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The Internet of Manufacturing Things
Athulan Vijayaraghavan
CTO and Co-Founder, System Insights Berkeley, CA | Chennai, India
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The Internet of Things
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“interconnection of sensors, devices, and other things”
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The Hype Cycle
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Why Manufacturing?• Manufacturing is Big: $2
Trillion sector
• Discrete Manufacturing: Products for consumers and the supply chain
• High potential for productivity improvement
• Manufacturing generates a very large amount of data – most of it falls on the floor
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Manufacturing Today
• Global
• Fragmented
• Heterogeneous
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Improving the Transformation• Focus:
• Productivity? Profitability? Return on asset?
• Part quality? Employee safety?
• Sustainability? Energy usage?
• How about a more holistic view?
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transformation
raw material
stuff
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Track flow of resources and intelligence across manufacturing process
Process Improvement Design Integration Usage Analytics
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Grand Challenge: Process Traceability
time
Foundry Forging Roughing Finishing Sub-Assembly WarehouseFinal
Assembly Shippingunique
part
Design Impacts
Usage ImpactsManufacturing
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Enabling Technology
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Standards Sensors Software
Internet of Things
Internet of Manufacturing Things
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Standards
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Why no Standards?
• Manufacturing data highly complex
• High barriers to entry
• Specialized technical knowledge deterrent to innovation
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Why Standards!
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What do you prefer?
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MTConnect• Open royalty-free standard providing data from devices using a
common unambiguous vocabulary
• Uses XML and HTTP – Internet ready
• Simple, free, and extensible
• Design Goals
• Capture manufacturing domain model
• Read-only – inherently secure
• Borrow when you can
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Differentiation• How is MTConnect different than OPC, Ethernet/IP?
• OPC is a transport – it gets data from one location to another
• Same with the field-buses
• OPC and Ethernet/IP do not have a domain model – nothing specific for manufacturing
• Tags are different for every implementation
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Example: “Current Postion”• OPC
• Siemens Controller: /Channel/MachineAxis/actToolBasePos[u1,1] – returns a single value
• Bosch ControllerNC.Chan.AxisPosMcs,1 – returns a vector of values that needs to be decoded
• MTConnect
• Devices <Axes id=“a” name=“axes”> <Linear id="x1" name="X”> <DataItems> <DataItem category="SAMPLE" id="x2" name="Xact” subType="ACTUAL" type=“POSITION" units=“MILLIMETER"/> </DataItem></Linear></Axes>
• Stream of data<ComponentStream component="Linear"name="X" componentId="x1”><Samples>
<Position dataItemId=“x2” timestamp="2014-02-18T23:32:11.133634” name="Xact" sequence="2810505090” subType=“ACTUAL">2.0019900000</Position></Samples>
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IoMT Requirements• Security
• MTConnect is read-only and inherently secure
• Reliability
• MTConnect provides a reliable messaging platform
• Performance
• MTConnect can run on embedded platforms
• One agent can support multiple devices
• Sensor data
• The most complete standardized sensor vocabulary and extensible for new sensors
• Support for realtime time-series data
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IOT Standards• MQTT Protocol
• Lightweight IP-based M2M communication protocol
• Pub/sub model – highly scalable
• Large community, supported by Eclipse
• MQTT + MTConnect
• Take domain model of MTConnect and apply in MQTT
• MTConnect can sit below/above MQTT layer based on the application
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The Digital Thread
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Initial ModelIdea
Product
Design Refinement
Solid Model
Manufacturing Planning
Manufacturing Process
Metrology
Design
Manufacturing
Integrating manufacturing with the design phase
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Standards along the way
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Initial ModelIdea
Product
Design Refinement
Solid Model
Manufacturing Planning
Manufacturing Process
Metrology
Design
Manufacturing
STEP-NC MTConnect QIF/DMSC
STL, STEP, IGES
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But can we do better?
• We have standards for the boxes – what about the arrows?
• How do we standardize analysis across the digital thread?
• We need cross-standard interoperability
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Sensors
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Sensors• Enable decision-making and automation
• Sensors at every level:
• Manufacturing process —> Supply Chain
• What we need:
• Minimally Invasive
• Physics based
• Open question: Where do you put the intelligence?
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Sensor Intelligence• Traditional Approach: Self-contained local
command-and-control loops
• Is centralized intelligence possible?
• Challenges: data load, bandwidth, latency
• Solution:
• Distributed decentralized systems
• Split decisions between local and central controllers
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centralized controllers
balance?
device controllers
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Software
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Software• Data Management:
• High data volumes
• Structured and unstructured data
• Decision-making:
• Event-based decision making
• Multi-dimensional reasoning
• Multiple temporal scales
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Data Volumes
Small Shop: 2~10 TB/year Medium Shop: 5 ~ 25 TB/year Large Shop: 16 ~ 80 TB/year Enterprise: 80 ~ 5000 TB/year
US Machining Sector: 200 PB ~ 1XB/year
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Data TypesStructured Unstructured Tribal Knowledge
Sensor Machine Telemetrics
Alarms, Faults Quality Control
Performance + Test
Annotations Over-rides
Interruptions
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So what do we do with all of this data?
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Event ReasoningEvent Processing
fusion
filter
aggregate
identify relationships
The Manufacturing Event Cloud
spindle speed position
alarms
notification
static datafeedrate overrides
Event: Something that happened at a point in time
tribal knowledge
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Complex Event ProcessingTemporal
Overlap ContainsBefore/After
Spatial
Clustering TrendingShapes
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Temporal Decision ScalesTemporal scales can vary from µ-seconds to days
anytime:process management
neartime:process improvement
realtime:process control
m-Seconds Seconds Hours Days
Process Interface
Sub-Components
Manufacturing Equipment
Manufacturing Supply Chain
Manufacturing Enterprise
Temporal Decision Scale
Man
ufac
turin
g A
naly
sis
Sca
le
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Multidimensional Reasoning
:01 :02 :03 :04 :05 :06
V Mill 1
Lathe 1
Robot
CMM
Cell
Day
Line
Part 1Part 2
Part 6
......Batch
Power: 3562 WattsLoad[X]: 120%Alarm: ACTIVE
Time
Equi
pmen
t
Prod
uct
:01 :02 :03 :04 :05 :06
V Mill 1
Lathe 1
Robot
CMM
Cell
Day
Line
Part 1Part 2
Part 6
......Batch
Time
Equi
pmen
t
Prod
uct
Report: Total Energy UsagePeriod: DailyDevice: V-Mill 1
:01 :02 :03 :04 :05 :06
V Mill 1
Lathe 1
Robot
CMM
Cell
Day
Line
Part 1Part 2
Part 6
......Batch
Time
Equi
pmen
t
Prod
uct
Analysis: Power ConsumptionDevice: Cell 1Time: NowMulti-dimensional
reasoning allows us to slice data across any plane,
including: time, machine organization, parts
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IoMT: Enabling Technology
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High speed data from heterogeneous
sources
Integration across software and
hardware platforms
Decision-making across spatial and
temporal resolutions
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IoMT vs. IoT
• Enterprise focused vs. Consumer Focused
• Islands of Excellence vs. New Ecosystems
• Mature markets vs. New Markets
• “the internet of what?” vs. “take my money!”
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Closing Thoughts
• Terrific potential
• Being domain specific helps – a lot
• Don’t reinvent the wheel
• Boxes ok, Arrows need work
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