Omnitrol(See http//www.omnitrol.c
  om/ for more info)
• Benchmarking of alternative solutions/technologies
  1) communications 2) manufacturing
• Experimenting with RFID-unfriendly
  environments/products Research
• Level of tagging (i.e., nested tags) what works
  what does not
• Levels of visibility How does it affect business
  events/scenarios?

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Learning Experience

• Establishing such an infrastructure requires
  detailed initial planning with institutional IT
  support
• Maintaining such an infrastructure requires
  high-caliber students with computer skills in my
  case, I had 1 electrical engineering and 2
  computer science undergraduate level lab
  assistants a multi-disciplinary team
• Teaching Defining roles (who/what) is very
  time-consuming Synchronizing all the events is
  challenging technology curriculum components
• Learning First intimidating for students then
  fun overall very effective Learning by
  doingLearnMate a web-based, self-paced,
  asynchronous learning platform

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Q
A

Dr. Can (John) Saygincan.saygin_at_utsa.edu
210-458-7614